modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-06 00:36:47
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 540
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-06 00:36:27
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
bizarre123/standardized-app-v2
|
bizarre123
| 2024-01-13T03:33:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-01-13T03:29:46Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
yangzhou301/rl_course_vizdoom_health_gathering_supreme
|
yangzhou301
| 2024-01-13T03:32:38Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-13T03:08:53Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.90 +/- 3.87
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r yangzhou301/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
yangzhou301/ppo-LunarLander-v2-unit8
|
yangzhou301
| 2024-01-13T03:14:09Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-13T02:24:15Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -46.19 +/- 61.46
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 500000
'learning_rate': 0.0001
'num_envs': 4
'num_steps': 256
'anneal_lr': True
'gae': True
'gamma': 0.997
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'yangzhou301/ppo-LunarLander-v2-unit8'
'batch_size': 1024
'minibatch_size': 256}
```
|
Kquant03/Phalanx-512x460M-MoE
|
Kquant03
| 2024-01-13T02:59:53Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"merge",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T15:59:26Z |
---
license: apache-2.0
tags:
- merge
language:
- en
---
# It will run...but on what hardware?
## 512 experts in one MoE
[A giant merge of all the same model](https://huggingface.co/ahxt/LiteLlama-460M-1T)
|
fufufukakaka/pokemon_team_BERT
|
fufufukakaka
| 2024-01-13T02:45:18Z | 104 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"dataset:fufufukakaka/pokemon_party_dataset",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-01-13T02:13:18Z |
---
tags:
- generated_from_trainer
model-index:
- name: pokemon_team_BERT
results: []
widget:
- text: グライオン ハピナス クレセリア クレベース モロバレル [MASK]
example_title: example1
datasets:
- fufufukakaka/pokemon_party_dataset
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pokemon_team_BERT
ポケモンのパーティの並びで学習した BERT(Masked Language Model) です。
学習に使ったデータは、自分自身で収集したパーティの並びをシャッフルなどして増やしたものを使用しています。
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4446
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 3.786 | 1.0 | 1995 | 3.7066 |
| 3.5106 | 2.0 | 3990 | 3.5062 |
| 3.3389 | 3.0 | 5985 | 3.2973 |
| 3.2422 | 4.0 | 7980 | 3.3000 |
| 3.173 | 5.0 | 9975 | nan |
| 3.0516 | 6.0 | 11970 | 3.0902 |
| 2.9928 | 7.0 | 13965 | 3.1138 |
| 2.9509 | 8.0 | 15960 | 3.0007 |
| 2.8988 | 9.0 | 17955 | 3.0047 |
| 2.8105 | 10.0 | 19950 | 2.9341 |
| 2.8212 | 11.0 | 21945 | 2.8955 |
| 2.6472 | 12.0 | 23940 | 2.7615 |
| 2.6196 | 13.0 | 25935 | 2.7013 |
| 2.6267 | 14.0 | 27930 | 2.7081 |
| 2.5083 | 15.0 | 29925 | 2.4976 |
| 2.447 | 16.0 | 31920 | 2.5197 |
| 2.3858 | 17.0 | 33915 | 2.4245 |
| 2.3841 | 18.0 | 35910 | 2.3988 |
| 2.3517 | 19.0 | 37905 | 2.3718 |
| 2.2163 | 20.0 | 39900 | 2.3823 |
| 2.1698 | 21.0 | 41895 | 2.2830 |
| 2.1829 | 22.0 | 43890 | 2.1554 |
| 2.0978 | 23.0 | 45885 | 2.2174 |
| 2.0231 | 24.0 | 47880 | 2.2168 |
| 1.9973 | 25.0 | 49875 | 2.2039 |
| 1.9273 | 26.0 | 51870 | 2.1422 |
| 1.9079 | 27.0 | 53865 | 2.0993 |
| 1.8985 | 28.0 | 55860 | 1.9250 |
| 1.8285 | 29.0 | 57855 | 1.9467 |
| 1.8864 | 30.0 | 59850 | nan |
| 1.7704 | 31.0 | 61845 | 1.8719 |
| 1.7629 | 32.0 | 63840 | 1.8193 |
| 1.6854 | 33.0 | 65835 | 1.8562 |
| 1.6579 | 34.0 | 67830 | 1.7616 |
| 1.5921 | 35.0 | 69825 | 1.7197 |
| 1.637 | 36.0 | 71820 | nan |
| 1.6207 | 37.0 | 73815 | nan |
| 1.6019 | 38.0 | 75810 | 1.6114 |
| 1.5648 | 39.0 | 77805 | nan |
| 1.5385 | 40.0 | 79800 | nan |
| 1.5333 | 41.0 | 81795 | nan |
| 1.5206 | 42.0 | 83790 | 1.6189 |
| 1.4768 | 43.0 | 85785 | 1.5352 |
| 1.4768 | 44.0 | 87780 | 1.5310 |
| 1.5099 | 45.0 | 89775 | 1.5464 |
| 1.455 | 46.0 | 91770 | 1.5714 |
| 1.4361 | 47.0 | 93765 | nan |
| 1.4291 | 48.0 | 95760 | 1.5281 |
| 1.443 | 49.0 | 97755 | 1.5089 |
| 1.4544 | 50.0 | 99750 | nan |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
hndc/distilbert-base-uncased-finetuned-emotion
|
hndc
| 2024-01-13T02:42:39Z | 91 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-12T23:54:48Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9235
- name: F1
type: f1
value: 0.92365716261768
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2138
- Accuracy: 0.9235
- F1: 0.9237
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8035 | 1.0 | 250 | 0.2991 | 0.9145 | 0.9143 |
| 0.2413 | 2.0 | 500 | 0.2138 | 0.9235 | 0.9237 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
yangzhou301/poca-SoccerTwos
|
yangzhou301
| 2024-01-13T02:12:39Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] |
reinforcement-learning
| 2024-01-13T02:12:28Z |
---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: yangzhou301/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
lxuechen/phi-2-tool-use
|
lxuechen
| 2024-01-13T01:56:39Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi-msft",
"text-generation",
"custom_code",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-11T23:01:20Z |
---
license: mit
---
## Model Summary
`phi-2-tool-use` is fine-tuned version of Phi-2 for function calling purposes. The model was fine-tuned on the public function call dataset [`glaiveai/glaive-function-calling-v2`](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2).
The purpose of the experiment is to understand the quality of the pre-trained Phi-2 model. `phi-2-tool-use` can generalize to call simple tools/functions not seen during fine-tuning.
## Decoding
Format your prompt as
```
"""SYSTEM: {system_content}\n\nUSER: {user_content} {eos_token} ASSISTANT:"""
```
where `system_content` is the system message containing a description of the tool/function as a json schema, `user_content` is the user message, and `eos_token` is the EOS token.
The model can handle multi-turn dialogue as it was trained on such data.
Here's a full-fledged example:
```
import torch
import transformers
model_name_or_path = "lxuechen/phi-2-tool-use"
model: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.float16
)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name_or_path)
input_text = """SYSTEM: You are a helpful assistant with access to the following functions. Use them if required - { "name": "get_exchange_rate", "description": "Get the exchange rate between two currencies", "parameters": { "type": "object", "properties": { "base_currency": { "type": "string", "description": "The currency to convert from" }, "target_currency": { "type": "string", "description": "The currency to convert to" } }, "required": [ "base_currency", "target_currency" ] } }\n\nUSER: Convert 100 USD to CAD <|endoftext|> ASSISTANT:"""
outputs = model.generate(
tokenizer(input_text, return_tensors="pt").to(model.device)['input_ids'],
max_length=1024,
temperature=0.7,
top_p=0.9,
do_sample=True,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training
The model was fine-tuned with SFT on [`glaiveai/glaive-function-calling-v2`](https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2).
Hyperparameters:
- learning rate: 3% linear warmup, with a peak of 2e-5 and cosine decay
- epochs: 2
- batch size: 64
- context length: 2048
|
Luckythespacecat/Somethin
|
Luckythespacecat
| 2024-01-13T01:38:16Z | 0 | 0 | null |
[
"text-to-image",
"region:us"
] |
text-to-image
| 2024-01-13T01:36:41Z |
---
pipeline_tag: text-to-image
---
|
littlepanic1234/tmp_trainer
|
littlepanic1234
| 2024-01-13T01:35:10Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-01-13T01:33:33Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: tmp_trainer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tmp_trainer
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.1
- Tokenizers 0.15.0
|
prashrex/fintuned_model
|
prashrex
| 2024-01-13T01:33:26Z | 4 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-01-12T17:39:16Z |
---
library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
cnatale/mistral_7b_instruct_v0_1_txt_2_sql
|
cnatale
| 2024-01-13T01:32:30Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-01-13T00:49:27Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: mistral_7b_instruct_v0_1_txt_2_sql
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral_7b_instruct_v0_1_txt_2_sql
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6482
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 80
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3599 | 0.71 | 10 | 1.0764 |
| 1.0005 | 1.43 | 20 | 0.8726 |
| 0.8508 | 2.14 | 30 | 0.7721 |
| 0.7585 | 2.86 | 40 | 0.7218 |
| 0.7078 | 3.57 | 50 | 0.6825 |
| 0.6413 | 4.29 | 60 | 0.6594 |
| 0.6128 | 5.0 | 70 | 0.6508 |
| 0.5745 | 5.71 | 80 | 0.6482 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
mihael974/speecht5_finetuned_voxpopuli_hr_v2
|
mihael974
| 2024-01-13T01:32:04Z | 60 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2024-01-12T23:10:23Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_hr_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voxpopuli_hr_v2
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4538
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4819 | 32.26 | 1000 | 0.4499 |
| 0.4538 | 64.52 | 2000 | 0.4465 |
| 0.4495 | 96.77 | 3000 | 0.4497 |
| 0.4464 | 129.03 | 4000 | 0.4538 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
TheBloke/deepmoney-34b-200k-base-GGUF
|
TheBloke
| 2024-01-13T00:56:04Z | 571 | 15 |
transformers
|
[
"transformers",
"gguf",
"yi",
"finance",
"invest",
"en",
"zh",
"base_model:TriadParty/deepmoney-34b-200k-base",
"base_model:quantized:TriadParty/deepmoney-34b-200k-base",
"license:apache-2.0",
"region:us"
] | null | 2024-01-13T00:36:52Z |
---
base_model: TriadParty/deepmoney-34b-200k-base
inference: false
language:
- en
- zh
license: apache-2.0
model_creator: triad party
model_name: Deepmoney 34B 200K Base
model_type: yi
prompt_template: '{prompt}
'
quantized_by: TheBloke
tags:
- finance
- invest
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Deepmoney 34B 200K Base - GGUF
- Model creator: [triad party](https://huggingface.co/TriadParty)
- Original model: [Deepmoney 34B 200K Base](https://huggingface.co/TriadParty/deepmoney-34b-200k-base)
<!-- description start -->
## Description
This repo contains GGUF format model files for [triad party's Deepmoney 34B 200K Base](https://huggingface.co/TriadParty/deepmoney-34b-200k-base).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF)
* [triad party's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TriadParty/deepmoney-34b-200k-base)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [deepmoney-34b-200k-base.Q2_K.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q2_K.gguf) | Q2_K | 2 | 12.77 GB| 15.27 GB | smallest, significant quality loss - not recommended for most purposes |
| [deepmoney-34b-200k-base.Q3_K_S.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q3_K_S.gguf) | Q3_K_S | 3 | 14.96 GB| 17.46 GB | very small, high quality loss |
| [deepmoney-34b-200k-base.Q3_K_M.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q3_K_M.gguf) | Q3_K_M | 3 | 16.65 GB| 19.15 GB | very small, high quality loss |
| [deepmoney-34b-200k-base.Q3_K_L.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q3_K_L.gguf) | Q3_K_L | 3 | 18.14 GB| 20.64 GB | small, substantial quality loss |
| [deepmoney-34b-200k-base.Q4_0.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q4_0.gguf) | Q4_0 | 4 | 19.47 GB| 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [deepmoney-34b-200k-base.Q4_K_S.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q4_K_S.gguf) | Q4_K_S | 4 | 19.60 GB| 22.10 GB | small, greater quality loss |
| [deepmoney-34b-200k-base.Q4_K_M.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q4_K_M.gguf) | Q4_K_M | 4 | 20.66 GB| 23.16 GB | medium, balanced quality - recommended |
| [deepmoney-34b-200k-base.Q5_0.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q5_0.gguf) | Q5_0 | 5 | 23.71 GB| 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [deepmoney-34b-200k-base.Q5_K_S.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q5_K_S.gguf) | Q5_K_S | 5 | 23.71 GB| 26.21 GB | large, low quality loss - recommended |
| [deepmoney-34b-200k-base.Q5_K_M.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q5_K_M.gguf) | Q5_K_M | 5 | 24.32 GB| 26.82 GB | large, very low quality loss - recommended |
| [deepmoney-34b-200k-base.Q6_K.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q6_K.gguf) | Q6_K | 6 | 28.21 GB| 30.71 GB | very large, extremely low quality loss |
| [deepmoney-34b-200k-base.Q8_0.gguf](https://huggingface.co/TheBloke/deepmoney-34b-200k-base-GGUF/blob/main/deepmoney-34b-200k-base.Q8_0.gguf) | Q8_0 | 8 | 36.54 GB| 39.04 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/deepmoney-34b-200k-base-GGUF and below it, a specific filename to download, such as: deepmoney-34b-200k-base.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF deepmoney-34b-200k-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepmoney-34b-200k-base-GGUF deepmoney-34b-200k-base.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m deepmoney-34b-200k-base.Q4_K_M.gguf --color -c 200000 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 200000` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./deepmoney-34b-200k-base.Q4_K_M.gguf", # Download the model file first
n_ctx=200000, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"{prompt}", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./deepmoney-34b-200k-base.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: triad party's Deepmoney 34B 200K Base
# **Deepmoney**
Introducing **Greed** in the Seven Deadly Sins series of models.
- Full-para pre-training on Yi-34b
- High-quality research reports
- High-end cleaning process
### 1. What do I want to do?
Most of the current so-called financial models are mostly trained on public knowledge, but in the actual financial field, these public knowledge are often seriously insufficient for the current market interpretability. If you are interested, you can learn about the various propositions of Keynes, Friedman and even current behavioral finance. According to my observation, most financial models cannot make investment judgments. Because they are trained on ordinary textbooks, entry-level analyst exams, and even company public reports. I think this is of very little value for the investment.
You can think I'm joking, but the fact is that the logic of many subjective analysts may not be as rigorous as that of large models of 34b and above (excluding those excellent ones, of course). The market is changing every moment, with a lot of news and massive data in real time. For most retail investors, instead of waiting for a crappy analyst to write a report, why not use a large model to make a pipeline?
In my plan, this model is the base model of this process. In my plan, models such as information collector, target judge, qualitative analyst, quantitative analyst, and data extractor are all part of this process. . But the model itself is undoubtedly important to master a large number of qualitative and quantitative methods. That's why this model was born.
### 2. About the data
As I just said, a lot of public knowledge has some questionable validity - but that doesn't mean it's wrong. The theoretical support behind many research methods in research reports also relies on this knowledge. So in my training, I picked up some college textbooks and some professional books. Not a lot of quantity but good quality. In addition, I selected a large number of research report data from 2019 to December 2023 - these reports are issued by a variety of publishers, including traditional brokers and professional research institutions. Most of them are paid and only available to institutions. But I got them anyway through various means.
If you have read research reports, especially high-quality ones, you will find that research reports are all subjective judgment + quantitative analysis, and data support in quantitative analysis is crucial to the entire logical chain. In order to extract this data (most of them are in the form of graphs or tables), I tried a lot of multi-modal models, and the process was very painful. The conclusion is that cog-agent and emu2 are very effective for this kind of tasks. In order to better extract information, I created a process that summarizes the context of research reports as part of the prompt.
Finally, I made a blend of the data. General data is not included because it is just for greed. Moreover, the knowledge in industry research reports is comprehensive enough.
### 3. About training
Raw text, full parameter training. The base uses long context yi-34b-200k. This is necessary to complete and understand an in-depth report.
Of course, I also did a sft. [This](https://huggingface.co/TriadParty/deepmoney-34b-200k-chat-evaluator) is the analyzer in my process – I haven’t broken down the qualitative and quantitative analysis yet, but I’m already blown away by how well it works.
### More:
More technical details and evals coming soon……
### 1. 我想干什么？
当下大多数所谓的金融模型大多在公开知识上进行训练，但在实际的金融领域，这些公开知识对当前的市场可解释性往往严重不足。如果您感兴趣，可以了解一下凯恩斯、弗里德曼乃至当下行为金融学的各类主张。而据我观察，大多数金融模型无法对投资进行判断。因为它们都是在普通的教材、入门的分析师考试，乃至公司的公开报告上训练。我认为这对于投资的价值非常小。
你可以当我开玩笑，但事实是很多主观分析师的逻辑性可能还不如34b及以上的大模型来的严谨（当然不包括那些优秀的）。而每时每刻，市场都在变化，大量的新闻，海量的数据都是实时的，对于大多数散户们，与其等待蹩脚的分析师写出报告，为什么不用大模型制作一套pipeline呢？
在我的计划中，该模型是这套流程的基座模型，在我的计划中，信息搜集者、标的判断者、定性分析者定性分析者、定量分析者、数据提取者等模型都是该流程的一部分。但模型本身掌握大量的定性和定量方法毫无疑问是重要的。这就是这个模型诞生的理由。
### 2. 关于数据：
正如我刚才所说，很多公开知识的有效性都有些问题——但这并不意味着它们是错误的。在研报中很多研究方法背后的理论支撑也依赖这些知识。所以在我的训练中，我挑选了一些大学教材和一些专业书籍。数量不是很多但质量还不错。另外，我挑选了在2019-2023年12月的大量研究报告数据——这些报告的发布者多种多样，有传统的broke，也有专业研究机构。他们中的大多数是付费的，而且只对机构提供。但无论如何我通过各种各样的手段获取了它们。
如果你看过研报，尤其是高质量的那些，你会发现研报都是主观判断+定量分析，而定量分析中的数据支撑对于整个逻辑链条至关重要。为了提取这些数据（他们中的大多数以图形或者表格的形式出现），我尝试了很多多模态模型，过程非常痛苦，结论是cog-agent和emu2对于这类任务很有效。为了更好的提取信息，我制作了一套从研报上下文总结作为prompt一部分的流程。
最后，我把这些数据做了一个混合。并没有放入通识数据, 因为它就是为了greed而生的。而且行业研报中的知识足够全。
### 3：关于训练：
raw text，全参数训练。基座采用了长上下文的yi-34b-200k。这对于完成理解一篇深度报告是必须的。
当然，我也做了一次sft。这是我的流程中的分析者——目前还没有细分定性和定量分析，但[它的效果](https://huggingface.co/TriadParty/deepmoney-34b-200k-chat-evaluator)已经让我大吃一惊了。
<!-- original-model-card end -->
|
Jongdae/koalpaca-polyglot-12.8b-bill
|
Jongdae
| 2024-01-13T00:41:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"base_model:adapter:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | null | 2024-01-13T00:41:02Z |
---
library_name: peft
base_model: beomi/polyglot-ko-12.8b-safetensors
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
veronica-girolimetti/t5-summarization-zero-shot-headers-and-better-prompt
|
veronica-girolimetti
| 2024-01-13T00:35:45Z | 89 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-13T00:03:43Z |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-summarization-zero-shot-headers-and-better-prompt
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-summarization-zero-shot-headers-and-better-prompt
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2226
- Rouge: {'rouge1': 0.4351, 'rouge2': 0.2124, 'rougeL': 0.215, 'rougeLsum': 0.215}
- Bert Score: 0.8806
- Bleurt 20: -0.7502
- Gen Len: 14.645
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 7
- eval_batch_size: 7
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge | Bert Score | Bleurt 20 | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:---------------------------------------------------------------------------:|:----------:|:---------:|:-------:|
| 3.0683 | 1.0 | 186 | 2.5857 | {'rouge1': 0.4573, 'rouge2': 0.1803, 'rougeL': 0.1858, 'rougeLsum': 0.1858} | 0.8683 | -0.8521 | 15.445 |
| 2.7283 | 2.0 | 372 | 2.4092 | {'rouge1': 0.446, 'rouge2': 0.1853, 'rougeL': 0.1969, 'rougeLsum': 0.1969} | 0.8709 | -0.828 | 15.115 |
| 2.4766 | 3.0 | 558 | 2.3190 | {'rouge1': 0.4183, 'rouge2': 0.1834, 'rougeL': 0.1947, 'rougeLsum': 0.1947} | 0.869 | -0.8673 | 14.425 |
| 2.351 | 4.0 | 744 | 2.2736 | {'rouge1': 0.4264, 'rouge2': 0.1843, 'rougeL': 0.1919, 'rougeLsum': 0.1919} | 0.8693 | -0.8411 | 15.205 |
| 2.287 | 5.0 | 930 | 2.2440 | {'rouge1': 0.42, 'rouge2': 0.1924, 'rougeL': 0.1991, 'rougeLsum': 0.1991} | 0.875 | -0.8358 | 14.305 |
| 2.1426 | 6.0 | 1116 | 2.2100 | {'rouge1': 0.4196, 'rouge2': 0.1903, 'rougeL': 0.2027, 'rougeLsum': 0.2027} | 0.8779 | -0.8189 | 14.38 |
| 2.0381 | 7.0 | 1302 | 2.2171 | {'rouge1': 0.459, 'rouge2': 0.2143, 'rougeL': 0.2142, 'rougeLsum': 0.2142} | 0.8772 | -0.7757 | 14.825 |
| 1.9927 | 8.0 | 1488 | 2.2106 | {'rouge1': 0.44, 'rouge2': 0.2073, 'rougeL': 0.2132, 'rougeLsum': 0.2132} | 0.8795 | -0.7798 | 14.53 |
| 1.9347 | 9.0 | 1674 | 2.1976 | {'rouge1': 0.4289, 'rouge2': 0.2062, 'rougeL': 0.2122, 'rougeLsum': 0.2122} | 0.88 | -0.7774 | 14.14 |
| 1.8733 | 10.0 | 1860 | 2.1987 | {'rouge1': 0.4472, 'rouge2': 0.215, 'rougeL': 0.2124, 'rougeLsum': 0.2124} | 0.8791 | -0.7688 | 14.49 |
| 1.7883 | 11.0 | 2046 | 2.1963 | {'rouge1': 0.4375, 'rouge2': 0.2114, 'rougeL': 0.2064, 'rougeLsum': 0.2064} | 0.8786 | -0.785 | 14.66 |
| 1.8253 | 12.0 | 2232 | 2.2055 | {'rouge1': 0.4351, 'rouge2': 0.2073, 'rougeL': 0.2106, 'rougeLsum': 0.2106} | 0.8803 | -0.7759 | 14.59 |
| 1.7751 | 13.0 | 2418 | 2.2029 | {'rouge1': 0.4371, 'rouge2': 0.2125, 'rougeL': 0.2119, 'rougeLsum': 0.2119} | 0.8796 | -0.7711 | 14.7 |
| 1.7087 | 14.0 | 2604 | 2.2073 | {'rouge1': 0.448, 'rouge2': 0.2211, 'rougeL': 0.2176, 'rougeLsum': 0.2176} | 0.8806 | -0.7492 | 14.695 |
| 1.7034 | 15.0 | 2790 | 2.2150 | {'rouge1': 0.4381, 'rouge2': 0.214, 'rougeL': 0.2158, 'rougeLsum': 0.2158} | 0.8809 | -0.7611 | 14.555 |
| 1.6671 | 16.0 | 2976 | 2.2211 | {'rouge1': 0.4388, 'rouge2': 0.2162, 'rougeL': 0.2169, 'rougeLsum': 0.2169} | 0.8797 | -0.7532 | 14.73 |
| 1.6964 | 17.0 | 3162 | 2.2207 | {'rouge1': 0.4316, 'rouge2': 0.2117, 'rougeL': 0.2137, 'rougeLsum': 0.2137} | 0.8799 | -0.7729 | 14.54 |
| 1.6556 | 18.0 | 3348 | 2.2183 | {'rouge1': 0.4379, 'rouge2': 0.2122, 'rougeL': 0.2163, 'rougeLsum': 0.2163} | 0.8804 | -0.7475 | 14.735 |
| 1.6391 | 19.0 | 3534 | 2.2200 | {'rouge1': 0.4332, 'rouge2': 0.2105, 'rougeL': 0.2149, 'rougeLsum': 0.2149} | 0.8805 | -0.7521 | 14.635 |
| 1.6309 | 20.0 | 3720 | 2.2226 | {'rouge1': 0.4351, 'rouge2': 0.2124, 'rougeL': 0.215, 'rougeLsum': 0.215} | 0.8806 | -0.7502 | 14.645 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
RiverTest/RiverMTG
|
RiverTest
| 2024-01-13T00:22:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"region:us"
] | null | 2024-01-13T00:21:44Z |
---
library_name: peft
base_model: teknium/OpenHermes-2.5-Mistral-7B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
ntc-ai/SDXL-LoRA-slider.the-starry-night
|
ntc-ai
| 2024-01-13T00:21:29Z | 13 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2024-01-13T00:21:26Z |
---
language:
- en
thumbnail: "images/evaluate/the starry night.../the starry night_17_3.0.png"
widget:
- text: the starry night
output:
url: images/the starry night_17_3.0.png
- text: the starry night
output:
url: images/the starry night_19_3.0.png
- text: the starry night
output:
url: images/the starry night_20_3.0.png
- text: the starry night
output:
url: images/the starry night_21_3.0.png
- text: the starry night
output:
url: images/the starry night_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "the starry night"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - the starry night (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/the starry night_17_-3.0.png" width=256 height=256 /> | <img src="images/the starry night_17_0.0.png" width=256 height=256 /> | <img src="images/the starry night_17_3.0.png" width=256 height=256 /> |
| <img src="images/the starry night_19_-3.0.png" width=256 height=256 /> | <img src="images/the starry night_19_0.0.png" width=256 height=256 /> | <img src="images/the starry night_19_3.0.png" width=256 height=256 /> |
| <img src="images/the starry night_20_-3.0.png" width=256 height=256 /> | <img src="images/the starry night_20_0.0.png" width=256 height=256 /> | <img src="images/the starry night_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
the starry night
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.the-starry-night', weight_name='the starry night.safetensors', adapter_name="the starry night")
# Activate the LoRA
pipe.set_adapters(["the starry night"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, the starry night"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1070+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
cnatale/Mistral-7B-Instruct-v0_1-Txt-2-Presto-SQL
|
cnatale
| 2024-01-13T00:14:40Z | 5 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-01-12T18:27:30Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: mistralai/Mistral-7B-Instruct-v0.1
model-index:
- name: Mistral-7B-Instruct-v0_1-Txt-2-Presto-SQL
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-Instruct-v0_1-Txt-2-Presto-SQL
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6454
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 80
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3516 | 0.71 | 10 | 1.0778 |
| 1.0131 | 1.43 | 20 | 0.8707 |
| 0.8446 | 2.14 | 30 | 0.7695 |
| 0.7563 | 2.86 | 40 | 0.7202 |
| 0.7009 | 3.57 | 50 | 0.6803 |
| 0.6368 | 4.29 | 60 | 0.6585 |
| 0.6201 | 5.0 | 70 | 0.6473 |
| 0.5755 | 5.71 | 80 | 0.6454 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
navneet1v/finetunedmodels
|
navneet1v
| 2024-01-13T00:01:26Z | 0 | 1 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-01-12T19:57:39Z |
---
license: apache-2.0
---
### Description
This repo contains the finetuned models zip that can be used to do the benchmarks with [neural search plugin](https://github.com/opensearch-project/neural-search).
### Models Reference with Datasets
| Data set Name | Link To download data set | Model Zip file Name | |
|---------------|----------------------------------------------------------------------------------------------------------------------------------------|----------------------|--------------------------------------------------------------------|
| NFCorpus | https://github.com/martin-gaievski/info-retrieval-test/blob/score-normalization-combination-testing/README.md#beers-available-datasets | nfcorpus_traced.zip | 23a3126a61055b1f6b7937e2fb89b418ec44639af562da2b691a6250a2fffecf |
| Trec-Covid | https://github.com/martin-gaievski/info-retrieval-test/blob/score-normalization-combination-testing/README.md#beers-available-datasets | trec_covid_tuned.zip | 774d19dd78acbdac62a1c9c13ec4f6c58d0209afe8c6c9a1d37b3d2d4d16fde5 |
| Scidocs | https://github.com/martin-gaievski/info-retrieval-test/blob/score-normalization-combination-testing/README.md#beers-available-datasets | scidocs_tuned.zip | c44d1345624857871d9202620be39c71cedf7fbe405f23c1be0ba4e9028a5117 |
| Quora | https://github.com/martin-gaievski/info-retrieval-test/blob/score-normalization-combination-testing/README.md#beers-available-datasets | quora_tuned.zip | 4d5e856b640cfaaa07af7cdc3f155f7144095f93dd1fd632b6fd0c0029b99535 |
| Amazon | https://github.com/amazon-science/esci-data?tab=readme-ov-file#usage | amazon_traced.zip | 8d54bd2c1a1a9e46a8f2fc86cf939d2674b08c55e65e1f2ba5359351bf76b3d8 |
| DBPedia | https://github.com/martin-gaievski/info-retrieval-test/blob/score-normalization-combination-testing/README.md#beers-available-datasets | dbpedia_tuned.zip | f14d99e5061d7add4ed4c01395c12cc79a22c32b4aea2de7e0521fa67c7c6546 |
| FiQA | https://github.com/martin-gaievski/info-retrieval-test/blob/score-normalization-combination-testing/README.md#beers-available-datasets | fiqa_tuned.zip | 1ddaf302dc224fb1dea084cbb67f2d87dd72b0053f69202f159dc58868e228e4 |
## Generating Hash
```
python create_hash.py <zipfile-name/or full path of zip>
```
|
TheBloke/TowerInstruct-7B-v0.1-GGUF
|
TheBloke
| 2024-01-12T23:57:32Z | 215 | 16 |
transformers
|
[
"transformers",
"gguf",
"llama",
"translation",
"en",
"de",
"fr",
"zh",
"pt",
"nl",
"ru",
"ko",
"it",
"es",
"base_model:Unbabel/TowerInstruct-7B-v0.1",
"base_model:quantized:Unbabel/TowerInstruct-7B-v0.1",
"license:cc-by-nc-4.0",
"region:us",
"conversational"
] |
translation
| 2024-01-12T23:50:11Z |
---
base_model: Unbabel/TowerInstruct-7B-v0.1
inference: false
language:
- en
- de
- fr
- zh
- pt
- nl
- ru
- ko
- it
- es
license: cc-by-nc-4.0
metrics:
- comet
model_creator: Unbabel
model_name: TowerInstruct 7B v0.1
model_type: llama
pipeline_tag: translation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# TowerInstruct 7B v0.1 - GGUF
- Model creator: [Unbabel](https://huggingface.co/Unbabel)
- Original model: [TowerInstruct 7B v0.1](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Unbabel's TowerInstruct 7B v0.1](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF)
* [Unbabel's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Unbabel's TowerInstruct 7B v0.1](https://huggingface.co/Unbabel/TowerInstruct-7B-v0.1).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [towerinstruct-7b-v0.1.Q2_K.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q2_K.gguf) | Q2_K | 2 | 2.53 GB| 5.03 GB | smallest, significant quality loss - not recommended for most purposes |
| [towerinstruct-7b-v0.1.Q3_K_S.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
| [towerinstruct-7b-v0.1.Q3_K_M.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
| [towerinstruct-7b-v0.1.Q3_K_L.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
| [towerinstruct-7b-v0.1.Q4_0.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [towerinstruct-7b-v0.1.Q4_K_S.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
| [towerinstruct-7b-v0.1.Q4_K_M.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
| [towerinstruct-7b-v0.1.Q5_0.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [towerinstruct-7b-v0.1.Q5_K_S.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
| [towerinstruct-7b-v0.1.Q5_K_M.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended |
| [towerinstruct-7b-v0.1.Q6_K.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
| [towerinstruct-7b-v0.1.Q8_0.gguf](https://huggingface.co/TheBloke/TowerInstruct-7B-v0.1-GGUF/blob/main/towerinstruct-7b-v0.1.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/TowerInstruct-7B-v0.1-GGUF and below it, a specific filename to download, such as: towerinstruct-7b-v0.1.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/TowerInstruct-7B-v0.1-GGUF towerinstruct-7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/TowerInstruct-7B-v0.1-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/TowerInstruct-7B-v0.1-GGUF towerinstruct-7b-v0.1.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m towerinstruct-7b-v0.1.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./towerinstruct-7b-v0.1.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./towerinstruct-7b-v0.1.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Unbabel's TowerInstruct 7B v0.1
# Model Card for TowerInstruct-7B-v0.1
## Model Details
### Model Description
TowerInstruct-7B is a language model that results from fine-tuning TowerBase on the TowerBlocks supervised fine-tuning dataset. TowerInstruct-7B-v0.1 is the first model in the series.
The model is trained to handle several translation-related tasks, such as general machine translation (e.g., sentence- and document-level translation, terminology-aware translation, context-aware translation), automatic post edition, named-entity recognition, gramatical error correction, and paraphrase generation.
We will release more details in the upcoming technical report.
- **Developed by:** Unbabel, Instituto Superior Técnico, CentraleSupélec University of Paris-Saclay
- **Model type:** A 7B parameter model fine-tuned on a mix of publicly available, synthetic datasets on translation-related tasks, as well as conversational datasets and code instructions.
- **Language(s) (NLP):** English, Portuguese, Spanish, French, German, Dutch, Italian, Korean, Chinese, Russian
- **License:** CC-BY-NC-4.0, Llama 2 is licensed under the [LLAMA 2 Community License](https://ai.meta.com/llama/license/), Copyright © Meta Platforms, Inc. All Rights Reserved.
- **Finetuned from model:** [TowerBase](https://huggingface.co/Unbabel/TowerBase-7B-v0.1)
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed supervised fine-tuning dataset ([TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1)), which contains a diverse range of data sources:
- Translation
- Automatic Post Edition
- Machine Translation Evaluation
- Context-aware Translation
- Terminology-aware Translation
- Multi-reference Translation
- Named-entity Recognition
- Paraphrase Generation
- Synthetic Chat data
- Code instructions
You can find the dataset and all data sources of [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1) here.
Here's how you can run the model using the `pipeline()` function from 🤗 Transformers:
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="Unbabel/TowerInstruct-v0.1", torch_dtype=torch.bfloat16, device_map="auto")
# We use the tokenizer’s chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
{"role": "user", "content": "Translate the following text from Portuguese into English.\nPortuguese: Um grupo de investigadores lançou um novo modelo para tarefas relacionadas com tradução.\nEnglish:"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=False)
print(outputs[0]["generated_text"])
# <|im_start|>user
# Translate the following text from Portuguese into English.
# Portuguese: Um grupo de investigadores lançou um novo modelo para tarefas relacionadas com tradução.
# English:<|im_end|>
# <|im_start|>assistant
# A group of researchers has launched a new model for translation-related tasks.
```
### Out-of-Scope Use
The model is not guaranteed to perform for languages other than the 10 languages it supports. Even though we trained the model on conversational data and code instructions, it is not intended to be used as a conversational chatbot or code assistant.
## Bias, Risks, and Limitations
TowerInstruct-v0.1 has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).
## Prompt Format
TowerInstruct-v0.1 was trained using the ChatML prompt templates without any system prompts. An example follows below:
```
<|im_start|>user
{USER PROMPT}<|im_end|>
<|im_start|>assistant
{MODEL RESPONSE}<|im_end|>
<|im_start|>user
[...]
```
### Supervised tasks
The prompts for all supervised tasks can be found in [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1). We have used multiple prompt templates for each task. While different prompts may offer different outputs, the difference in downstream performance should be very minimal.
## Training Details
### Training Data
Link to [TowerBlocks](https://huggingface.co/datasets/Unbabel/TowerBlocks-v0.1).
#### Training Hyperparameters
The following hyperparameters were used during training:
- total_train_batch_size: 256
- learning_rate: 7e-06
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 500
- weight_decay: 0.01
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- num_epochs: 4
- max_seq_length: 2048
## Citation
To be completed.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<!-- original-model-card end -->
|
TheBloke/PiVoT-SUS-RP-GPTQ
|
TheBloke
| 2024-01-12T23:49:50Z | 8 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:maywell/PiVoT-SUS-RP",
"base_model:quantized:maywell/PiVoT-SUS-RP",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-01-12T18:57:15Z |
---
base_model: maywell/PiVoT-SUS-RP
inference: false
license: apache-2.0
model_creator: Jeonghwan Park
model_name: Pivot SUS RP
model_type: yi
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Pivot SUS RP - GPTQ
- Model creator: [Jeonghwan Park](https://huggingface.co/maywell)
- Original model: [Pivot SUS RP](https://huggingface.co/maywell/PiVoT-SUS-RP)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Jeonghwan Park's Pivot SUS RP](https://huggingface.co/maywell/PiVoT-SUS-RP).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/PiVoT-SUS-RP-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF)
* [Jeonghwan Park's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/maywell/PiVoT-SUS-RP)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 18.60 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 19.25 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 21.21 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 15.03 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 35.34 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 16.90 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 36.11 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/PiVoT-SUS-RP-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/PiVoT-SUS-RP-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `PiVoT-SUS-RP-GPTQ`:
```shell
mkdir PiVoT-SUS-RP-GPTQ
huggingface-cli download TheBloke/PiVoT-SUS-RP-GPTQ --local-dir PiVoT-SUS-RP-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir PiVoT-SUS-RP-GPTQ
huggingface-cli download TheBloke/PiVoT-SUS-RP-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir PiVoT-SUS-RP-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir PiVoT-SUS-RP-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/PiVoT-SUS-RP-GPTQ --local-dir PiVoT-SUS-RP-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/PiVoT-SUS-RP-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/PiVoT-SUS-RP-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `PiVoT-SUS-RP-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/PiVoT-SUS-RP-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(
prompt_template,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/PiVoT-SUS-RP-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Jeonghwan Park's Pivot SUS RP
|
jysssacc/opt-350m_adalora_lr0.05_bs10_epoch5_wd0.01
|
jysssacc
| 2024-01-12T23:45:25Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"region:us"
] | null | 2024-01-12T23:43:47Z |
---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/opt-350m
model-index:
- name: opt-350m_adalora_lr0.05_bs10_epoch5_wd0.01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-350m_adalora_lr0.05_bs10_epoch5_wd0.01
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 9.2670
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 63 | 3.8373 |
| 4.0958 | 2.0 | 126 | 5.9224 |
| 4.0958 | 3.0 | 189 | 8.2854 |
| 6.616 | 4.0 | 252 | 9.5332 |
| 12.126 | 5.0 | 315 | 9.2670 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
LoneStriker/Bagel-Hermes-2x34b-4.0bpw-h6-exl2
|
LoneStriker
| 2024-01-12T23:11:50Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"yi",
"moe",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T22:59:16Z |
---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
tags:
- yi
- moe
---
# Bagel-Hermes-2x34B
This is the model for Bagel-Hermes-2x34B. I used [mergekit](https://github.com/cg123/mergekit) to make this MOE model.
# Prompt Template(s):
Since [bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2) uses many prompt templates, and [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) uses ChatML, you can utilize ChatML and other prompt templates provided by bagel.
**Note:** I currently do not know which prompt template is best.
### ChatML:
```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
```
### Alpaca (sort of)
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system}
{instruction}
### Response:
```
### Vicuna
```
{system}
USER: {instruction}
ASSISTANT:
```
Visit [bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2) to try more prompt templates.
# Yaml Config to reproduce
```yaml
base_model: nontoxic-bagel-34b-v0.2
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: bagel-dpo-34b-v0.2
positive_prompts: ["question answering", "Q:", science", "biology", "chemistry", "physics"]
- source_model: Nous-Hermes-2-Yi-34B
positive_prompts: ["chat", "math", "reason", "mathematics", "solve", "count", "python", "javascript", "programming", "algorithm", "tell me", "assistant"]
```
# Quantizationed versions
Quantizationed versions of this model is available thanks to [TheBloke](https://hf.co/TheBloke).
##### GPTQ
- [TheBloke/Bagel-Hermes-2x34B-GPTQ](https://huggingface.co/TheBloke/Bagel-Hermes-2x34B-GPTQ)
##### GGUF
- [TheBloke/Bagel-Hermes-2x34B-GGUF](https://huggingface.co/TheBloke/Bagel-Hermes-2x34B-GGUF)
##### AWQ
- [TheBloke/Bagel-Hermes-2x34B-AWQ](https://huggingface.co/TheBloke/Bagel-Hermes-2x34B-AWQ)
If you would like to support me:
[☕ Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi)
|
Utshav/bert-fined-tuned-cola
|
Utshav
| 2024-01-12T23:09:10Z | 95 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-12T22:57:04Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: bert-fined-tuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fined-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7846
- Matthews Correlation: 0.5626
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.453 | 1.0 | 1069 | 0.4373 | 0.5189 |
| 0.3124 | 2.0 | 2138 | 0.6774 | 0.5727 |
| 0.1966 | 3.0 | 3207 | 0.7846 | 0.5626 |
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.0+cu121
- Tokenizers 0.15.0
|
Ricardo54321/TaxiV3
|
Ricardo54321
| 2024-01-12T23:08:31Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-12T22:39:38Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: TaxiV3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Ricardo54321/TaxiV3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
andrewma5/FoodBase-distilBERT
|
andrewma5
| 2024-01-12T22:56:48Z | 46 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-12T22:48:31Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: andrewma5/FoodBase-distilBERT
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# andrewma5/FoodBase-distilBERT
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0368
- Validation Loss: 0.0443
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'inner_optimizer': {'module': 'transformers.optimization_tf', 'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 190, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.8999999761581421, 'beta_2': 0.9990000128746033, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}, 'registered_name': 'AdamWeightDecay'}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.5478 | 0.2080 | 0 |
| 0.1247 | 0.0643 | 1 |
| 0.0532 | 0.0492 | 2 |
| 0.0416 | 0.0447 | 3 |
| 0.0368 | 0.0443 | 4 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.15.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
LoneStriker/Bagel-Hermes-2x34b-3.0bpw-h6-exl2
|
LoneStriker
| 2024-01-12T22:34:58Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"yi",
"moe",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T22:25:17Z |
---
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
tags:
- yi
- moe
---
# Bagel-Hermes-2x34B
This is the model for Bagel-Hermes-2x34B. I used [mergekit](https://github.com/cg123/mergekit) to make this MOE model.
# Prompt Template(s):
Since [bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2) uses many prompt templates, and [Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) uses ChatML, you can utilize ChatML and other prompt templates provided by bagel.
**Note:** I currently do not know which prompt template is best.
### ChatML:
```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
```
### Alpaca (sort of)
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system}
{instruction}
### Response:
```
### Vicuna
```
{system}
USER: {instruction}
ASSISTANT:
```
Visit [bagel-dpo-34b-v0.2](https://huggingface.co/jondurbin/bagel-dpo-34b-v0.2) to try more prompt templates.
# Yaml Config to reproduce
```yaml
base_model: nontoxic-bagel-34b-v0.2
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: bagel-dpo-34b-v0.2
positive_prompts: ["question answering", "Q:", science", "biology", "chemistry", "physics"]
- source_model: Nous-Hermes-2-Yi-34B
positive_prompts: ["chat", "math", "reason", "mathematics", "solve", "count", "python", "javascript", "programming", "algorithm", "tell me", "assistant"]
```
# Quantizationed versions
Quantizationed versions of this model is available thanks to [TheBloke](https://hf.co/TheBloke).
##### GPTQ
- [TheBloke/Bagel-Hermes-2x34B-GPTQ](https://huggingface.co/TheBloke/Bagel-Hermes-2x34B-GPTQ)
##### GGUF
- [TheBloke/Bagel-Hermes-2x34B-GGUF](https://huggingface.co/TheBloke/Bagel-Hermes-2x34B-GGUF)
##### AWQ
- [TheBloke/Bagel-Hermes-2x34B-AWQ](https://huggingface.co/TheBloke/Bagel-Hermes-2x34B-AWQ)
If you would like to support me:
[☕ Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi)
|
TheBloke/PiVoT-SUS-RP-GGUF
|
TheBloke
| 2024-01-12T22:31:47Z | 40 | 1 |
transformers
|
[
"transformers",
"gguf",
"yi",
"base_model:maywell/PiVoT-SUS-RP",
"base_model:quantized:maywell/PiVoT-SUS-RP",
"license:apache-2.0",
"region:us"
] | null | 2024-01-12T18:57:15Z |
---
base_model: maywell/PiVoT-SUS-RP
inference: false
license: apache-2.0
model_creator: Jeonghwan Park
model_name: Pivot SUS RP
model_type: yi
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Pivot SUS RP - GGUF
- Model creator: [Jeonghwan Park](https://huggingface.co/maywell)
- Original model: [Pivot SUS RP](https://huggingface.co/maywell/PiVoT-SUS-RP)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jeonghwan Park's Pivot SUS RP](https://huggingface.co/maywell/PiVoT-SUS-RP).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/PiVoT-SUS-RP-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF)
* [Jeonghwan Park's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/maywell/PiVoT-SUS-RP)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [pivot-sus-rp.Q2_K.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q2_K.gguf) | Q2_K | 2 | 12.77 GB| 15.27 GB | smallest, significant quality loss - not recommended for most purposes |
| [pivot-sus-rp.Q3_K_S.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q3_K_S.gguf) | Q3_K_S | 3 | 14.96 GB| 17.46 GB | very small, high quality loss |
| [pivot-sus-rp.Q3_K_M.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q3_K_M.gguf) | Q3_K_M | 3 | 16.65 GB| 19.15 GB | very small, high quality loss |
| [pivot-sus-rp.Q3_K_L.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q3_K_L.gguf) | Q3_K_L | 3 | 18.14 GB| 20.64 GB | small, substantial quality loss |
| [pivot-sus-rp.Q4_0.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q4_0.gguf) | Q4_0 | 4 | 19.47 GB| 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [pivot-sus-rp.Q4_K_S.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q4_K_S.gguf) | Q4_K_S | 4 | 19.60 GB| 22.10 GB | small, greater quality loss |
| [pivot-sus-rp.Q4_K_M.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q4_K_M.gguf) | Q4_K_M | 4 | 20.66 GB| 23.16 GB | medium, balanced quality - recommended |
| [pivot-sus-rp.Q5_0.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q5_0.gguf) | Q5_0 | 5 | 23.71 GB| 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [pivot-sus-rp.Q5_K_S.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q5_K_S.gguf) | Q5_K_S | 5 | 23.71 GB| 26.21 GB | large, low quality loss - recommended |
| [pivot-sus-rp.Q5_K_M.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q5_K_M.gguf) | Q5_K_M | 5 | 24.32 GB| 26.82 GB | large, very low quality loss - recommended |
| [pivot-sus-rp.Q6_K.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q6_K.gguf) | Q6_K | 6 | 28.21 GB| 30.71 GB | very large, extremely low quality loss |
| [pivot-sus-rp.Q8_0.gguf](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF/blob/main/pivot-sus-rp.Q8_0.gguf) | Q8_0 | 8 | 36.54 GB| 39.04 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/PiVoT-SUS-RP-GGUF and below it, a specific filename to download, such as: pivot-sus-rp.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/PiVoT-SUS-RP-GGUF pivot-sus-rp.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/PiVoT-SUS-RP-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/PiVoT-SUS-RP-GGUF pivot-sus-rp.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m pivot-sus-rp.Q4_K_M.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./pivot-sus-rp.Q4_K_M.gguf", # Download the model file first
n_ctx=8192, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"{prompt}", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./pivot-sus-rp.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Jeonghwan Park's Pivot SUS RP
<!-- original-model-card end -->
|
kkireyev/falcon-7b-chat-oasst1
|
kkireyev
| 2024-01-12T22:19:19Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"region:us"
] | null | 2024-01-12T22:19:17Z |
---
library_name: peft
base_model: tiiuae/falcon-7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
dan0102dan/ohuenko
|
dan0102dan
| 2024-01-12T22:15:31Z | 1 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2024-01-12T12:28:56Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: arts sks
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
TheBloke/PiVoT-SUS-RP-AWQ
|
TheBloke
| 2024-01-12T22:13:01Z | 7 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:maywell/PiVoT-SUS-RP",
"base_model:quantized:maywell/PiVoT-SUS-RP",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2024-01-12T18:57:15Z |
---
base_model: maywell/PiVoT-SUS-RP
inference: false
license: apache-2.0
model_creator: Jeonghwan Park
model_name: Pivot SUS RP
model_type: yi
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Pivot SUS RP - AWQ
- Model creator: [Jeonghwan Park](https://huggingface.co/maywell)
- Original model: [Pivot SUS RP](https://huggingface.co/maywell/PiVoT-SUS-RP)
<!-- description start -->
## Description
This repo contains AWQ model files for [Jeonghwan Park's Pivot SUS RP](https://huggingface.co/maywell/PiVoT-SUS-RP).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/PiVoT-SUS-RP-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/PiVoT-SUS-RP-GGUF)
* [Jeonghwan Park's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/maywell/PiVoT-SUS-RP)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/PiVoT-SUS-RP-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 19.23 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/PiVoT-SUS-RP-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `PiVoT-SUS-RP-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/PiVoT-SUS-RP-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''{prompt}
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/PiVoT-SUS-RP-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/PiVoT-SUS-RP-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/PiVoT-SUS-RP-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Jeonghwan Park's Pivot SUS RP
|
skehlet/mistral-7b-know_sql-finetune
|
skehlet
| 2024-01-12T22:04:10Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"mistral",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2024-01-12T00:27:58Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
togethercomputer/m2-bert-80M-32k-retrieval
|
togethercomputer
| 2024-01-12T21:49:00Z | 439 | 125 |
transformers
|
[
"transformers",
"pytorch",
"m2_bert",
"text-classification",
"sentence-similarity",
"custom_code",
"en",
"arxiv:2310.12109",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
sentence-similarity
| 2023-11-04T03:09:52Z |
---
license: apache-2.0
language:
- en
pipeline_tag: sentence-similarity
inference: false
---
# Monarch Mixer-BERT
An 80M checkpoint of M2-BERT, pretrained with sequence length 32768, and it has been fine-tuned for long-context retrieval.
Check out the paper [Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture](https://arxiv.org/abs/2310.12109) and our [blog post]() on retrieval for more on how we trained this model for long sequence.
This model was trained by Jon Saad-Falcon, Dan Fu, and Simran Arora.
Check out our [GitHub](https://github.com/HazyResearch/m2/tree/main) for instructions on how to download and fine-tune it!
## How to use
You can load this model using Hugging Face `AutoModel`:
```python
from transformers import AutoModelForSequenceClassification
model = AutoModelForSequenceClassification.from_pretrained(
"togethercomputer/m2-bert-80M-32k-retrieval",
trust_remote_code=True
)
```
You should expect to see a large error message about unused parameters for FlashFFTConv.
If you'd like to load the model with FlashFFTConv, you can check out our [GitHub](https://github.com/HazyResearch/m2/tree/main).
This model generates embeddings for retrieval. The embeddings have a dimensionality of 768:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
max_seq_length = 32768
testing_string = "Every morning, I make a cup of coffee to start my day."
model = AutoModelForSequenceClassification.from_pretrained(
"togethercomputer/m2-bert-80M-32k-retrieval",
trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained(
"bert-base-uncased",
model_max_length=max_seq_length
)
input_ids = tokenizer(
[testing_string],
return_tensors="pt",
padding="max_length",
return_token_type_ids=False,
truncation=True,
max_length=max_seq_length
)
outputs = model(**input_ids)
embeddings = outputs['sentence_embedding']
```
You can also get embeddings from this model using the Together API as follows (you can find your API key [here](https://api.together.xyz/settings/api-keys)):
```python
import os
import requests
def generate_together_embeddings(text: str, model_api_string: str, api_key: str):
url = "https://api.together.xyz/api/v1/embeddings"
headers = {
"accept": "application/json",
"content-type": "application/json",
"Authorization": f"Bearer {api_key}"
}
session = requests.Session()
response = session.post(
url,
headers=headers,
json={
"input": text,
"model": model_api_string
}
)
if response.status_code != 200:
raise ValueError(f"Request failed with status code {response.status_code}: {response.text}")
return response.json()['data'][0]['embedding']
print(generate_together_embeddings(
'Hello world',
'togethercomputer/m2-bert-80M-32k-retrieval',
os.environ['TOGETHER_API_KEY'])[:10]
)
```
## Acknowledgments
Alycia Lee helped with AutoModel support.
## Citation
If you use this model, or otherwise found our work valuable, you can cite us as follows:
```
@inproceedings{fu2023monarch,
title={Monarch Mixer: A Simple Sub-Quadratic GEMM-Based Architecture},
author={Fu, Daniel Y and Arora, Simran and Grogan, Jessica and Johnson, Isys and Eyuboglu, Sabri and Thomas, Armin W and Spector, Benjamin and Poli, Michael and Rudra, Atri and R{\'e}, Christopher},
booktitle={Advances in Neural Information Processing Systems},
year={2023}
}
```
|
virtsion/nilmformer_final_generic_prompt_50tokens
|
virtsion
| 2024-01-12T21:36:29Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-12T21:36:28Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
kim-sha/sql-sodabot-v1.0
|
kim-sha
| 2024-01-12T21:36:02Z | 92 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Salesforce/codet5-small",
"base_model:finetune:Salesforce/codet5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-12T18:05:46Z |
---
license: apache-2.0
base_model: Salesforce/codet5-small
tags:
- generated_from_trainer
model-index:
- name: sodabot-sql-sm
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sql-sodabot-v1.0
This encoder-decoder model is a descendent of [Salesforce/codet5-small](https://huggingface.co/Salesforce/codet5-small), fine-tuned on a modified version of [b-mc2/sql-create-context](https://huggingface.co/datasets/b-mc2/sql-create-context) data. The original [CodeT5](https://github.com/salesforce/CodeT5) was published by Salesfoce Research as an "AI-powered coding assistant to boost the productivity of software developers". The goal of this project is to apply transfer learning in order to appropriate this model for text-to-SQL applications, specifically in the context of generating Socrata SQL ([SoQL](https://dev.socrata.com/docs/queries/)) queries that can be executed on the Socrata Open Data API (e.g., to analyze [NYC Open Data](https://opendata.cityofnewyork.us)).
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
ntc-ai/SDXL-LoRA-slider.looking-out-of-the-window
|
ntc-ai
| 2024-01-12T21:21:17Z | 11 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2024-01-12T21:21:14Z |
---
language:
- en
thumbnail: "images/evaluate/looking out of the window.../looking out of the window_17_3.0.png"
widget:
- text: looking out of the window
output:
url: images/looking out of the window_17_3.0.png
- text: looking out of the window
output:
url: images/looking out of the window_19_3.0.png
- text: looking out of the window
output:
url: images/looking out of the window_20_3.0.png
- text: looking out of the window
output:
url: images/looking out of the window_21_3.0.png
- text: looking out of the window
output:
url: images/looking out of the window_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "looking out of the window"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - looking out of the window (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/looking out of the window_17_-3.0.png" width=256 height=256 /> | <img src="images/looking out of the window_17_0.0.png" width=256 height=256 /> | <img src="images/looking out of the window_17_3.0.png" width=256 height=256 /> |
| <img src="images/looking out of the window_19_-3.0.png" width=256 height=256 /> | <img src="images/looking out of the window_19_0.0.png" width=256 height=256 /> | <img src="images/looking out of the window_19_3.0.png" width=256 height=256 /> |
| <img src="images/looking out of the window_20_-3.0.png" width=256 height=256 /> | <img src="images/looking out of the window_20_0.0.png" width=256 height=256 /> | <img src="images/looking out of the window_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
looking out of the window
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.looking-out-of-the-window', weight_name='looking out of the window.safetensors', adapter_name="looking out of the window")
# Activate the LoRA
pipe.set_adapters(["looking out of the window"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, looking out of the window"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 1060+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
NucleusOrg/Nucleus-1B-alpha-1
|
NucleusOrg
| 2024-01-12T21:14:28Z | 170 | 12 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:nampdn-ai/tiny-textbooks",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T10:19:56Z |
---
license: mit
language:
- en
datasets:
- nampdn-ai/tiny-textbooks
---
# Nuclues 1B Alpha1
<p align="center">
<img src="https://github.com/prp-e/nucleus/raw/main/nucleus-logo.png" width=256 height=256>
</p>
## What is Nucleus?
Nucleus is a small language model based on Mistral (actually, the trimmed untrained version you can find [here](https://huggingface.co/lmlab/lmlab-mistral-1b-untrained)) and trained in different steps. First, we've pretrained it on TinyStories dataset, then [TinyTextBooks](https://huggingface.co/datasets/nampdn-ai/tiny-textbooks) to make it a more specific model. This model is just a _proof of concept_ at this point, but showed good promises in early tests. So with proper training, can be a good product over time!
## Inference
[](https://colab.research.google.com/github/prp-e/nucleus/blob/main/nucleus_1b_inference.ipynb)
First you need to install `transformers` and `accelerate` libraries in order to run this model. Then, you basically have to run the following code:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
import torch
model_name_or_id = "NucleusOrg/Nucleus-1B-alpha-1"
model = AutoModelForCausalLM.from_pretrained(model_name_or_id, torch_dtype=torch.float16, device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_id)
prompt = "### Lesson: Python Programming 101\n### Introduction\n"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
generation_config = GenerationConfig(
do_sample=True,
top_k=1,
temperature=0.9,
max_new_tokens=500,
repetition_penalty=1.5,
pad_token_id=tokenizer.eos_token_id
)
outputs = model.generate(**inputs, generation_config=generation_config)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
__Prompt Format__: This model does not have a specific prompt format, but the best results could be achieved with a _textbook_ type of format like:
```
### Chapter 1: Elon Musk and Iron Man
Elon met Tony at a Cafe in Monaco, then they had a conversation about
```
You also can try something like this:
```
Question: Who are you?
Answer:
```
But since the model isn't made for chat/question answering, the result won't be good enough.
__Repetition Penalty__: Since most of these models like to repeat themselves, just keep that number there. You can increase or decrease it based on your liking,but keep in mind that a number lower than 1 makes the model _super repetitive_.
## Known Issues
* Since we only had 420k rows of data, a lot of information are missing on this model. Since mentioned earlier in this very model card, it's a _proof of concept_ model.
* You probably may test it with coding. Let's say that the model is terrible at coding. We may release a coding optimized model as soon as possible.
## Our Team
* Muhammadreza Haghiri ([X (formerly Twitter)](https://twitter.com/haghiri_ai) - [Website](https://haghiri75.com/en) - [Github](https://github.com/prp-e) - [LinkedIn](https://www.linkedin.com/in/muhammadreza-haghiri-1761325b))
* Mahi Mohrechi ([Website](https://mohrechi-portfolio.vercel.app/) - [Github](https://github.com/f-mohrechi) - [LinkedIn](https://www.linkedin.com/in/faeze-mohrechi/))
## Special Thanks
* LMLabs for providing 1B untrained model.
* Mistral Team for providing the best open source base model ever.
* _Sina Rashidi_, who translated Alpaca dataset to Persian.
* [Jupyto](https://jupyto.com) team for providing our infrastructure.
|
chris1tava/position_names
|
chris1tava
| 2024-01-12T21:03:57Z | 93 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"en",
"dataset:chris1tava/position_names",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-07T02:21:11Z |
---
license: mit
datasets:
- chris1tava/position_names
language:
- en
library_name: transformers
pipeline_tag: token-classification
---
|
yaizaa/winxclub
|
yaizaa
| 2024-01-12T20:59:19Z | 0 | 1 |
fastai
|
[
"fastai",
"region:us"
] | null | 2024-01-12T20:10:31Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Ricardo54321/Huggy
|
Ricardo54321
| 2024-01-12T20:54:10Z | 20 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2024-01-12T20:53:54Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Ricardo54321/Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
timotewb/twb-image-03-512-lora
|
timotewb
| 2024-01-12T20:50:04Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-12T03:56:29Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - timotewb/twb-image-03-512-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following.
|
Edmon02/speecht5_finetuned_hy
|
Edmon02
| 2024-01-12T20:42:32Z | 23 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"hy",
"en",
"nl",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"region:us"
] |
text-to-speech
| 2023-12-07T18:57:17Z |
---
inference: false
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_hy
results: []
language:
- hy
- en
- nl
datasets:
- mozilla-foundation/common_voice_11_0
pipeline_tag: text-to-speech
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_hy
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4785
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 125
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5547 | 2.04 | 250 | 0.4983 |
| 0.525 | 4.07 | 500 | 0.4864 |
| 0.52 | 6.11 | 750 | 0.4812 |
| 0.5286 | 8.15 | 1000 | 0.4785 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.0
- Tokenizers 0.15.0
|
virtsion/nilmformer_final_generic_prompt_2
|
virtsion
| 2024-01-12T20:42:14Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-12T20:41:08Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
giux78/zefiro-7b-beta-ITA-v0.1
|
giux78
| 2024-01-12T20:16:57Z | 2,797 | 9 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"it",
"dataset:giux78/100k-sft-ready-ultrafeedback-ita",
"arxiv:2310.16944",
"arxiv:2312.09993",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-09T21:07:32Z |
---
license: apache-2.0
datasets:
- giux78/100k-sft-ready-ultrafeedback-ita
language:
- it
pipeline_tag: text-generation
---
<img src="https://hoodie-creator.s3.eu-west-1.amazonaws.com/15be78c6-original.png" alt="llamantino53" border="0" width="400px">
# Model Card for zefiro-7b-beta-ITA-v0.1
*Last Update: 11/01/2024*<br>
<!-- Provide a quick summary of what the model is/does. -->
Zefiro is a SFT fine tuned model for the Italian language based on [Mistral](https://huggingface.co/mistralai/Mistral-7B-v0.1) .
To create a set of open source models and datasets suited for italian language is the aim of the project and this is the first experiment.
The model can be used as base model for more specific conversationl tasks for Italian language
## Model Details
Zefiro is a porting of the [Zephyr](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) model to the italian language using the wonderful recipes
from [alignment-handbook](https://huggingface.co/alignment-handbook) . It has also taken ispiration and insights from the [Llamantino](https://huggingface.co/swap-uniba/LLaMAntino-2-chat-7b-hf-UltraChat-ITA) model
developed by Università di Bari. For the implementation we combined different approaches from the two models mentioned but also from the wondeful communtity of open source.
## Model description
- **Model type:** A 7B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily Italian
- **License:** Apache 2
- **Finetuned from model:** [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- **Developed by:** [giux78](https://alessandroercolani.webflow.io/)
- **Funded by:** [Business Operating System](https://www.businessos.xyz)
## Intended uses & limitations
The model was initially fine-tuned on a filtered and preprocessed version of [UltraChat-ITA](https://huggingface.co/datasets/giux78/100k-sft-ready-ultrafeedback-ita) that is a filtered version of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat) dataset, which contains a diverse range of synthetic dialogues generated by ChatGPT.
Here's how you can run the model using Transformers from 🤗 :
```python
# Install transformers from source - only needed for versions <= v4.34
# pip install git+https://github.com/huggingface/transformers.git
# pip install accelerate
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "giux78/zefiro-7b-beta-ITA-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_id)
model.to('cuda')
tokenizer = AutoTokenizer.from_pretrained(model_id, padding_side="left")
sys_prompt = "Sei un assistente disponibile, rispettoso e onesto. " \
"Rispondi sempre nel modo piu' utile possibile, pur essendo sicuro. " \
"Le risposte non devono includere contenuti dannosi, non etici, razzisti, sessisti, tossici, pericolosi o illegali. " \
"Assicurati che le tue risposte siano socialmente imparziali e positive. " \
"Se una domanda non ha senso o non e' coerente con i fatti, spiegane il motivo invece di rispondere in modo non corretto. " \
"Se non conosci la risposta a una domanda, non condividere informazioni false."
messages = [{ 'content' : sys_prompt, 'role' : 'assistant'},
{'content' : 'Crea una lista su cosa mangiare a pranzo ogni giorno della settimana a pranzo e cena', 'role' : 'user'}]
def generate_text(sys_prompt, user_prompt):
messages = [{ 'content' : sys_prompt, 'role' : 'assistant'},
{'content' : user_prompt, 'role' : 'user'}]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
generated_ids = model.generate(**model_inputs, max_new_tokens=1024)
return tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
generate_text(sys_prompt, 'cosa ne pensi della politica italiana?')
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
Zefiro-7b-beta-ITA-v0.1 has not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
It is also unknown what the size and composition of the corpus was used to train the base model (`mistralai/Mistral-7B-v0.1`), however it is likely to have included a mix of Web data and technical sources like books and code. See the [Falcon 180B model card](https://huggingface.co/tiiuae/falcon-180B#training-data) for an example of this.
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
We used [UltraChat-ITA](https://huggingface.co/datasets/giux78/100k-sft-ready-ultrafeedback-ita) as training data that is a filtered version of the [`UltraChat`](https://huggingface.co/datasets/stingning/ultrachat).
For translating the dataset we combined different tools and API we are also evaluating the best approach for translating many more datasets.
We have seen that the translation phase is critical and can introduce incorrect syntax and semantics.
#### Summary
Zefiro-7b-beta-ITA-v0.1 is finetuned version of mistral-7b using the zephyr approach for the italian language.
## Citation
```
@misc{tunstall2023zephyr,
title={Zephyr: Direct Distillation of LM Alignment},
author={Lewis Tunstall and Edward Beeching and Nathan Lambert and Nazneen Rajani and Kashif Rasul and Younes Belkada and Shengyi Huang and Leandro von Werra and Clémentine Fourrier and Nathan Habib and Nathan Sarrazin and Omar Sanseviero and Alexander M. Rush and Thomas Wolf},
year={2023},
eprint={2310.16944},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
@misc{basile2023llamantino,
title={LLaMAntino: LLaMA 2 Models for Effective Text Generation in Italian Language},
author={Pierpaolo Basile and Elio Musacchio and Marco Polignano and Lucia Siciliani and Giuseppe Fiameni and Giovanni Semeraro},
year={2023},
eprint={2312.09993},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
## Model Card Authors
[giux78](https://huggingface.co/giux78)
## Model Card Contact
**ale.ercolani@gmail.com
|
TheBloke/TenyxChat-7B-v1-GPTQ
|
TheBloke
| 2024-01-12T20:14:42Z | 17 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"tenyx-fine-tuning",
"dpo",
"tenyxchat",
"conversational",
"en",
"arxiv:2305.18290",
"arxiv:2309.11235",
"arxiv:2306.05685",
"arxiv:1803.05457",
"arxiv:1905.07830",
"arxiv:2009.03300",
"arxiv:2109.07958",
"arxiv:1907.10641",
"arxiv:2110.14168",
"base_model:tenyx/TenyxChat-7B-v1",
"base_model:quantized:tenyx/TenyxChat-7B-v1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-01-12T19:46:35Z |
---
base_model: tenyx/TenyxChat-7B-v1
inference: false
language:
- en
library_name: transformers
license: apache-2.0
model_creator: Tenyx
model_name: TenyxChat 7B v1
model_type: mistral
prompt_template: 'System: {system_message}
User: {prompt}
Assistant:
'
quantized_by: TheBloke
tags:
- tenyx-fine-tuning
- dpo
- tenyxchat
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# TenyxChat 7B v1 - GPTQ
- Model creator: [Tenyx](https://huggingface.co/tenyx)
- Original model: [TenyxChat 7B v1](https://huggingface.co/tenyx/TenyxChat-7B-v1)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Tenyx's TenyxChat 7B v1](https://huggingface.co/tenyx/TenyxChat-7B-v1).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/TenyxChat-7B-v1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/TenyxChat-7B-v1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/TenyxChat-7B-v1-GGUF)
* [Tenyx's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/tenyx/TenyxChat-7B-v1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: System-User-Assistant-nohash
```
System: {system_message}
User: {prompt}
Assistant:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/TenyxChat-7B-v1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/TenyxChat-7B-v1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/TenyxChat-7B-v1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/TenyxChat-7B-v1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/TenyxChat-7B-v1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/TenyxChat-7B-v1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.30 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/TenyxChat-7B-v1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/TenyxChat-7B-v1-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `TenyxChat-7B-v1-GPTQ`:
```shell
mkdir TenyxChat-7B-v1-GPTQ
huggingface-cli download TheBloke/TenyxChat-7B-v1-GPTQ --local-dir TenyxChat-7B-v1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir TenyxChat-7B-v1-GPTQ
huggingface-cli download TheBloke/TenyxChat-7B-v1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir TenyxChat-7B-v1-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir TenyxChat-7B-v1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/TenyxChat-7B-v1-GPTQ --local-dir TenyxChat-7B-v1-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/TenyxChat-7B-v1-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/TenyxChat-7B-v1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/TenyxChat-7B-v1-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `TenyxChat-7B-v1-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/TenyxChat-7B-v1-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''System: {system_message}
User: {prompt}
Assistant:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(
prompt_template,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/TenyxChat-7B-v1-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''System: {system_message}
User: {prompt}
Assistant:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Tenyx's TenyxChat 7B v1
# TenyxChat: Language Model Alignment using Tenyx Fine-tuning
Introducing TenyxChat, a series of ChatGPT-like models trained to function as useful assistants through preference tuning, using Tenyx's recently released advanced fine-tuning technology ([VentureBeat article](https://venturebeat.com/ai/tenyx-aims-to-fix-llms-catastrophic-forgetting-problem/)). Our first chat model in the series, TenyxChat-7B-v1, is trained using the [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) framework on the open-source AI feedback dataset [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
We fine-tune [Openchat-3.5](https://arxiv.org/pdf/2309.11235.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods), [service](https://www.tenyx.com/fine-tuning)), which shows an increase in [MT-Bench](https://arxiv.org/abs/2306.05685), without a drop in performance of the model on other benchmarks. Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-7B-v1 was trained using eight A100s (80GB) for two hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)).
# Model details
- Model type: Fine-tuned 7B model for chat.
- License: Apache 2.0
- Base model: OpenChat 3.5 ([https://huggingface.co/openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5))
- Demo: [spaces/tenyx/TenyxChat-7B-v1](https://huggingface.co/spaces/tenyx/TenyxChat-7B-v1)
## Usage
Our model uses a simple chat template based on OpenChat 3.5. The chat template usage with a Hugging face generation example is shown below.
### Chat Template (Jinja)
```rust
{{ bos_token }}
{% for message in messages %}
{% if message['role'] == 'user' %}
{{ 'User:' + message['content'] + eos_token }}
{% elif message['role'] == 'system' %}
{{ 'System:' + message['content'] + eos_token }}
{% elif message['role'] == 'assistant' %}
{{ 'Assistant:' + message['content'] + eos_token }}
{% endif %}
{% if loop.last and add_generation_prompt %}\n{{ 'Assistant:' }}{% endif %}\n{% endfor %}
```
### Hugging face Example
```python
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="tenyx/TenyxChat-7B-v1", torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate."},
{"role": "user", "content": "Hi. I would like to make a hotel booking."},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=512, do_sample=False)
```
### Output
```
<s> System:You are a friendly chatbot who always responds in the style of a pirate.<|end_of_turn|>
User:Hi. I would like to make a hotel booking.<|end_of_turn|>
Assistant: Ahoy there me hearty! Arr, ye be lookin' fer a place to rest yer weary bones, eh?
Well then, let's set sail on this grand adventure and find ye a swell place to stay!
To begin, tell me the location ye be seekin' and the dates ye be lookin' to set sail.
And don't ye worry, me matey, I'll be sure to find ye a place that'll make ye feel like a king or queen on land!
```
# Performance
At the time of release (Jan 2024), TenyxChat-7B-v1 is the highest-ranked 7B chat model on the MT-Bench evaluation available for download and commercial use. We list here the benchmark results on several standard setups while comparing popular 7B models as baselines.
## MT-Bench
MT-Bench is a benchmark made up of 80 high-quality multi-turn questions. These questions fall into eight categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, and Humanities. The chat models are rated using GPT-4 on a scale of 1 to 10, with higher values corresponding to better responses.
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| GPT-4* | 8.95625 | 9.02500 | 8.990625 |
| TenyxChat-7B-v1 | 8.45000 | 7.75625 | 8.103125 |
| Starling-lm-7B-alpha | 8.42500 | 7.68750 | 8.056250 |
| OpenChat-3.5 | 8.18125 | 7.41250 | 7.796875 |
| GPT-3.5-turbo* | 8.07500 | 7.81250 | 7.943750 |
| OpenLLM Leader-7B** | 8.05000 | 7.61250 | 7.831250 |
*values reported on [lmsys](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) ChatBot Arena
**The [OpenLLM Leader](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) as of Jan 5, 2024 is the merge model available as [samir-fama/SamirGPT-v1](https://huggingface.co/samir-fama/SamirGPT-v1)
### Comparison with additional Open LLM LeaderBoard models
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| TenyxChat-7B-v1 | 8.45000 | 7.756250 | 8.103125 |
| SamirGPT-v1 | 8.05000 | 7.612500 | 7.831250 |
| FernandoGPT-v1 | 8.08125 | 7.256250 | 7.668750 |
| Go-Bruins-v2 | 8.13750 | 7.150000 | 7.643750 |
| mistral_tv-neural-marconroni | 7.76875 | 6.987500 | 7.378125 |
| neuronovo-7B-v0.2 | 7.73750 | 6.662500 | 7.200000 |
| neural-chat-7b-v3-3 | 7.39375 | 5.881250 | 6.637500 |
## LM Evaluation - Open LLM Leaderboard
We assess models on 7 benchmarks using the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). This setup is based of that used for [Open LLM Leaderboard.](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
- [AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) (25-shot) - grade-school science questions.
- [HellaSwag](https://arxiv.org/abs/1905.07830) (10-shot) - commonsense inference test.
- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - multitask accuracy test covering 57 tasks.
- [TruthfulQA](https://arxiv.org/abs/2109.07958) (0-shot) - test measuring model's propensity to reproduce online falsehoods.
- [Winogrande](https://arxiv.org/abs/1907.10641) (5-shot) - Winograd benchmark for commonsense reasoning.
- [GSM8k](https://arxiv.org/abs/2110.14168) (5-shot) - grade school math word problems test.
These benchmarks test reasoning and knowledge in various tasks in few-shot settings (higher scores are better).
| Model | MMLU | Winogrande | GSM8k | ARC | HellaSwag | TruthfulQA | Average |
| --- | --- | --- | --- | --- | --- | --- | --- |
| TenyxChat-7B-v1 | 63.6 | 72.3 | 69.0 | 62.7 | 66.6 | 46.7 | 63.48 |
| Starling-7B-alpha | 63.5 | 72.1 | 67.9 | 61.1 | 66.1 | 42.1 | 62.13 |
| OpenChat-3.5 | 63.6 | 72.1 | 68.2 | 61.3 | 65.2 | 41.8 | 62.03 |
| Mistral-7B | 62.4 | 74.0 | 38.1 | 57.2 | 62.8 | 37.8 | 55.38 |
| OpenLLM Leader-7B | 64.3 | 78.7 | 73.3 | 66.6 | 68.4 | 58.5 | 68.3 |
**Note:** While the Open LLM Leaderboard indicates that these chat models perform less effectively compared to the leading 7B model, it's important to note that the leading model struggles in the multi-turn chat setting of MT-Bench (as demonstrated in our evaluation [above](#comparison-with-additional-open-llm-leaderboard-models)). In contrast, TenyxChat-7B-v1 demonstrates robustness against common fine-tuning challenges, such as *catastrophic forgetting*. This unique feature enables TenyxChat-7B-v1 to excel not only in chat benchmarks like MT-Bench, but also in a wider range of general reasoning benchmarks on the Open LLM Leaderboard.
# Limitations
TenyxChat-7B-v1, like other small-sized language models, has its own set of limitations. We haven’t fine-tuned the model explicitly to align with **human** safety preferences. Therefore, it is capable of producing undesirable outputs, particularly when adversarially prompted. From our observation, the model still tends to struggle with tasks that involve reasoning and math questions. In some instances, it might generate verbose or extraneous content.
# License
TenyxChat-7B-v1, similar to OpenChat 3.5, is distributed under the Apache License 2.0.
# Citation
If you use TenyxChat-7B for your research, cite us as
```
@misc{tenyxchat2024,
title={TenyxChat: Language Model Alignment using Tenyx Fine-tuning},
author={Tenyx},
year={2024},
}
```
|
amayprro552/jugggi
|
amayprro552
| 2024-01-12T20:12:44Z | 6 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-12T17:50:01Z |
---
license: other
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
Models info :
https://civitai.com/models/46422?modelVersionId=114770
Sample image I made thru huggingface's API:
Original Author's DEMO images :
|
RiverTest/MTG3Unmerged
|
RiverTest
| 2024-01-12T20:10:48Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"region:us"
] | null | 2024-01-12T20:04:02Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: teknium/OpenHermes-2.5-Mistral-7B
model-index:
- name: teknium/OpenHermes-2.5-Mistral-7B
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# teknium/OpenHermes-2.5-Mistral-7B
This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4764
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 220
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6429 | 0.23 | 10 | 1.3400 |
| 1.2684 | 0.47 | 20 | 1.0591 |
| 1.0218 | 0.7 | 30 | 0.8600 |
| 0.8095 | 0.93 | 40 | 0.7113 |
| 0.6887 | 1.16 | 50 | 0.6353 |
| 0.6207 | 1.4 | 60 | 0.5958 |
| 0.5726 | 1.63 | 70 | 0.5717 |
| 0.5639 | 1.86 | 80 | 0.5548 |
| 0.5496 | 2.09 | 90 | 0.5409 |
| 0.5353 | 2.33 | 100 | 0.5297 |
| 0.4968 | 2.56 | 110 | 0.5204 |
| 0.5031 | 2.79 | 120 | 0.5118 |
| 0.4906 | 3.02 | 130 | 0.5053 |
| 0.4671 | 3.26 | 140 | 0.5015 |
| 0.4734 | 3.49 | 150 | 0.4960 |
| 0.4734 | 3.72 | 160 | 0.4890 |
| 0.4522 | 3.95 | 170 | 0.4857 |
| 0.44 | 4.19 | 180 | 0.4824 |
| 0.4376 | 4.42 | 190 | 0.4797 |
| 0.4361 | 4.65 | 200 | 0.4778 |
| 0.4428 | 4.88 | 210 | 0.4769 |
| 0.4591 | 5.12 | 220 | 0.4764 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
DopeorNope/Mark1-revision-10.7B
|
DopeorNope
| 2024-01-12T20:04:56Z | 11 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"ko",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-22T20:25:03Z |
---
language:
- ko
---
# BASE Architecture
# 한국어 모델 빌드중..
현재 모델 빌딩 중입니다.
## vocab 확장 완료
vocab 확장 완료 되었습니다.(Beomi님의 토크나이저를 참고하였습니다.)
## pretraining 시작..
프리트레이닝 시작중입니다.
총 2B 토큰활용
|
TeeZee/Psyfighter2-Orca2-13B-ties-bpw8.0-h8-exl2
|
TeeZee
| 2024-01-12T19:55:00Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"microsoft/Orca-2-13b",
"KoboldAI/LLaMA2-13B-Psyfighter2",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T19:44:29Z |
---
license: other
tags:
- merge
- mergekit
- lazymergekit
- microsoft/Orca-2-13b
- KoboldAI/LLaMA2-13B-Psyfighter2
license_name: microsoft-research-license
---
## **Psyfighter2-Orca2-ties**
[exllamav2](https://github.com/turboderp/exllamav2) quant for [tuantran1632001/Psyfighter2-Orca2-13B-ties](https://huggingface.co/tuantran1632001/Psyfighter2-Orca2-13B-ties)
Runs smoothly on single 3090 in webui with context length set to 4096, ExLlamav2_HF loader
and cache_8bit=True
All comments are greatly appreciated, download, test and if you appreciate my work, consider buying me my fuel:
<a href="https://www.buymeacoffee.com/TeeZee" target="_blank"><img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;" ></a>
|
jysssacc/opt-350m_adalora_lr0.0005_bs10_epoch5_wd0.01
|
jysssacc
| 2024-01-12T19:25:00Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"region:us"
] | null | 2024-01-12T19:23:27Z |
---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/opt-350m
model-index:
- name: opt-350m_adalora_lr0.0005_bs10_epoch5_wd0.01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-350m_adalora_lr0.0005_bs10_epoch5_wd0.01
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4629
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 63 | 4.7285 |
| 4.9399 | 2.0 | 126 | 4.1198 |
| 4.9399 | 3.0 | 189 | 3.5662 |
| 4.0013 | 4.0 | 252 | 3.4764 |
| 3.4355 | 5.0 | 315 | 3.4629 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
hiiamsid/mixtral_54B_instruct_8k
|
hiiamsid
| 2024-01-12T19:24:14Z | 14 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:mistralai/Mixtral-8x7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mixtral-8x7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T17:53:22Z |
---
license: apache-2.0
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
tags:
- generated_from_trainer
model-index:
- name: mixtral_54B_instruct_8k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mixtral_54B_instruct_8k
This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7899
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5125 | 1.0 | 32 | 1.4529 |
| 0.9656 | 2.0 | 64 | 0.9357 |
| 0.7992 | 3.0 | 96 | 0.8070 |
| 0.7865 | 4.0 | 128 | 0.7899 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
mitro99/distilhubert-finetuned-gtzan_batch4_grad16_cosinelr
|
mitro99
| 2024-01-12T19:23:52Z | 145 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-01-12T17:59:31Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.85
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.85
- Loss: 0.7531
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:---------------:|
| 2.2849 | 1.0 | 14 | 0.17 | 2.2588 |
| 2.1931 | 1.99 | 28 | 0.47 | 2.0874 |
| 1.9194 | 2.99 | 42 | 0.58 | 1.8044 |
| 1.6351 | 3.98 | 56 | 0.61 | 1.5806 |
| 1.4473 | 4.98 | 70 | 0.71 | 1.3886 |
| 1.3131 | 5.97 | 84 | 0.7 | 1.2738 |
| 1.2141 | 6.97 | 98 | 0.72 | 1.1616 |
| 1.0657 | 7.96 | 112 | 0.74 | 1.1272 |
| 0.96 | 8.96 | 126 | 0.75 | 1.0251 |
| 0.8387 | 9.96 | 140 | 0.8 | 0.9364 |
| 0.8653 | 10.95 | 154 | 0.79 | 0.8858 |
| 0.7653 | 11.95 | 168 | 0.8 | 0.8233 |
| 0.7329 | 12.94 | 182 | 0.83 | 0.7982 |
| 0.675 | 13.94 | 196 | 0.81 | 0.8189 |
| 0.6174 | 14.93 | 210 | 0.82 | 0.8236 |
| 0.5714 | 16.0 | 225 | 0.82 | 0.7755 |
| 0.598 | 17.0 | 239 | 0.81 | 0.7511 |
| 0.5794 | 17.99 | 253 | 0.84 | 0.7553 |
| 0.589 | 18.99 | 267 | 0.85 | 0.7533 |
| 0.5717 | 19.91 | 280 | 0.85 | 0.7531 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
foobb/opt-6.7b-lora
|
foobb
| 2024-01-12T19:22:34Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:facebook/opt-6.7b",
"base_model:adapter:facebook/opt-6.7b",
"region:us"
] | null | 2024-01-12T19:22:30Z |
---
library_name: peft
base_model: facebook/opt-6.7b
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
jysssacc/mt0-base_fine_lr5e-05_bs1_epoch5_wd0.01
|
jysssacc
| 2024-01-12T19:21:18Z | 89 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:bigscience/mt0-base",
"base_model:finetune:bigscience/mt0-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-12T17:57:38Z |
---
license: apache-2.0
base_model: bigscience/mt0-base
tags:
- generated_from_trainer
model-index:
- name: mt0-base_fine_lr5e-05_bs1_epoch5_wd0.01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt0-base_fine_lr5e-05_bs1_epoch5_wd0.01
This model is a fine-tuned version of [bigscience/mt0-base](https://huggingface.co/bigscience/mt0-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.006 | 1.0 | 8866 | 0.0000 |
| 0.0016 | 2.0 | 17732 | 0.0000 |
| 0.0003 | 3.0 | 26598 | 0.0000 |
| 0.007 | 4.0 | 35464 | 0.0000 |
| 0.0 | 5.0 | 44330 | 0.0000 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
virtsion/nilmformer_final_generic_no_prompt_2epochs
|
virtsion
| 2024-01-12T19:09:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-12T19:09:12Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
rjomega/bert-finetuned-squad
|
rjomega
| 2024-01-12T19:06:45Z | 109 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-12T17:22:31Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
LoneStriker/TenyxChat-7B-v1-6.0bpw-h6-exl2
|
LoneStriker
| 2024-01-12T19:03:20Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"tenyx-fine-tuning",
"dpo",
"tenyxchat",
"conversational",
"en",
"arxiv:2305.18290",
"arxiv:2309.11235",
"arxiv:2306.05685",
"arxiv:1803.05457",
"arxiv:1905.07830",
"arxiv:2009.03300",
"arxiv:2109.07958",
"arxiv:1907.10641",
"arxiv:2110.14168",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T19:01:00Z |
---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- tenyx-fine-tuning
- dpo
- tenyxchat
---
# TenyxChat: Language Model Alignment using Tenyx Fine-tuning
Introducing TenyxChat, a series of ChatGPT-like models trained to function as useful assistants through preference tuning, using Tenyx's recently released advanced fine-tuning technology ([VentureBeat article](https://venturebeat.com/ai/tenyx-aims-to-fix-llms-catastrophic-forgetting-problem/)). Our first chat model in the series, TenyxChat-7B-v1, is trained using the [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) framework on the open-source AI feedback dataset [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
We fine-tune [Openchat-3.5](https://arxiv.org/pdf/2309.11235.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods), [service](https://www.tenyx.com/fine-tuning)), which shows an increase in [MT-Bench](https://arxiv.org/abs/2306.05685), without a drop in performance of the model on other benchmarks. Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-7B-v1 was trained using eight A100s (80GB) for two hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)).
# Model details
- Model type: Fine-tuned 7B model for chat.
- License: Apache 2.0
- Base model: OpenChat 3.5 ([https://huggingface.co/openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5))
- Demo: [spaces/tenyx/TenyxChat-7B-v1](https://huggingface.co/spaces/tenyx/TenyxChat-7B-v1)
## Usage
Our model uses a simple chat template based on OpenChat 3.5. The chat template usage with a Hugging face generation example is shown below.
### Chat Template (Jinja)
```rust
{{ bos_token }}
{% for message in messages %}
{% if message['role'] == 'user' %}
{{ 'User:' + message['content'] + eos_token }}
{% elif message['role'] == 'system' %}
{{ 'System:' + message['content'] + eos_token }}
{% elif message['role'] == 'assistant' %}
{{ 'Assistant:' + message['content'] + eos_token }}
{% endif %}
{% if loop.last and add_generation_prompt %}\n{{ 'Assistant:' }}{% endif %}\n{% endfor %}
```
### Hugging face Example
```python
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="tenyx/TenyxChat-7B-v1", torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate."},
{"role": "user", "content": "Hi. I would like to make a hotel booking."},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=512, do_sample=False)
```
### Output
```
<s> System:You are a friendly chatbot who always responds in the style of a pirate.<|end_of_turn|>
User:Hi. I would like to make a hotel booking.<|end_of_turn|>
Assistant: Ahoy there me hearty! Arr, ye be lookin' fer a place to rest yer weary bones, eh?
Well then, let's set sail on this grand adventure and find ye a swell place to stay!
To begin, tell me the location ye be seekin' and the dates ye be lookin' to set sail.
And don't ye worry, me matey, I'll be sure to find ye a place that'll make ye feel like a king or queen on land!
```
# Performance
At the time of release (Jan 2024), TenyxChat-7B-v1 is the highest-ranked 7B chat model on the MT-Bench evaluation available for download and commercial use. We list here the benchmark results on several standard setups while comparing popular 7B models as baselines.
## MT-Bench
MT-Bench is a benchmark made up of 80 high-quality multi-turn questions. These questions fall into eight categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, and Humanities. The chat models are rated using GPT-4 on a scale of 1 to 10, with higher values corresponding to better responses.
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| GPT-4* | 8.95625 | 9.02500 | 8.990625 |
| TenyxChat-7B-v1 | 8.45000 | 7.75625 | 8.103125 |
| Starling-lm-7B-alpha | 8.42500 | 7.68750 | 8.056250 |
| OpenChat-3.5 | 8.18125 | 7.41250 | 7.796875 |
| GPT-3.5-turbo* | 8.07500 | 7.81250 | 7.943750 |
| OpenLLM Leader-7B** | 8.05000 | 7.61250 | 7.831250 |
*values reported on [lmsys](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) ChatBot Arena
**The [OpenLLM Leader](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) as of Jan 5, 2024 is the merge model available as [samir-fama/SamirGPT-v1](https://huggingface.co/samir-fama/SamirGPT-v1)
### Comparison with additional Open LLM LeaderBoard models
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| TenyxChat-7B-v1 | 8.45000 | 7.756250 | 8.103125 |
| SamirGPT-v1 | 8.05000 | 7.612500 | 7.831250 |
| FernandoGPT-v1 | 8.08125 | 7.256250 | 7.668750 |
| Go-Bruins-v2 | 8.13750 | 7.150000 | 7.643750 |
| mistral_tv-neural-marconroni | 7.76875 | 6.987500 | 7.378125 |
| neuronovo-7B-v0.2 | 7.73750 | 6.662500 | 7.200000 |
| neural-chat-7b-v3-3 | 7.39375 | 5.881250 | 6.637500 |
## LM Evaluation - Open LLM Leaderboard
We assess models on 7 benchmarks using the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). This setup is based of that used for [Open LLM Leaderboard.](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
- [AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) (25-shot) - grade-school science questions.
- [HellaSwag](https://arxiv.org/abs/1905.07830) (10-shot) - commonsense inference test.
- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - multitask accuracy test covering 57 tasks.
- [TruthfulQA](https://arxiv.org/abs/2109.07958) (0-shot) - test measuring model's propensity to reproduce online falsehoods.
- [Winogrande](https://arxiv.org/abs/1907.10641) (5-shot) - Winograd benchmark for commonsense reasoning.
- [GSM8k](https://arxiv.org/abs/2110.14168) (5-shot) - grade school math word problems test.
These benchmarks test reasoning and knowledge in various tasks in few-shot settings (higher scores are better).
| Model | MMLU | Winogrande | GSM8k | ARC | HellaSwag | TruthfulQA | Average |
| --- | --- | --- | --- | --- | --- | --- | --- |
| TenyxChat-7B-v1 | 63.6 | 72.3 | 69.0 | 62.7 | 66.6 | 46.7 | 63.48 |
| Starling-7B-alpha | 63.5 | 72.1 | 67.9 | 61.1 | 66.1 | 42.1 | 62.13 |
| OpenChat-3.5 | 63.6 | 72.1 | 68.2 | 61.3 | 65.2 | 41.8 | 62.03 |
| Mistral-7B | 62.4 | 74.0 | 38.1 | 57.2 | 62.8 | 37.8 | 55.38 |
| OpenLLM Leader-7B | 64.3 | 78.7 | 73.3 | 66.6 | 68.4 | 58.5 | 68.3 |
**Note:** While the Open LLM Leaderboard indicates that these chat models perform less effectively compared to the leading 7B model, it's important to note that the leading model struggles in the multi-turn chat setting of MT-Bench (as demonstrated in our evaluation [above](#comparison-with-additional-open-llm-leaderboard-models)). In contrast, TenyxChat-7B-v1 demonstrates robustness against common fine-tuning challenges, such as *catastrophic forgetting*. This unique feature enables TenyxChat-7B-v1 to excel not only in chat benchmarks like MT-Bench, but also in a wider range of general reasoning benchmarks on the Open LLM Leaderboard.
# Limitations
TenyxChat-7B-v1, like other small-sized language models, has its own set of limitations. We haven’t fine-tuned the model explicitly to align with **human** safety preferences. Therefore, it is capable of producing undesirable outputs, particularly when adversarially prompted. From our observation, the model still tends to struggle with tasks that involve reasoning and math questions. In some instances, it might generate verbose or extraneous content.
# License
TenyxChat-7B-v1, similar to OpenChat 3.5, is distributed under the Apache License 2.0.
# Citation
If you use TenyxChat-7B for your research, cite us as
```
@misc{tenyxchat2024,
title={TenyxChat: Language Model Alignment using Tenyx Fine-tuning},
author={Tenyx},
year={2024},
}
```
|
LoneStriker/TenyxChat-7B-v1-5.0bpw-h6-exl2
|
LoneStriker
| 2024-01-12T18:57:36Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"tenyx-fine-tuning",
"dpo",
"tenyxchat",
"conversational",
"en",
"arxiv:2305.18290",
"arxiv:2309.11235",
"arxiv:2306.05685",
"arxiv:1803.05457",
"arxiv:1905.07830",
"arxiv:2009.03300",
"arxiv:2109.07958",
"arxiv:1907.10641",
"arxiv:2110.14168",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T18:55:37Z |
---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- tenyx-fine-tuning
- dpo
- tenyxchat
---
# TenyxChat: Language Model Alignment using Tenyx Fine-tuning
Introducing TenyxChat, a series of ChatGPT-like models trained to function as useful assistants through preference tuning, using Tenyx's recently released advanced fine-tuning technology ([VentureBeat article](https://venturebeat.com/ai/tenyx-aims-to-fix-llms-catastrophic-forgetting-problem/)). Our first chat model in the series, TenyxChat-7B-v1, is trained using the [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) framework on the open-source AI feedback dataset [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
We fine-tune [Openchat-3.5](https://arxiv.org/pdf/2309.11235.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods), [service](https://www.tenyx.com/fine-tuning)), which shows an increase in [MT-Bench](https://arxiv.org/abs/2306.05685), without a drop in performance of the model on other benchmarks. Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-7B-v1 was trained using eight A100s (80GB) for two hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)).
# Model details
- Model type: Fine-tuned 7B model for chat.
- License: Apache 2.0
- Base model: OpenChat 3.5 ([https://huggingface.co/openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5))
- Demo: [spaces/tenyx/TenyxChat-7B-v1](https://huggingface.co/spaces/tenyx/TenyxChat-7B-v1)
## Usage
Our model uses a simple chat template based on OpenChat 3.5. The chat template usage with a Hugging face generation example is shown below.
### Chat Template (Jinja)
```rust
{{ bos_token }}
{% for message in messages %}
{% if message['role'] == 'user' %}
{{ 'User:' + message['content'] + eos_token }}
{% elif message['role'] == 'system' %}
{{ 'System:' + message['content'] + eos_token }}
{% elif message['role'] == 'assistant' %}
{{ 'Assistant:' + message['content'] + eos_token }}
{% endif %}
{% if loop.last and add_generation_prompt %}\n{{ 'Assistant:' }}{% endif %}\n{% endfor %}
```
### Hugging face Example
```python
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="tenyx/TenyxChat-7B-v1", torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate."},
{"role": "user", "content": "Hi. I would like to make a hotel booking."},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=512, do_sample=False)
```
### Output
```
<s> System:You are a friendly chatbot who always responds in the style of a pirate.<|end_of_turn|>
User:Hi. I would like to make a hotel booking.<|end_of_turn|>
Assistant: Ahoy there me hearty! Arr, ye be lookin' fer a place to rest yer weary bones, eh?
Well then, let's set sail on this grand adventure and find ye a swell place to stay!
To begin, tell me the location ye be seekin' and the dates ye be lookin' to set sail.
And don't ye worry, me matey, I'll be sure to find ye a place that'll make ye feel like a king or queen on land!
```
# Performance
At the time of release (Jan 2024), TenyxChat-7B-v1 is the highest-ranked 7B chat model on the MT-Bench evaluation available for download and commercial use. We list here the benchmark results on several standard setups while comparing popular 7B models as baselines.
## MT-Bench
MT-Bench is a benchmark made up of 80 high-quality multi-turn questions. These questions fall into eight categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, and Humanities. The chat models are rated using GPT-4 on a scale of 1 to 10, with higher values corresponding to better responses.
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| GPT-4* | 8.95625 | 9.02500 | 8.990625 |
| TenyxChat-7B-v1 | 8.45000 | 7.75625 | 8.103125 |
| Starling-lm-7B-alpha | 8.42500 | 7.68750 | 8.056250 |
| OpenChat-3.5 | 8.18125 | 7.41250 | 7.796875 |
| GPT-3.5-turbo* | 8.07500 | 7.81250 | 7.943750 |
| OpenLLM Leader-7B** | 8.05000 | 7.61250 | 7.831250 |
*values reported on [lmsys](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) ChatBot Arena
**The [OpenLLM Leader](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) as of Jan 5, 2024 is the merge model available as [samir-fama/SamirGPT-v1](https://huggingface.co/samir-fama/SamirGPT-v1)
### Comparison with additional Open LLM LeaderBoard models
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| TenyxChat-7B-v1 | 8.45000 | 7.756250 | 8.103125 |
| SamirGPT-v1 | 8.05000 | 7.612500 | 7.831250 |
| FernandoGPT-v1 | 8.08125 | 7.256250 | 7.668750 |
| Go-Bruins-v2 | 8.13750 | 7.150000 | 7.643750 |
| mistral_tv-neural-marconroni | 7.76875 | 6.987500 | 7.378125 |
| neuronovo-7B-v0.2 | 7.73750 | 6.662500 | 7.200000 |
| neural-chat-7b-v3-3 | 7.39375 | 5.881250 | 6.637500 |
## LM Evaluation - Open LLM Leaderboard
We assess models on 7 benchmarks using the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). This setup is based of that used for [Open LLM Leaderboard.](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
- [AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) (25-shot) - grade-school science questions.
- [HellaSwag](https://arxiv.org/abs/1905.07830) (10-shot) - commonsense inference test.
- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - multitask accuracy test covering 57 tasks.
- [TruthfulQA](https://arxiv.org/abs/2109.07958) (0-shot) - test measuring model's propensity to reproduce online falsehoods.
- [Winogrande](https://arxiv.org/abs/1907.10641) (5-shot) - Winograd benchmark for commonsense reasoning.
- [GSM8k](https://arxiv.org/abs/2110.14168) (5-shot) - grade school math word problems test.
These benchmarks test reasoning and knowledge in various tasks in few-shot settings (higher scores are better).
| Model | MMLU | Winogrande | GSM8k | ARC | HellaSwag | TruthfulQA | Average |
| --- | --- | --- | --- | --- | --- | --- | --- |
| TenyxChat-7B-v1 | 63.6 | 72.3 | 69.0 | 62.7 | 66.6 | 46.7 | 63.48 |
| Starling-7B-alpha | 63.5 | 72.1 | 67.9 | 61.1 | 66.1 | 42.1 | 62.13 |
| OpenChat-3.5 | 63.6 | 72.1 | 68.2 | 61.3 | 65.2 | 41.8 | 62.03 |
| Mistral-7B | 62.4 | 74.0 | 38.1 | 57.2 | 62.8 | 37.8 | 55.38 |
| OpenLLM Leader-7B | 64.3 | 78.7 | 73.3 | 66.6 | 68.4 | 58.5 | 68.3 |
**Note:** While the Open LLM Leaderboard indicates that these chat models perform less effectively compared to the leading 7B model, it's important to note that the leading model struggles in the multi-turn chat setting of MT-Bench (as demonstrated in our evaluation [above](#comparison-with-additional-open-llm-leaderboard-models)). In contrast, TenyxChat-7B-v1 demonstrates robustness against common fine-tuning challenges, such as *catastrophic forgetting*. This unique feature enables TenyxChat-7B-v1 to excel not only in chat benchmarks like MT-Bench, but also in a wider range of general reasoning benchmarks on the Open LLM Leaderboard.
# Limitations
TenyxChat-7B-v1, like other small-sized language models, has its own set of limitations. We haven’t fine-tuned the model explicitly to align with **human** safety preferences. Therefore, it is capable of producing undesirable outputs, particularly when adversarially prompted. From our observation, the model still tends to struggle with tasks that involve reasoning and math questions. In some instances, it might generate verbose or extraneous content.
# License
TenyxChat-7B-v1, similar to OpenChat 3.5, is distributed under the Apache License 2.0.
# Citation
If you use TenyxChat-7B for your research, cite us as
```
@misc{tenyxchat2024,
title={TenyxChat: Language Model Alignment using Tenyx Fine-tuning},
author={Tenyx},
year={2024},
}
```
|
LoneStriker/TenyxChat-7B-v1-4.0bpw-h6-exl2
|
LoneStriker
| 2024-01-12T18:51:53Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"tenyx-fine-tuning",
"dpo",
"tenyxchat",
"conversational",
"en",
"arxiv:2305.18290",
"arxiv:2309.11235",
"arxiv:2306.05685",
"arxiv:1803.05457",
"arxiv:1905.07830",
"arxiv:2009.03300",
"arxiv:2109.07958",
"arxiv:1907.10641",
"arxiv:2110.14168",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T18:50:14Z |
---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- tenyx-fine-tuning
- dpo
- tenyxchat
---
# TenyxChat: Language Model Alignment using Tenyx Fine-tuning
Introducing TenyxChat, a series of ChatGPT-like models trained to function as useful assistants through preference tuning, using Tenyx's recently released advanced fine-tuning technology ([VentureBeat article](https://venturebeat.com/ai/tenyx-aims-to-fix-llms-catastrophic-forgetting-problem/)). Our first chat model in the series, TenyxChat-7B-v1, is trained using the [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) framework on the open-source AI feedback dataset [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
We fine-tune [Openchat-3.5](https://arxiv.org/pdf/2309.11235.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods), [service](https://www.tenyx.com/fine-tuning)), which shows an increase in [MT-Bench](https://arxiv.org/abs/2306.05685), without a drop in performance of the model on other benchmarks. Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-7B-v1 was trained using eight A100s (80GB) for two hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)).
# Model details
- Model type: Fine-tuned 7B model for chat.
- License: Apache 2.0
- Base model: OpenChat 3.5 ([https://huggingface.co/openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5))
- Demo: [spaces/tenyx/TenyxChat-7B-v1](https://huggingface.co/spaces/tenyx/TenyxChat-7B-v1)
## Usage
Our model uses a simple chat template based on OpenChat 3.5. The chat template usage with a Hugging face generation example is shown below.
### Chat Template (Jinja)
```rust
{{ bos_token }}
{% for message in messages %}
{% if message['role'] == 'user' %}
{{ 'User:' + message['content'] + eos_token }}
{% elif message['role'] == 'system' %}
{{ 'System:' + message['content'] + eos_token }}
{% elif message['role'] == 'assistant' %}
{{ 'Assistant:' + message['content'] + eos_token }}
{% endif %}
{% if loop.last and add_generation_prompt %}\n{{ 'Assistant:' }}{% endif %}\n{% endfor %}
```
### Hugging face Example
```python
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="tenyx/TenyxChat-7B-v1", torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate."},
{"role": "user", "content": "Hi. I would like to make a hotel booking."},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=512, do_sample=False)
```
### Output
```
<s> System:You are a friendly chatbot who always responds in the style of a pirate.<|end_of_turn|>
User:Hi. I would like to make a hotel booking.<|end_of_turn|>
Assistant: Ahoy there me hearty! Arr, ye be lookin' fer a place to rest yer weary bones, eh?
Well then, let's set sail on this grand adventure and find ye a swell place to stay!
To begin, tell me the location ye be seekin' and the dates ye be lookin' to set sail.
And don't ye worry, me matey, I'll be sure to find ye a place that'll make ye feel like a king or queen on land!
```
# Performance
At the time of release (Jan 2024), TenyxChat-7B-v1 is the highest-ranked 7B chat model on the MT-Bench evaluation available for download and commercial use. We list here the benchmark results on several standard setups while comparing popular 7B models as baselines.
## MT-Bench
MT-Bench is a benchmark made up of 80 high-quality multi-turn questions. These questions fall into eight categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, and Humanities. The chat models are rated using GPT-4 on a scale of 1 to 10, with higher values corresponding to better responses.
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| GPT-4* | 8.95625 | 9.02500 | 8.990625 |
| TenyxChat-7B-v1 | 8.45000 | 7.75625 | 8.103125 |
| Starling-lm-7B-alpha | 8.42500 | 7.68750 | 8.056250 |
| OpenChat-3.5 | 8.18125 | 7.41250 | 7.796875 |
| GPT-3.5-turbo* | 8.07500 | 7.81250 | 7.943750 |
| OpenLLM Leader-7B** | 8.05000 | 7.61250 | 7.831250 |
*values reported on [lmsys](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) ChatBot Arena
**The [OpenLLM Leader](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) as of Jan 5, 2024 is the merge model available as [samir-fama/SamirGPT-v1](https://huggingface.co/samir-fama/SamirGPT-v1)
### Comparison with additional Open LLM LeaderBoard models
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| TenyxChat-7B-v1 | 8.45000 | 7.756250 | 8.103125 |
| SamirGPT-v1 | 8.05000 | 7.612500 | 7.831250 |
| FernandoGPT-v1 | 8.08125 | 7.256250 | 7.668750 |
| Go-Bruins-v2 | 8.13750 | 7.150000 | 7.643750 |
| mistral_tv-neural-marconroni | 7.76875 | 6.987500 | 7.378125 |
| neuronovo-7B-v0.2 | 7.73750 | 6.662500 | 7.200000 |
| neural-chat-7b-v3-3 | 7.39375 | 5.881250 | 6.637500 |
## LM Evaluation - Open LLM Leaderboard
We assess models on 7 benchmarks using the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). This setup is based of that used for [Open LLM Leaderboard.](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
- [AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) (25-shot) - grade-school science questions.
- [HellaSwag](https://arxiv.org/abs/1905.07830) (10-shot) - commonsense inference test.
- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - multitask accuracy test covering 57 tasks.
- [TruthfulQA](https://arxiv.org/abs/2109.07958) (0-shot) - test measuring model's propensity to reproduce online falsehoods.
- [Winogrande](https://arxiv.org/abs/1907.10641) (5-shot) - Winograd benchmark for commonsense reasoning.
- [GSM8k](https://arxiv.org/abs/2110.14168) (5-shot) - grade school math word problems test.
These benchmarks test reasoning and knowledge in various tasks in few-shot settings (higher scores are better).
| Model | MMLU | Winogrande | GSM8k | ARC | HellaSwag | TruthfulQA | Average |
| --- | --- | --- | --- | --- | --- | --- | --- |
| TenyxChat-7B-v1 | 63.6 | 72.3 | 69.0 | 62.7 | 66.6 | 46.7 | 63.48 |
| Starling-7B-alpha | 63.5 | 72.1 | 67.9 | 61.1 | 66.1 | 42.1 | 62.13 |
| OpenChat-3.5 | 63.6 | 72.1 | 68.2 | 61.3 | 65.2 | 41.8 | 62.03 |
| Mistral-7B | 62.4 | 74.0 | 38.1 | 57.2 | 62.8 | 37.8 | 55.38 |
| OpenLLM Leader-7B | 64.3 | 78.7 | 73.3 | 66.6 | 68.4 | 58.5 | 68.3 |
**Note:** While the Open LLM Leaderboard indicates that these chat models perform less effectively compared to the leading 7B model, it's important to note that the leading model struggles in the multi-turn chat setting of MT-Bench (as demonstrated in our evaluation [above](#comparison-with-additional-open-llm-leaderboard-models)). In contrast, TenyxChat-7B-v1 demonstrates robustness against common fine-tuning challenges, such as *catastrophic forgetting*. This unique feature enables TenyxChat-7B-v1 to excel not only in chat benchmarks like MT-Bench, but also in a wider range of general reasoning benchmarks on the Open LLM Leaderboard.
# Limitations
TenyxChat-7B-v1, like other small-sized language models, has its own set of limitations. We haven’t fine-tuned the model explicitly to align with **human** safety preferences. Therefore, it is capable of producing undesirable outputs, particularly when adversarially prompted. From our observation, the model still tends to struggle with tasks that involve reasoning and math questions. In some instances, it might generate verbose or extraneous content.
# License
TenyxChat-7B-v1, similar to OpenChat 3.5, is distributed under the Apache License 2.0.
# Citation
If you use TenyxChat-7B for your research, cite us as
```
@misc{tenyxchat2024,
title={TenyxChat: Language Model Alignment using Tenyx Fine-tuning},
author={Tenyx},
year={2024},
}
```
|
virtsion/nilmformer_final_generic_prompt
|
virtsion
| 2024-01-12T18:48:31Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-12T18:48:29Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
ostapeno/indepexp_adauniNeo1B_ultrachat_25_sub05_3ep
|
ostapeno
| 2024-01-12T18:48:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-12T15:41:07Z |
Number of experts present in the library: 3
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| ultrachat_25_v1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_25 | lora |
| ultrachat_25 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_25 | lora |
| ultrachat_25_v2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/ultrachat_25 | lora |
Last updated on: 2024-01-12 18:48:26+00:00
|
MaziyarPanahi/neural-chat-11b-v3-2-Mistral-7B-Instruct-v0.2-slerp
|
MaziyarPanahi
| 2024-01-12T18:25:59Z | 19 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"NurtureAI/neural-chat-11b-v3-2",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T18:20:56Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- NurtureAI/neural-chat-11b-v3-2
---
# neural-chat-11b-v3-2-Mistral-7B-Instruct-v0.2-slerp
neural-chat-11b-v3-2-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [NurtureAI/neural-chat-11b-v3-2](https://huggingface.co/NurtureAI/neural-chat-11b-v3-2)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: NurtureAI/neural-chat-11b-v3-2
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/neural-chat-11b-v3-2-Mistral-7B-Instruct-v0.2-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
Perselope/Taxi-v3_v5
|
Perselope
| 2024-01-12T18:19:32Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-12T18:19:27Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3_v5
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.96 +/- 2.30
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
model = load_from_hub(repo_id="Perselope/Taxi-v3_v5", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
|
tenyx/TenyxChat-7B-v1
|
tenyx
| 2024-01-12T18:16:23Z | 1,400 | 25 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"tenyx-fine-tuning",
"dpo",
"tenyxchat",
"conversational",
"en",
"arxiv:2305.18290",
"arxiv:2309.11235",
"arxiv:2306.05685",
"arxiv:1803.05457",
"arxiv:1905.07830",
"arxiv:2009.03300",
"arxiv:2109.07958",
"arxiv:1907.10641",
"arxiv:2110.14168",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-05T23:18:34Z |
---
license: apache-2.0
language:
- en
library_name: transformers
tags:
- tenyx-fine-tuning
- dpo
- tenyxchat
---
# TenyxChat: Language Model Alignment using Tenyx Fine-tuning
Introducing TenyxChat, a series of ChatGPT-like models trained to function as useful assistants through preference tuning, using Tenyx's recently released advanced fine-tuning technology ([VentureBeat article](https://venturebeat.com/ai/tenyx-aims-to-fix-llms-catastrophic-forgetting-problem/)). Our first chat model in the series, TenyxChat-7B-v1, is trained using the [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290) framework on the open-source AI feedback dataset [UltraFeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).
We fine-tune [Openchat-3.5](https://arxiv.org/pdf/2309.11235.pdf) with our proprietary approach ([blog](https://www.tenyx.com/post/forgetting-and-toxicity-in-llms-a-deep-dive-on-fine-tuning-methods), [service](https://www.tenyx.com/fine-tuning)), which shows an increase in [MT-Bench](https://arxiv.org/abs/2306.05685), without a drop in performance of the model on other benchmarks. Our approach aims to mitigate forgetting in LLMs in a computationally efficient manner, thereby enabling continual fine-tuning capabilities without altering the pre-trained output distribution. TenyxChat-7B-v1 was trained using eight A100s (80GB) for two hours, with a training setup obtained from HuggingFaceH4 ([GitHub](https://github.com/huggingface/alignment-handbook)).
# Model details
- Model type: Fine-tuned 7B model for chat.
- License: Apache 2.0
- Base model: OpenChat 3.5 ([https://huggingface.co/openchat/openchat_3.5](https://huggingface.co/openchat/openchat_3.5))
- Demo: [spaces/tenyx/TenyxChat-7B-v1](https://huggingface.co/spaces/tenyx/TenyxChat-7B-v1)
## Usage
Our model uses a simple chat template based on OpenChat 3.5. The chat template usage with a Hugging face generation example is shown below.
### Chat Template (Jinja)
```rust
{{ bos_token }}
{% for message in messages %}
{% if message['role'] == 'user' %}
{{ 'User:' + message['content'] + eos_token }}
{% elif message['role'] == 'system' %}
{{ 'System:' + message['content'] + eos_token }}
{% elif message['role'] == 'assistant' %}
{{ 'Assistant:' + message['content'] + eos_token }}
{% endif %}
{% if loop.last and add_generation_prompt %}\n{{ 'Assistant:' }}{% endif %}\n{% endfor %}
```
### Hugging face Example
```python
import torch
from transformers import pipeline
pipe = pipeline("text-generation", model="tenyx/TenyxChat-7B-v1", torch_dtype=torch.bfloat16, device_map="auto")
messages = [
{"role": "system", "content": "You are a friendly chatbot who always responds in the style of a pirate."},
{"role": "user", "content": "Hi. I would like to make a hotel booking."},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=512, do_sample=False)
```
### Output
```
<s> System:You are a friendly chatbot who always responds in the style of a pirate.<|end_of_turn|>
User:Hi. I would like to make a hotel booking.<|end_of_turn|>
Assistant: Ahoy there me hearty! Arr, ye be lookin' fer a place to rest yer weary bones, eh?
Well then, let's set sail on this grand adventure and find ye a swell place to stay!
To begin, tell me the location ye be seekin' and the dates ye be lookin' to set sail.
And don't ye worry, me matey, I'll be sure to find ye a place that'll make ye feel like a king or queen on land!
```
# Performance
At the time of release (Jan 2024), TenyxChat-7B-v1 is the highest-ranked 7B chat model on the MT-Bench evaluation available for download and commercial use. We list here the benchmark results on several standard setups while comparing popular 7B models as baselines.
## MT-Bench
MT-Bench is a benchmark made up of 80 high-quality multi-turn questions. These questions fall into eight categories: Writing, Roleplay, Reasoning, Math, Coding, Extraction, STEM, and Humanities. The chat models are rated using GPT-4 on a scale of 1 to 10, with higher values corresponding to better responses.
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| GPT-4* | 8.95625 | 9.02500 | 8.990625 |
| TenyxChat-7B-v1 | 8.45000 | 7.75625 | 8.103125 |
| Starling-lm-7B-alpha | 8.42500 | 7.68750 | 8.056250 |
| OpenChat-3.5 | 8.18125 | 7.41250 | 7.796875 |
| GPT-3.5-turbo* | 8.07500 | 7.81250 | 7.943750 |
| OpenLLM Leader-7B** | 8.05000 | 7.61250 | 7.831250 |
*values reported on [lmsys](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) ChatBot Arena
**The [OpenLLM Leader](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) as of Jan 5, 2024 is the merge model available as [samir-fama/SamirGPT-v1](https://huggingface.co/samir-fama/SamirGPT-v1)
### Comparison with additional Open LLM LeaderBoard models
| Model | First Turn | Second Turn | Average |
| --- | --- | --- | --- |
| TenyxChat-7B-v1 | 8.45000 | 7.756250 | 8.103125 |
| SamirGPT-v1 | 8.05000 | 7.612500 | 7.831250 |
| FernandoGPT-v1 | 8.08125 | 7.256250 | 7.668750 |
| Go-Bruins-v2 | 8.13750 | 7.150000 | 7.643750 |
| mistral_tv-neural-marconroni | 7.76875 | 6.987500 | 7.378125 |
| neuronovo-7B-v0.2 | 7.73750 | 6.662500 | 7.200000 |
| neural-chat-7b-v3-3 | 7.39375 | 5.881250 | 6.637500 |
## LM Evaluation - Open LLM Leaderboard
We assess models on 7 benchmarks using the [Eleuther AI Language Model Evaluation Harness](https://github.com/EleutherAI/lm-evaluation-harness). This setup is based of that used for [Open LLM Leaderboard.](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
- [AI2 Reasoning Challenge](https://arxiv.org/abs/1803.05457) (25-shot) - grade-school science questions.
- [HellaSwag](https://arxiv.org/abs/1905.07830) (10-shot) - commonsense inference test.
- [MMLU](https://arxiv.org/abs/2009.03300) (5-shot) - multitask accuracy test covering 57 tasks.
- [TruthfulQA](https://arxiv.org/abs/2109.07958) (0-shot) - test measuring model's propensity to reproduce online falsehoods.
- [Winogrande](https://arxiv.org/abs/1907.10641) (5-shot) - Winograd benchmark for commonsense reasoning.
- [GSM8k](https://arxiv.org/abs/2110.14168) (5-shot) - grade school math word problems test.
These benchmarks test reasoning and knowledge in various tasks in few-shot settings (higher scores are better).
| Model | MMLU | Winogrande | GSM8k | ARC | HellaSwag | TruthfulQA | Average |
| --- | --- | --- | --- | --- | --- | --- | --- |
| TenyxChat-7B-v1 | 63.6 | 72.3 | 69.0 | 62.7 | 66.6 | 46.7 | 63.48 |
| Starling-7B-alpha | 63.5 | 72.1 | 67.9 | 61.1 | 66.1 | 42.1 | 62.13 |
| OpenChat-3.5 | 63.6 | 72.1 | 68.2 | 61.3 | 65.2 | 41.8 | 62.03 |
| Mistral-7B | 62.4 | 74.0 | 38.1 | 57.2 | 62.8 | 37.8 | 55.38 |
| OpenLLM Leader-7B | 64.3 | 78.7 | 73.3 | 66.6 | 68.4 | 58.5 | 68.3 |
**Note:** While the Open LLM Leaderboard indicates that these chat models perform less effectively compared to the leading 7B model, it's important to note that the leading model struggles in the multi-turn chat setting of MT-Bench (as demonstrated in our evaluation [above](#comparison-with-additional-open-llm-leaderboard-models)). In contrast, TenyxChat-7B-v1 demonstrates robustness against common fine-tuning challenges, such as *catastrophic forgetting*. This unique feature enables TenyxChat-7B-v1 to excel not only in chat benchmarks like MT-Bench, but also in a wider range of general reasoning benchmarks on the Open LLM Leaderboard.
# Limitations
TenyxChat-7B-v1, like other small-sized language models, has its own set of limitations. We haven’t fine-tuned the model explicitly to align with **human** safety preferences. Therefore, it is capable of producing undesirable outputs, particularly when adversarially prompted. From our observation, the model still tends to struggle with tasks that involve reasoning and math questions. In some instances, it might generate verbose or extraneous content.
# License
TenyxChat-7B-v1, similar to OpenChat 3.5, is distributed under the Apache License 2.0.
# Citation
If you use TenyxChat-7B for your research, cite us as
```
@misc{tenyxchat2024,
title={TenyxChat: Language Model Alignment using Tenyx Fine-tuning},
author={Tenyx},
year={2024},
}
```
|
jysssacc/opt-350m_IA3_lr0.0005_bs10_epoch5_wd0.01
|
jysssacc
| 2024-01-12T18:02:13Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:adapter:facebook/opt-350m",
"license:other",
"region:us"
] | null | 2024-01-12T18:01:43Z |
---
license: other
library_name: peft
tags:
- generated_from_trainer
base_model: facebook/opt-350m
model-index:
- name: opt-350m_IA3_lr0.0005_bs10_epoch5_wd0.01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-350m_IA3_lr0.0005_bs10_epoch5_wd0.01
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6376
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 63 | 3.8293 |
| 4.0255 | 2.0 | 126 | 3.7927 |
| 4.0255 | 3.0 | 189 | 3.7419 |
| 3.9543 | 4.0 | 252 | 3.6876 |
| 3.8541 | 5.0 | 315 | 3.6376 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
narayan214/ppo-LunarLander-v2
|
narayan214
| 2024-01-12T17:59:27Z | 0 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-12T17:59:11Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 283.41 +/- 19.19
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
coversia21/RVC_Haroo
|
coversia21
| 2024-01-12T17:57:08Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:h94/IP-Adapter-FaceID",
"base_model:adapter:h94/IP-Adapter-FaceID",
"license:openrail",
"region:us"
] |
text-to-image
| 2024-01-12T17:44:10Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/descarga (2).jpeg
base_model: h94/IP-Adapter-FaceID
instance_prompt: null
license: openrail
---
# RVC_Haroo
<Gallery />
## Download model
[Download](/coversia21/RVC_Haroo/tree/main) them in the Files & versions tab.
|
imagepipeline/NightVision-XL
|
imagepipeline
| 2024-01-12T17:56:25Z | 47 | 1 |
diffusers
|
[
"diffusers",
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-01-12T17:52:31Z |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## NightVision-XL
<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1302f16e-bd67-42b5-bff7-6a412980527f/width=450/01793-2023-11-30-3450031999.jpeg,https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/de72fd88-b408-493d-b3db-41ee104b56ec/width=450/01888-2023-11-30-1075883091.jpeg" alt="Generated by Image Pipeline" style="border-radius: 10px;">
**This checkpoint model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - This is NightVision XL, a lightly trained base SDXL model that is then further refined with community LORAs to get it to where it is now. NightVision XL has been refined and biased to produce touched-up photorealistic portrait output that is ready-stylized for Social media posting! NightVision XL has nice coherency and is avoiding some of the weird body issues and biases that are starting to plague some of the other photorealistic models. Further, NightVision XL produces rich deep blacks and great evening/night time scenes. It can also produce ridiculously bright output as well! Use it without refiner.
[](https://imagepipeline.io/models/NightVision-XL?id=a0a4bfa3-2879-4be2-8e98-3d71e041432d/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sdxl/text2image/v1/run"
payload = json.dumps({
"model_id": "a0a4bfa3-2879-4be2-8e98-3d71e041432d",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "",
"lora_weights": ""
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sdxl/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at hello@imagepipeline.io
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
MaziyarPanahi/juanako-7b-UNA-Mistral-7B-Instruct-v0.2-slerp
|
MaziyarPanahi
| 2024-01-12T17:51:52Z | 20 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"fblgit/juanako-7b-UNA",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T17:46:57Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- fblgit/juanako-7b-UNA
---
# juanako-7b-UNA-Mistral-7B-Instruct-v0.2-slerp
juanako-7b-UNA-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [fblgit/juanako-7b-UNA](https://huggingface.co/fblgit/juanako-7b-UNA)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: fblgit/juanako-7b-UNA
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/juanako-7b-UNA-Mistral-7B-Instruct-v0.2-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
Polu/lunarlander
|
Polu
| 2024-01-12T17:49:37Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-12T17:22:01Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 246.54 +/- 16.75
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
imagepipeline/DevlishPhotoRealism-SDXL
|
imagepipeline
| 2024-01-12T17:49:25Z | 43 | 0 |
diffusers
|
[
"diffusers",
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-01-12T17:45:12Z |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## DevlishPhotoRealism-SDXL
<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/d6303f3a-9835-4ba6-a8ce-50d41627d5e6/width=450/00013-584840008.jpeg" alt="Generated by Image Pipeline" style="border-radius: 10px;">
**This checkpoint model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - merged it on base of the default SD-XL model with several different models. (Around 40 merges).SD-XL VAE is embedded.
[](https://imagepipeline.io/models/DevlishPhotoRealism-SDXL?id=182e67fb-8b74-44b3-800d-af840b2a3550/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sdxl/text2image/v1/run"
payload = json.dumps({
"model_id": "182e67fb-8b74-44b3-800d-af840b2a3550",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "",
"lora_weights": ""
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sdxl/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at hello@imagepipeline.io
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
LoneStriker/WhiteRabbitNeo-33B-v1-6.0bpw-h6-exl2
|
LoneStriker
| 2024-01-12T17:45:38Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T17:35:08Z |
---
license: other
license_name: deepseek
license_link: https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE
---
# Our 33B-v1.1 model is now live (We'll always be serving the newest model on our web app)!
33B-v1.1 model comes with a "Prompt Enhancement" feature. Access at: https://www.whiterabbitneo.com/
# Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version
# Licence: Usage Restrictions
```
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
- For military use in any way;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
```
# Topics Covered:
```
- Open Ports: Identifying open ports is crucial as they can be entry points for attackers. Common ports to check include HTTP (80, 443), FTP (21), SSH (22), and SMB (445).
- Outdated Software or Services: Systems running outdated software or services are often vulnerable to exploits. This includes web servers, database servers, and any third-party software.
- Default Credentials: Many systems and services are installed with default usernames and passwords, which are well-known and can be easily exploited.
- Misconfigurations: Incorrectly configured services, permissions, and security settings can introduce vulnerabilities.
- Injection Flaws: SQL injection, command injection, and cross-site scripting (XSS) are common issues in web applications.
- Unencrypted Services: Services that do not use encryption (like HTTP instead of HTTPS) can expose sensitive data.
- Known Software Vulnerabilities: Checking for known vulnerabilities in software using databases like the National Vulnerability Database (NVD) or tools like Nessus or OpenVAS.
- Cross-Site Request Forgery (CSRF): This is where unauthorized commands are transmitted from a user that the web application trusts.
- Insecure Direct Object References: This occurs when an application provides direct access to objects based on user-supplied input.
- Security Misconfigurations in Web Servers/Applications: This includes issues like insecure HTTP headers or verbose error messages that reveal too much information.
- Broken Authentication and Session Management: This can allow attackers to compromise passwords, keys, or session tokens, or to exploit other implementation flaws to assume other users' identities.
- Sensitive Data Exposure: Includes vulnerabilities that expose sensitive data, such as credit card numbers, health records, or personal information.
- API Vulnerabilities: In modern web applications, APIs are often used and can have vulnerabilities like insecure endpoints or data leakage.
- Denial of Service (DoS) Vulnerabilities: Identifying services that are vulnerable to DoS attacks, which can make the resource unavailable to legitimate users.
- Buffer Overflows: Common in older software, these vulnerabilities can allow an attacker to crash the system or execute arbitrary code.
```
# WhiteRabbitNeo
<br>
<br>
WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity.
Our 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI.
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "whiterabbitneo/WhiteRabbitNeo-33B-v-1"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
tot_system_prompt = """
Answer the Question by exploring multiple reasoning paths as follows:
- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.
- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.
- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option. Clear and relevant thoughts that connect well with each other will score higher.
- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.
- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.
- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.
- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.
- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.
In summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers.
"""
conversation = f"SYSTEM: {tot_system_prompt} Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
# Sample Conversations:
1. "Write me a Fast API server with one end-point. The endpoint returns files from a S3 bucket.": https://www.whiterabbitneo.com/share/y06Po0e
2. "How can Metasploit be used for exploiting Android based IoT devices? What are some of the IoT devices that run Android? Show an example with code": https://www.whiterabbitneo.com/share/gWBwKlz
3. "How do I attack a wifi network?": https://www.whiterabbitneo.com/share/WLovxcu
4. "How do I create a reverse shell in Python": https://www.whiterabbitneo.com/share/LERgm8w
5. "How do we use Scapy for vulnerability assessment?": https://www.whiterabbitneo.com/share/t73iMzv
|
wenqiglantz/LlamaGuard-7b-GGUF
|
wenqiglantz
| 2024-01-12T17:28:26Z | 15 | 1 |
transformers
|
[
"transformers",
"gguf",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-2",
"en",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2024-01-02T14:18:07Z |
---
language:
- en
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
license: llama2
---
# Model Card for LlamaGuard-7b-GGUF
This is a quantized model for `meta-llama/LlamaGuard-7b`. Two quantization methods were used:
- Q5_K_M: 5-bit, preserves most of the model's performance
- Q4_K_M: 4-bit, smaller footprints and saves more memory
## Model Details
### Model Description
Refer to details from [Meta's official model card]: (https://huggingface.co/meta-llama/LlamaGuard-7b).
|
ostapeno/indepexp_adauniNeo1B_wiqa_what_is_the_final_step_of_the_following_process_sub05_3ep
|
ostapeno
| 2024-01-12T17:22:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-12T15:56:57Z |
Number of experts present in the library: 3
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| wiqa_what_is_the_final_step_of_the_following_process | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| wiqa_what_is_the_final_step_of_the_following_process_v1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
| wiqa_what_is_the_final_step_of_the_following_process_v2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/wiqa_what_is_the_final_step_of_the_following_process | lora |
Last updated on: 2024-01-12 17:22:53+00:00
|
s3nh/diffnamehard-Psyfighter2-Noromaid-ties-13B-GGUF
|
s3nh
| 2024-01-12T17:18:08Z | 10 | 1 |
transformers
|
[
"transformers",
"gguf",
"text-generation",
"zh",
"en",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T15:36:31Z |
---
license: openrail
pipeline_tag: text-generation
library_name: transformers
language:
- zh
- en
---
## Original model card
Buy me a coffee if you like this project ;)
<a href="https://www.buymeacoffee.com/s3nh"><img src="https://www.buymeacoffee.com/assets/img/guidelines/download-assets-sm-1.svg" alt=""></a>
#### Description
GGUF Format model files for [This project](https://huggingface.co/diffnamehard/Psyfighter2-Noromaid-ties-13B).
### GGUF Specs
GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:
Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information.
Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models.
mmap compatibility: models can be loaded using mmap for fast loading and saving.
Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used.
Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user.
The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values.
This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for
inference or for identifying the model.
### Perplexity params
Model Measure Q2_K Q3_K_S Q3_K_M Q3_K_L Q4_0 Q4_1 Q4_K_S Q4_K_M Q5_0 Q5_1 Q5_K_S Q5_K_M Q6_K Q8_0 F16
7B perplexity 6.7764 6.4571 6.1503 6.0869 6.1565 6.0912 6.0215 5.9601 5.9862 5.9481 5.9419 5.9208 5.9110 5.9070 5.9066
13B perplexity 5.8545 5.6033 5.4498 5.4063 5.3860 5.3608 5.3404 5.3002 5.2856 5.2706 5.2785 5.2638 5.2568 5.2548 5.2543
### inference
TODO
# Original model card
|
version-control/tf-1.0-1.13-codegen-2B-multi-prefix
|
version-control
| 2024-01-12T17:11:26Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Salesforce/codegen-2B-multi",
"base_model:adapter:Salesforce/codegen-2B-multi",
"region:us"
] | null | 2024-01-12T16:44:34Z |
---
library_name: peft
base_model: Salesforce/codegen-2B-multi
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
Coelhomatias/vit-cxr4
|
Coelhomatias
| 2024-01-12T17:10:05Z | 22 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224",
"base_model:finetune:google/vit-base-patch16-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-10T18:39:28Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: vit-cxr4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-cxr4
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3774
- Precision: 0.8587
- Recall: 0.9317
- F1: 0.8937
- Accuracy: 0.8924
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 96
- eval_batch_size: 64
- seed: 17
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.3151 | 0.31 | 100 | 0.3317 | 0.8152 | 0.9143 | 0.8619 | 0.8552 |
| 0.319 | 0.63 | 200 | 0.3048 | 0.8670 | 0.8514 | 0.8591 | 0.8620 |
| 0.2926 | 0.94 | 300 | 0.2867 | 0.8580 | 0.8662 | 0.8621 | 0.8631 |
| 0.1884 | 1.25 | 400 | 0.2635 | 0.8468 | 0.9381 | 0.8901 | 0.8856 |
| 0.234 | 1.57 | 500 | 0.2639 | 0.8232 | 0.9677 | 0.8896 | 0.8814 |
| 0.2349 | 1.88 | 600 | 0.2478 | 0.8530 | 0.9328 | 0.8911 | 0.8874 |
| 0.1476 | 2.19 | 700 | 0.2560 | 0.8584 | 0.9297 | 0.8926 | 0.8895 |
| 0.1289 | 2.51 | 800 | 0.2698 | 0.8809 | 0.8916 | 0.8862 | 0.8869 |
| 0.1579 | 2.82 | 900 | 0.2614 | 0.8879 | 0.8715 | 0.8796 | 0.8822 |
| 0.0745 | 3.13 | 1000 | 0.2783 | 0.8854 | 0.8905 | 0.8880 | 0.8889 |
| 0.0697 | 3.45 | 1100 | 0.2844 | 0.8893 | 0.8879 | 0.8886 | 0.8900 |
| 0.0602 | 3.76 | 1200 | 0.3213 | 0.8797 | 0.8932 | 0.8864 | 0.8869 |
| 0.0246 | 4.08 | 1300 | 0.3393 | 0.8753 | 0.9096 | 0.8921 | 0.8913 |
| 0.0301 | 4.39 | 1400 | 0.3593 | 0.8644 | 0.9307 | 0.8964 | 0.8937 |
| 0.0348 | 4.7 | 1500 | 0.3804 | 0.8653 | 0.9344 | 0.8986 | 0.8957 |
| 0.011 | 5.02 | 1600 | 0.3897 | 0.8622 | 0.9365 | 0.8978 | 0.8947 |
| 0.0077 | 5.33 | 1700 | 0.4088 | 0.8754 | 0.9180 | 0.8962 | 0.8950 |
| 0.0064 | 5.64 | 1800 | 0.4281 | 0.8780 | 0.9170 | 0.8971 | 0.8960 |
| 0.0031 | 5.96 | 1900 | 0.4289 | 0.8736 | 0.9207 | 0.8965 | 0.8950 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
mhamza11/transaction
|
mhamza11
| 2024-01-12T17:09:46Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2024-01-09T06:58:33Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
ostapeno/indepexp_adauniNeo1B_aeslc_1_0_0_sub05_3ep
|
ostapeno
| 2024-01-12T17:08:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-12T14:24:46Z |
Number of experts present in the library: 3
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| aeslc_1_0_0 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| aeslc_1_0_0_v1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
| aeslc_1_0_0_v2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/aeslc_1_0_0 | lora |
Last updated on: 2024-01-12 17:08:39+00:00
|
ilhami/t5Adapter
|
ilhami
| 2024-01-12T17:06:18Z | 4 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"t5",
"adapterhub:sum/xsum",
"region:us"
] | null | 2024-01-12T17:05:34Z |
---
tags:
- t5
- adapter-transformers
- adapterhub:sum/xsum
---
# Adapter `ilhami/my-awesome-adapter` for t5-small
An [adapter](https://adapterhub.ml) for the `t5-small` model that was trained on the [sum/xsum](https://adapterhub.ml/explore/sum/xsum/) dataset and includes a prediction head for seq2seq lm.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("t5-small")
adapter_name = model.load_adapter("ilhami/my-awesome-adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
NousResearch/Obsidian-3B-V0.5
|
NousResearch
| 2024-01-12T17:02:26Z | 104 | 175 |
transformers
|
[
"transformers",
"pytorch",
"llava_stablelm_epoch",
"text-generation",
"Multimodal",
"StableLM",
"en",
"dataset:LDJnr/Capybara",
"dataset:LDJnr/LessWrong-Amplify-Instruct",
"dataset:LDJnr/Pure-Dove",
"dataset:LDJnr/Verified-Camel",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-10-24T02:00:29Z |
---
license: cc-by-sa-4.0
language:
- en
tags:
- Multimodal
- StableLM
datasets:
- LDJnr/Capybara
- LDJnr/LessWrong-Amplify-Instruct
- LDJnr/Pure-Dove
- LDJnr/Verified-Camel
---
# Obsidian: Worlds smallest multi-modal LLM. First multi-modal model in size 3B
## Model Name: Obsidian-3B-V0.5
Obsidian is a brand new series of Multimodal Language Models. This first project is led by Quan N. and Luigi D.(LDJ).
Obsidian-3B-V0.5 is a multi-modal AI model that has vision! it's smarts are built on [Capybara-3B-V1.9](https://huggingface.co/NousResearch/Capybara-3B-V1.9) based on [StableLM-3B-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t). Capybara-3B-V1.9 achieves state-of-the-art performance when compared to model with similar size, even beats some 7B models.
Current finetuning and inference code is available on our GitHub repo: [Here](https://github.com/NousResearch/Obsidian)
## Acknowledgement
Obsidian-3B-V0.5 was developed and finetuned by [Nous Research](https://huggingface.co/NousResearch), in collaboration with [Virtual Interactive](https://huggingface.co/vilm).
Special thank you to **LDJ** for the wonderful Capybara dataset, and **qnguyen3** for the model training procedure.
## Model Training
Obsidian-3B-V0.5 followed the same training procedure as LLaVA 1.5
## Prompt Format
The model followed ChatML format. However, with `###` as the seperator
```
<|im_start|>user
What is this sign about?\n<image>
###
<|im_start|>assistant
The sign is about bullying, and it is placed on a black background with a red background.
###
```
## Benchmarks
Coming Soon!
Citation:
```
@article{nguyen2023Obsidian-3B,
title={Obsidian-3B: First Multi-modal below 7B Parameters.},
author={Nguyen, Quan and Daniele},
journal={HuggingFace:https://huggingface.co/NousResearch/Obsidian-3B-V0.5},
year={2023}
}
```
Acknowledgements:
```
@article{daniele2023amplify-instruct,
title={Amplify-Instruct: Synthetically Generated Diverse Multi-turn Conversations for Effecient LLM Training.},
author={Daniele, Luigi and Suphavadeeprasit},
journal={arXiv preprint arXiv:(comming soon)},
year={2023}
}
```
|
RatanRohith/NeuralPizza-7B-V0.1
|
RatanRohith
| 2024-01-12T17:00:51Z | 1,371 | 3 |
Transformers
|
[
"Transformers",
"safetensors",
"mistral",
"text-generation",
"transformers",
"fine-tuned",
"language-modeling",
"direct-preference-optimization",
"dataset:Intel/orca_dpo_pairs",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T16:31:57Z |
---
library_name: Transformers
tags:
- transformers
- fine-tuned
- language-modeling
- direct-preference-optimization
datasets:
- Intel/orca_dpo_pairs
license: apache-2.0
---
## Model Description
NeuralPizza-7B-V0.1 is a fine-tuned version of the SanjiWatsuki/Kunoichi-7B model, specialized through Direct Preference Optimization (DPO). It was fine-tuned using the Intel/orca_dpo_pairs dataset, focusing on enhancing model performance based on preference comparisons.
## Intended Use
This model is primarily intended for research and experimental applications in language modeling, especially for exploring the Direct Preference Optimization method. It provides insights into the nuances of DPO in the context of language model tuning.
## Training Data
The model was fine-tuned using the Intel/orca_dpo_pairs dataset. This dataset is designed for applying and testing Direct Preference Optimization techniques in language models.
## Training Procedure
The training followed the guidelines and methodologies outlined in the "Fine-Tune a Mistral 7B Model with Direct Preference Optimization" guide from Medium's Towards Data Science platform. Specific training regimes and hyperparameters are based on this guide. Here : https://medium.com/towards-data-science/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac
## Limitations and Bias
As an experimental model, it may carry biases inherent from its training data. The model's performance and outputs should be critically evaluated, especially in sensitive and diverse applications.
|
LoneStriker/WhiteRabbitNeo-33B-v1-4.65bpw-h6-exl2
|
LoneStriker
| 2024-01-12T16:59:53Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T16:51:55Z |
---
license: other
license_name: deepseek
license_link: https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE
---
# Our 33B-v1.1 model is now live (We'll always be serving the newest model on our web app)!
33B-v1.1 model comes with a "Prompt Enhancement" feature. Access at: https://www.whiterabbitneo.com/
# Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version
# Licence: Usage Restrictions
```
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
- For military use in any way;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
```
# Topics Covered:
```
- Open Ports: Identifying open ports is crucial as they can be entry points for attackers. Common ports to check include HTTP (80, 443), FTP (21), SSH (22), and SMB (445).
- Outdated Software or Services: Systems running outdated software or services are often vulnerable to exploits. This includes web servers, database servers, and any third-party software.
- Default Credentials: Many systems and services are installed with default usernames and passwords, which are well-known and can be easily exploited.
- Misconfigurations: Incorrectly configured services, permissions, and security settings can introduce vulnerabilities.
- Injection Flaws: SQL injection, command injection, and cross-site scripting (XSS) are common issues in web applications.
- Unencrypted Services: Services that do not use encryption (like HTTP instead of HTTPS) can expose sensitive data.
- Known Software Vulnerabilities: Checking for known vulnerabilities in software using databases like the National Vulnerability Database (NVD) or tools like Nessus or OpenVAS.
- Cross-Site Request Forgery (CSRF): This is where unauthorized commands are transmitted from a user that the web application trusts.
- Insecure Direct Object References: This occurs when an application provides direct access to objects based on user-supplied input.
- Security Misconfigurations in Web Servers/Applications: This includes issues like insecure HTTP headers or verbose error messages that reveal too much information.
- Broken Authentication and Session Management: This can allow attackers to compromise passwords, keys, or session tokens, or to exploit other implementation flaws to assume other users' identities.
- Sensitive Data Exposure: Includes vulnerabilities that expose sensitive data, such as credit card numbers, health records, or personal information.
- API Vulnerabilities: In modern web applications, APIs are often used and can have vulnerabilities like insecure endpoints or data leakage.
- Denial of Service (DoS) Vulnerabilities: Identifying services that are vulnerable to DoS attacks, which can make the resource unavailable to legitimate users.
- Buffer Overflows: Common in older software, these vulnerabilities can allow an attacker to crash the system or execute arbitrary code.
```
# WhiteRabbitNeo
<br>
<br>
WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity.
Our 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI.
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "whiterabbitneo/WhiteRabbitNeo-33B-v-1"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
tot_system_prompt = """
Answer the Question by exploring multiple reasoning paths as follows:
- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.
- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.
- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option. Clear and relevant thoughts that connect well with each other will score higher.
- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.
- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.
- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.
- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.
- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.
In summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers.
"""
conversation = f"SYSTEM: {tot_system_prompt} Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
# Sample Conversations:
1. "Write me a Fast API server with one end-point. The endpoint returns files from a S3 bucket.": https://www.whiterabbitneo.com/share/y06Po0e
2. "How can Metasploit be used for exploiting Android based IoT devices? What are some of the IoT devices that run Android? Show an example with code": https://www.whiterabbitneo.com/share/gWBwKlz
3. "How do I attack a wifi network?": https://www.whiterabbitneo.com/share/WLovxcu
4. "How do I create a reverse shell in Python": https://www.whiterabbitneo.com/share/LERgm8w
5. "How do we use Scapy for vulnerability assessment?": https://www.whiterabbitneo.com/share/t73iMzv
|
Polu/Cartpole
|
Polu
| 2024-01-12T16:58:18Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-12T16:41:28Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ankarb/ppo-LunarLander-v2
|
ankarb
| 2024-01-12T16:54:30Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-12T16:54:10Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.77 +/- 15.70
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
kmok1/cs_mT5_0.01_50_v0.1
|
kmok1
| 2024-01-12T16:49:07Z | 89 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-12T16:24:03Z |
---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: cs_mT5_0.01_50_v0.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cs_mT5_0.01_50_v0.1
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 7.3188
- Bleu: 1.2029
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.01
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 3.6406 | 1.0 | 6 | 6.1758 | 0.1903 | 19.0 |
| 4.2513 | 2.0 | 12 | 6.4360 | 0.4971 | 19.0 |
| 3.1515 | 3.0 | 18 | 6.2761 | 0.1689 | 19.0 |
| 3.4713 | 4.0 | 24 | 6.4576 | 0.4973 | 19.0 |
| 3.2069 | 5.0 | 30 | 6.6858 | 0.176 | 10.0 |
| 3.5913 | 6.0 | 36 | 6.2785 | 0.7212 | 19.0 |
| 3.7814 | 7.0 | 42 | 6.1120 | 0.7212 | 19.0 |
| 3.2429 | 8.0 | 48 | 6.3660 | 0.3725 | 19.0 |
| 3.2716 | 9.0 | 54 | 6.6523 | 0.4214 | 19.0 |
| 3.3443 | 10.0 | 60 | 6.4341 | 0.3793 | 19.0 |
| 2.4705 | 11.0 | 66 | 6.8433 | 0.7412 | 19.0 |
| 3.0869 | 12.0 | 72 | 6.9583 | 0.0 | 19.0 |
| 2.5187 | 13.0 | 78 | 6.3333 | 1.1569 | 19.0 |
| 3.1211 | 14.0 | 84 | 6.4031 | 0.2813 | 19.0 |
| 2.7326 | 15.0 | 90 | 6.4055 | 0.7962 | 19.0 |
| 2.5142 | 16.0 | 96 | 6.5799 | 0.1843 | 19.0 |
| 3.0964 | 17.0 | 102 | 6.8379 | 0.9395 | 19.0 |
| 2.5998 | 18.0 | 108 | 6.4570 | 0.0 | 19.0 |
| 3.2495 | 19.0 | 114 | 6.6350 | 0.2045 | 19.0 |
| 3.2509 | 20.0 | 120 | 6.3533 | 0.7212 | 19.0 |
| 3.2998 | 21.0 | 126 | 6.3142 | 0.6756 | 19.0 |
| 2.7829 | 22.0 | 132 | 6.5953 | 0.6646 | 19.0 |
| 3.0842 | 23.0 | 138 | 6.6276 | 0.7056 | 19.0 |
| 1.8502 | 24.0 | 144 | 6.6472 | 0.2386 | 19.0 |
| 1.945 | 25.0 | 150 | 6.6534 | 0.6966 | 19.0 |
| 2.7704 | 26.0 | 156 | 7.1955 | 0.7611 | 13.0 |
| 3.1289 | 27.0 | 162 | 6.6522 | 0.7286 | 17.0 |
| 3.0663 | 28.0 | 168 | 6.3873 | 0.8029 | 19.0 |
| 3.4269 | 29.0 | 174 | 6.4310 | 0.204 | 19.0 |
| 2.7845 | 30.0 | 180 | 6.7221 | 0.3228 | 19.0 |
| 2.0443 | 31.0 | 186 | 6.8353 | 0.3228 | 19.0 |
| 3.1621 | 32.0 | 192 | 7.1400 | 0.1346 | 19.0 |
| 2.4147 | 33.0 | 198 | 6.8844 | 1.2029 | 19.0 |
| 2.5869 | 34.0 | 204 | 6.7074 | 0.7475 | 19.0 |
| 2.1119 | 35.0 | 210 | 6.5778 | 0.7212 | 19.0 |
| 1.7629 | 36.0 | 216 | 6.5553 | 0.7867 | 19.0 |
| 2.3745 | 37.0 | 222 | 6.7126 | 0.7663 | 19.0 |
| 2.368 | 38.0 | 228 | 6.8008 | 0.4815 | 19.0 |
| 2.17 | 39.0 | 234 | 6.6388 | 0.7892 | 19.0 |
| 2.4311 | 40.0 | 240 | 6.6423 | 0.3228 | 19.0 |
| 2.8392 | 41.0 | 246 | 6.7127 | 0.3226 | 19.0 |
| 2.386 | 42.0 | 252 | 6.8011 | 0.31 | 19.0 |
| 2.7473 | 43.0 | 258 | 6.8704 | 0.31 | 19.0 |
| 1.9796 | 44.0 | 264 | 6.9846 | 1.2029 | 19.0 |
| 1.4857 | 45.0 | 270 | 7.1239 | 1.2029 | 19.0 |
| 1.8413 | 46.0 | 276 | 7.2177 | 1.194 | 19.0 |
| 2.171 | 47.0 | 282 | 7.2605 | 1.2029 | 19.0 |
| 1.9659 | 48.0 | 288 | 7.3048 | 1.2029 | 19.0 |
| 1.3681 | 49.0 | 294 | 7.3093 | 1.2029 | 19.0 |
| 2.086 | 50.0 | 300 | 7.3188 | 1.2029 | 19.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
|
sw882882/wake-word-direction
|
sw882882
| 2024-01-12T16:47:46Z | 1 | 0 |
keras
|
[
"keras",
"region:us"
] | null | 2024-01-12T15:10:34Z |
<!DOCTYPE html>
<html class="">
<head>
<meta charset="utf-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=no" />
<meta name="description" content="We’re on a journey to advance and democratize artificial intelligence through open source and open science." />
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta property="og:title" content="README.md · sw882882/wake-word-direction at main" />
<meta property="og:type" content="website" />
<meta property="og:url" content="https://huggingface.co/sw882882/wake-word-direction/blob/main/README.md" />
<meta property="og:image" content="https://cdn-thumbnails.huggingface.co/social-thumbnails/models/sw882882/wake-word-direction.png" />
<link rel="stylesheet" href="/front/build/kube-6099049/style.css" />
<link rel="preconnect" href="https://fonts.gstatic.com" />
<link
href="https://fonts.googleapis.com/css2?family=Source+Sans+Pro:ital,wght@0,200;0,300;0,400;0,600;0,700;0,900;1,200;1,300;1,400;1,600;1,700;1,900&display=swap"
rel="stylesheet"
/>
<link
href="https://fonts.googleapis.com/css2?family=IBM+Plex+Mono:wght@400;600;700&display=swap"
rel="stylesheet"
/>
<link
rel="preload"
href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.12.0/katex.min.css"
as="style"
onload="this.onload=null;this.rel='stylesheet'"
/>
<noscript>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.12.0/katex.min.css" />
</noscript>
<title>README.md · sw882882/wake-word-direction at main</title>
<script defer data-domain="huggingface.co" src="/js/script.js"></script>
<script type="text/javascript" src="https://de5282c3ca0c.edge.sdk.awswaf.com/de5282c3ca0c/526cf06acb0d/challenge.js" defer></script>
</head>
<body class="flex flex-col min-h-screen bg-white dark:bg-gray-950 text-black ViewerBlobPage">
<div class="flex min-h-screen flex-col">
<div class="SVELTE_HYDRATER contents" data-props="{"classNames":"","isWide":false,"isZh":false}" data-target="MainHeader"><header class="border-b border-gray-100 "><div class="w-full px-4 container flex h-16 items-center"><div class="flex flex-1 items-center"><a class="mr-5 flex flex-none items-center lg:mr-6" href="/"><img alt="Hugging Face's logo" class="w-7 md:mr-2" src="/front/assets/huggingface_logo-noborder.svg">
<span class="hidden whitespace-nowrap text-lg font-bold md:block">Hugging Face</span></a>
<div class="relative flex-1 lg:max-w-sm mr-2 sm:mr-4 lg:mr-6"><input autocomplete="off" class="w-full dark:bg-gray-950 pl-8 form-input-alt h-9 pr-3 focus:shadow-xl " name="" placeholder="Search models, datasets, users..." spellcheck="false" type="text" value="">
<svg class="absolute left-2.5 text-gray-400 top-1/2 transform -translate-y-1/2" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M30 28.59L22.45 21A11 11 0 1 0 21 22.45L28.59 30zM5 14a9 9 0 1 1 9 9a9 9 0 0 1-9-9z" fill="currentColor"></path></svg>
</div>
<div class="flex flex-none items-center justify-center p-0.5 place-self-stretch lg:hidden"><button class="relative z-40 flex h-6 w-8 items-center justify-center" type="button"><svg width="1em" height="1em" viewBox="0 0 10 10" class="text-xl" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" preserveAspectRatio="xMidYMid meet" fill="currentColor"><path fill-rule="evenodd" clip-rule="evenodd" d="M1.65039 2.9999C1.65039 2.8066 1.80709 2.6499 2.00039 2.6499H8.00039C8.19369 2.6499 8.35039 2.8066 8.35039 2.9999C8.35039 3.1932 8.19369 3.3499 8.00039 3.3499H2.00039C1.80709 3.3499 1.65039 3.1932 1.65039 2.9999ZM1.65039 4.9999C1.65039 4.8066 1.80709 4.6499 2.00039 4.6499H8.00039C8.19369 4.6499 8.35039 4.8066 8.35039 4.9999C8.35039 5.1932 8.19369 5.3499 8.00039 5.3499H2.00039C1.80709 5.3499 1.65039 5.1932 1.65039 4.9999ZM2.00039 6.6499C1.80709 6.6499 1.65039 6.8066 1.65039 6.9999C1.65039 7.1932 1.80709 7.3499 2.00039 7.3499H8.00039C8.19369 7.3499 8.35039 7.1932 8.35039 6.9999C8.35039 6.8066 8.19369 6.6499 8.00039 6.6499H2.00039Z"></path></svg>
</button>
</div></div>
<nav aria-label="Main" class="ml-auto hidden lg:block"><ul class="flex items-center space-x-2"><li><a class="group flex items-center px-2 py-0.5 dark:hover:text-gray-400 hover:text-indigo-700" href="/models"><svg class="mr-1.5 text-gray-400 group-hover:text-indigo-500" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg>
Models</a>
</li><li><a class="group flex items-center px-2 py-0.5 dark:hover:text-gray-400 hover:text-red-700" href="/datasets"><svg class="mr-1.5 text-gray-400 group-hover:text-red-500" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 25 25"><ellipse cx="12.5" cy="5" fill="currentColor" fill-opacity="0.25" rx="7.5" ry="2"></ellipse><path d="M12.5 15C16.6421 15 20 14.1046 20 13V20C20 21.1046 16.6421 22 12.5 22C8.35786 22 5 21.1046 5 20V13C5 14.1046 8.35786 15 12.5 15Z" fill="currentColor" opacity="0.5"></path><path d="M12.5 7C16.6421 7 20 6.10457 20 5V11.5C20 12.6046 16.6421 13.5 12.5 13.5C8.35786 13.5 5 12.6046 5 11.5V5C5 6.10457 8.35786 7 12.5 7Z" fill="currentColor" opacity="0.5"></path><path d="M5.23628 12C5.08204 12.1598 5 12.8273 5 13C5 14.1046 8.35786 15 12.5 15C16.6421 15 20 14.1046 20 13C20 12.8273 19.918 12.1598 19.7637 12C18.9311 12.8626 15.9947 13.5 12.5 13.5C9.0053 13.5 6.06886 12.8626 5.23628 12Z" fill="currentColor"></path></svg>
Datasets</a>
</li><li><a class="group flex items-center px-2 py-0.5 dark:hover:text-gray-400 hover:text-blue-700" href="/spaces"><svg class="mr-1.5 text-gray-400 group-hover:text-blue-500" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" viewBox="0 0 25 25"><path opacity=".5" d="M6.016 14.674v4.31h4.31v-4.31h-4.31ZM14.674 14.674v4.31h4.31v-4.31h-4.31ZM6.016 6.016v4.31h4.31v-4.31h-4.31Z" fill="currentColor"></path><path opacity=".75" fill-rule="evenodd" clip-rule="evenodd" d="M3 4.914C3 3.857 3.857 3 4.914 3h6.514c.884 0 1.628.6 1.848 1.414a5.171 5.171 0 0 1 7.31 7.31c.815.22 1.414.964 1.414 1.848v6.514A1.914 1.914 0 0 1 20.086 22H4.914A1.914 1.914 0 0 1 3 20.086V4.914Zm3.016 1.102v4.31h4.31v-4.31h-4.31Zm0 12.968v-4.31h4.31v4.31h-4.31Zm8.658 0v-4.31h4.31v4.31h-4.31Zm0-10.813a2.155 2.155 0 1 1 4.31 0 2.155 2.155 0 0 1-4.31 0Z" fill="currentColor"></path><path opacity=".25" d="M16.829 6.016a2.155 2.155 0 1 0 0 4.31 2.155 2.155 0 0 0 0-4.31Z" fill="currentColor"></path></svg>
Spaces</a>
</li><li><a class="group flex items-center px-2 py-0.5 dark:hover:text-gray-400 hover:text-yellow-700" href="/docs"><svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="mr-1.5 text-gray-400 group-hover:text-yellow-500" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path opacity="0.5" d="M20.9022 5.10334L10.8012 10.8791L7.76318 9.11193C8.07741 8.56791 8.5256 8.11332 9.06512 7.7914L15.9336 3.73907C17.0868 3.08811 18.5002 3.26422 19.6534 3.91519L19.3859 3.73911C19.9253 4.06087 20.5879 4.56025 20.9022 5.10334Z" fill="currentColor"></path><path d="M10.7999 10.8792V28.5483C10.2136 28.5475 9.63494 28.4139 9.10745 28.1578C8.5429 27.8312 8.074 27.3621 7.74761 26.7975C7.42122 26.2327 7.24878 25.5923 7.24756 24.9402V10.9908C7.25062 10.3319 7.42358 9.68487 7.74973 9.1123L10.7999 10.8792Z" fill="currentColor" fill-opacity="0.75"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M21.3368 10.8499V6.918C21.3331 6.25959 21.16 5.61234 20.8346 5.03949L10.7971 10.8727L10.8046 10.874L21.3368 10.8499Z" fill="currentColor"></path><path opacity="0.5" d="M21.7937 10.8488L10.7825 10.8741V28.5486L21.7937 28.5234C23.3344 28.5234 24.5835 27.2743 24.5835 25.7335V13.6387C24.5835 12.0979 23.4365 11.1233 21.7937 10.8488Z" fill="currentColor"></path></svg>
Docs</a>
</li>
<li><div class="relative ">
<button class="px-2 py-0.5 group hover:text-green-700 dark:hover:text-gray-400 flex items-center " type="button">
<svg class="mr-1.5 text-gray-400 group-hover:text-green-500" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-tertiary" d="M19 6H5a3 3 0 0 0-3 3v2.72L8.837 14h6.326L22 11.72V9a3 3 0 0 0-3-3z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M10 6V5h4v1h2V5a2.002 2.002 0 0 0-2-2h-4a2.002 2.002 0 0 0-2 2v1h2zm-1.163 8L2 11.72V18a3.003 3.003 0 0 0 3 3h14a3.003 3.003 0 0 0 3-3v-6.28L15.163 14H8.837z" fill="currentColor"></path></svg>
Solutions
</button>
</div></li>
<li><a class="group flex items-center px-2 py-0.5 hover:text-gray-500 dark:hover:text-gray-400" href="/pricing">Pricing
</a></li>
<li><div class="relative group">
<button class="px-2 py-0.5 hover:text-gray-500 dark:hover:text-gray-600 flex items-center " type="button">
<svg class="mr-1.5 text-gray-500 w-5 group-hover:text-gray-400 dark:text-gray-300 dark:group-hover:text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" viewBox="0 0 32 18" preserveAspectRatio="xMidYMid meet"><path fill-rule="evenodd" clip-rule="evenodd" d="M14.4504 3.30221C14.4504 2.836 14.8284 2.45807 15.2946 2.45807H28.4933C28.9595 2.45807 29.3374 2.836 29.3374 3.30221C29.3374 3.76842 28.9595 4.14635 28.4933 4.14635H15.2946C14.8284 4.14635 14.4504 3.76842 14.4504 3.30221Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M14.4504 9.00002C14.4504 8.53382 14.8284 8.15588 15.2946 8.15588H28.4933C28.9595 8.15588 29.3374 8.53382 29.3374 9.00002C29.3374 9.46623 28.9595 9.84417 28.4933 9.84417H15.2946C14.8284 9.84417 14.4504 9.46623 14.4504 9.00002Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M14.4504 14.6978C14.4504 14.2316 14.8284 13.8537 15.2946 13.8537H28.4933C28.9595 13.8537 29.3374 14.2316 29.3374 14.6978C29.3374 15.164 28.9595 15.542 28.4933 15.542H15.2946C14.8284 15.542 14.4504 15.164 14.4504 14.6978Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M1.94549 6.87377C2.27514 6.54411 2.80962 6.54411 3.13928 6.87377L6.23458 9.96907L9.32988 6.87377C9.65954 6.54411 10.194 6.54411 10.5237 6.87377C10.8533 7.20343 10.8533 7.73791 10.5237 8.06756L6.23458 12.3567L1.94549 8.06756C1.61583 7.73791 1.61583 7.20343 1.94549 6.87377Z" fill="currentColor"></path></svg>
</button>
</div></li>
<li><hr class="h-5 w-0.5 border-none bg-gray-100 dark:bg-gray-800"></li>
<li><a class="block cursor-pointer px-2 py-0.5 hover:text-gray-500 dark:hover:text-gray-400" href="/login">Log In
</a></li>
<li><a class="rounded-full border border-transparent bg-gray-900 px-3 py-1 leading-none text-white hover:border-black hover:bg-white hover:text-black" href="/join">Sign Up
</a></li></ul></nav></div></header></div>
<div class="SVELTE_HYDRATER contents" data-props="{}" data-target="GoogleAnalyticsTracker"></div>
<div class="SVELTE_HYDRATER contents" data-props="{}" data-target="SSOBanner"></div>
<main class="flex flex-1 flex-col"><div class="SVELTE_HYDRATER contents" data-props="{"activeTab":"files","author":{"avatarUrl":"/avatars/353870f8c40d044d4fbb2e37968d365b.svg","fullname":"Andrew","name":"sw882882","type":"user","isPro":false,"isHf":false},"canReadRepoSettings":false,"canWriteRepoContent":false,"canDisable":false,"model":{"author":"sw882882","cardData":{"license":"apache-2.0"},"cardExists":true,"config":{},"discussionsDisabled":false,"downloads":0,"downloadsAllTime":0,"id":"sw882882/wake-word-direction","isLikedByUser":false,"isWatchedByUser":false,"inference":"LibraryNotDetected","lastModified":"2024-01-12T15:10:34.000Z","likes":0,"model-index":null,"private":false,"repoType":"model","gated":false,"pwcLink":{"error":"Unknown error, can't generate link to Papers With Code."},"tags":["license:apache-2.0","region:us"],"tag_objs":[{"id":"license:apache-2.0","label":"apache-2.0","type":"license"},{"type":"region","label":"🇺🇸 Region: US","id":"region:us"}]},"discussionsStats":{"closed":0,"open":0,"total":0}}" data-target="ModelHeader"><header class="from-gray-50-to-white border-b border-gray-100 bg-gradient-to-t via-white dark:via-gray-950 pt-6 sm:pt-9"><div class="container relative "><h1 class="flex flex-wrap items-center leading-tight mb-3 text-lg md:text-xl">
<div class="group flex flex-none items-center"><div class="relative mr-1.5 flex items-center">
<img alt="" class="w-3.5 h-3.5 rounded-full flex-none" src="/avatars/353870f8c40d044d4fbb2e37968d365b.svg" crossorigin="anonymous"></div>
<a href="/sw882882" class="text-gray-400 hover:text-blue-600">sw882882</a>
<div class="mx-0.5 text-gray-300">/</div></div>
<div class="max-w-full "><a class="break-words font-mono font-semibold hover:text-blue-600 " href="/sw882882/wake-word-direction">wake-word-direction</a>
<button class="relative text-sm mr-4 inline-flex cursor-pointer items-center text-sm focus:outline-none mx-0.5 text-gray-600 " title="Copy model name to clipboard" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg>
</button></div>
<div class="inline-flex items-center overflow-hidden whitespace-nowrap rounded-md border bg-white text-sm leading-none text-gray-500 mr-2"><button class="relative flex items-center px-1.5 py-1 hover:bg-gradient-to-t focus:outline-none overflow-hidden from-red-50 to-transparent dark:from-red-900 dark:to-red-800" title="Like"><svg class="left-1.5 absolute" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" fill="currentColor"><path d="M22.45,6a5.47,5.47,0,0,1,3.91,1.64,5.7,5.7,0,0,1,0,8L16,26.13,5.64,15.64a5.7,5.7,0,0,1,0-8,5.48,5.48,0,0,1,7.82,0L16,10.24l2.53-2.58A5.44,5.44,0,0,1,22.45,6m0-2a7.47,7.47,0,0,0-5.34,2.24L16,7.36,14.89,6.24a7.49,7.49,0,0,0-10.68,0,7.72,7.72,0,0,0,0,10.82L16,29,27.79,17.06a7.72,7.72,0,0,0,0-10.82A7.49,7.49,0,0,0,22.45,4Z"></path></svg>
<span class="ml-4 pl-0.5">like</span></button>
<button class="flex items-center border-l px-1.5 py-1 text-gray-400 hover:bg-gray-50 focus:bg-gray-100 focus:outline-none dark:hover:bg-gray-900 dark:focus:bg-gray-800" title="See users who liked this repository">0</button></div>
</h1>
<div class="mb-3 flex flex-wrap md:mb-4"><div class="relative inline-block mr-1 mb-1 md:mr-1.5 md:mb-1.5">
<button class=" " type="button">
<a class="tag mr-0 mb-0 md:mr-0 md:mb-0 tag-white rounded-full" href="/models?license=license%3Aapache-2.0"><svg class="ml-2 text-xs text-gray-900" width="1em" height="1em" viewBox="0 0 10 10" fill="none" xmlns="http://www.w3.org/2000/svg"><path d="M1.46009 5.0945V6.88125C1.46009 7.25201 1.75937 7.55129 2.13012 7.55129C2.50087 7.55129 2.80016 7.25201 2.80016 6.88125V5.0945C2.80016 4.72375 2.50087 4.42446 2.13012 4.42446C1.75937 4.42446 1.46009 4.72375 1.46009 5.0945ZM4.14022 5.0945V6.88125C4.14022 7.25201 4.4395 7.55129 4.81026 7.55129C5.18101 7.55129 5.48029 7.25201 5.48029 6.88125V5.0945C5.48029 4.72375 5.18101 4.42446 4.81026 4.42446C4.4395 4.42446 4.14022 4.72375 4.14022 5.0945ZM1.23674 9.78473H8.38377C8.75452 9.78473 9.0538 9.48545 9.0538 9.1147C9.0538 8.74395 8.75452 8.44466 8.38377 8.44466H1.23674C0.865993 8.44466 0.566711 8.74395 0.566711 9.1147C0.566711 9.48545 0.865993 9.78473 1.23674 9.78473ZM6.82036 5.0945V6.88125C6.82036 7.25201 7.11964 7.55129 7.49039 7.55129C7.86114 7.55129 8.16042 7.25201 8.16042 6.88125V5.0945C8.16042 4.72375 7.86114 4.42446 7.49039 4.42446C7.11964 4.42446 6.82036 4.72375 6.82036 5.0945ZM4.39484 0.623142L0.865993 2.48137C0.682851 2.57517 0.566711 2.76725 0.566711 2.97273C0.566711 3.28094 0.816857 3.53109 1.12507 3.53109H8.49991C8.80365 3.53109 9.0538 3.28094 9.0538 2.97273C9.0538 2.76725 8.93766 2.57517 8.75452 2.48137L5.22568 0.623142C4.9666 0.484669 4.65391 0.484669 4.39484 0.623142V0.623142Z" fill="currentColor"></path></svg>
<span class="-mr-1 !pr-0 text-gray-400">License: </span>
<span>apache-2.0</span>
</a>
</button>
</div></div>
<div class="flex flex-col-reverse lg:flex-row lg:items-center lg:justify-between"><div class="-mb-px flex h-12 items-center overflow-x-auto overflow-y-hidden "><a class="tab-alternate " href="/sw882882/wake-word-direction"><svg class="mr-1.5 text-gray-400 flex-none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg>
Model card
</a><a class="tab-alternate active" href="/sw882882/wake-word-direction/tree/main"><svg class="mr-1.5 text-gray-400 flex-none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-tertiary" d="M21 19h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2zm0-4h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2zm0-8h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2zm0 4h-8a1 1 0 0 1 0-2h8a1 1 0 0 1 0 2z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M9 19a1 1 0 0 1-1-1V6a1 1 0 0 1 2 0v12a1 1 0 0 1-1 1zm-6-4.333a1 1 0 0 1-.64-1.769L3.438 12l-1.078-.898a1 1 0 0 1 1.28-1.538l2 1.667a1 1 0 0 1 0 1.538l-2 1.667a.999.999 0 0 1-.64.231z" fill="currentColor"></path></svg>
<span class="xl:hidden">Files</span>
<span class="hidden xl:inline">Files and versions</span>
</a><a class="tab-alternate " href="/sw882882/wake-word-direction/discussions"><svg class="mr-1.5 text-gray-400 flex-none" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M20.6081 3C21.7684 3 22.8053 3.49196 23.5284 4.38415C23.9756 4.93678 24.4428 5.82749 24.4808 7.16133C24.9674 7.01707 25.4353 6.93643 25.8725 6.93643C26.9833 6.93643 27.9865 7.37587 28.696 8.17411C29.6075 9.19872 30.0124 10.4579 29.8361 11.7177C29.7523 12.3177 29.5581 12.8555 29.2678 13.3534C29.8798 13.8646 30.3306 14.5763 30.5485 15.4322C30.719 16.1032 30.8939 17.5006 29.9808 18.9403C30.0389 19.0342 30.0934 19.1319 30.1442 19.2318C30.6932 20.3074 30.7283 21.5229 30.2439 22.6548C29.5093 24.3704 27.6841 25.7219 24.1397 27.1727C21.9347 28.0753 19.9174 28.6523 19.8994 28.6575C16.9842 29.4379 14.3477 29.8345 12.0653 29.8345C7.87017 29.8345 4.8668 28.508 3.13831 25.8921C0.356375 21.6797 0.754104 17.8269 4.35369 14.1131C6.34591 12.058 7.67023 9.02782 7.94613 8.36275C8.50224 6.39343 9.97271 4.20438 12.4172 4.20438H12.4179C12.6236 4.20438 12.8314 4.2214 13.0364 4.25468C14.107 4.42854 15.0428 5.06476 15.7115 6.02205C16.4331 5.09583 17.134 4.359 17.7682 3.94323C18.7242 3.31737 19.6794 3 20.6081 3ZM20.6081 5.95917C20.2427 5.95917 19.7963 6.1197 19.3039 6.44225C17.7754 7.44319 14.8258 12.6772 13.7458 14.7131C13.3839 15.3952 12.7655 15.6837 12.2086 15.6837C11.1036 15.6837 10.2408 14.5497 12.1076 13.1085C14.9146 10.9402 13.9299 7.39584 12.5898 7.1776C12.5311 7.16799 12.4731 7.16355 12.4172 7.16355C11.1989 7.16355 10.6615 9.33114 10.6615 9.33114C10.6615 9.33114 9.0863 13.4148 6.38031 16.206C3.67434 18.998 3.5346 21.2388 5.50675 24.2246C6.85185 26.2606 9.42666 26.8753 12.0653 26.8753C14.8021 26.8753 17.6077 26.2139 19.1799 25.793C19.2574 25.7723 28.8193 22.984 27.6081 20.6107C27.4046 20.212 27.0693 20.0522 26.6471 20.0522C24.9416 20.0522 21.8393 22.6726 20.5057 22.6726C20.2076 22.6726 19.9976 22.5416 19.9116 22.222C19.3433 20.1173 28.552 19.2325 27.7758 16.1839C27.639 15.6445 27.2677 15.4256 26.746 15.4263C24.4923 15.4263 19.4358 19.5181 18.3759 19.5181C18.2949 19.5181 18.2368 19.4937 18.2053 19.4419C17.6743 18.557 17.9653 17.9394 21.7082 15.6009C25.4511 13.2617 28.0783 11.8545 26.5841 10.1752C26.4121 9.98141 26.1684 9.8956 25.8725 9.8956C23.6001 9.89634 18.2311 14.9403 18.2311 14.9403C18.2311 14.9403 16.7821 16.496 15.9057 16.496C15.7043 16.496 15.533 16.4139 15.4169 16.2112C14.7956 15.1296 21.1879 10.1286 21.5484 8.06535C21.7928 6.66715 21.3771 5.95917 20.6081 5.95917Z" fill="#FF9D00"></path><path d="M5.50686 24.2246C3.53472 21.2387 3.67446 18.9979 6.38043 16.206C9.08641 13.4147 10.6615 9.33111 10.6615 9.33111C10.6615 9.33111 11.2499 6.95933 12.59 7.17757C13.93 7.39581 14.9139 10.9401 12.1069 13.1084C9.29997 15.276 12.6659 16.7489 13.7459 14.713C14.8258 12.6772 17.7747 7.44316 19.304 6.44221C20.8326 5.44128 21.9089 6.00204 21.5484 8.06532C21.188 10.1286 14.795 15.1295 15.4171 16.2118C16.0391 17.2934 18.2312 14.9402 18.2312 14.9402C18.2312 14.9402 25.0907 8.49588 26.5842 10.1752C28.0776 11.8545 25.4512 13.2616 21.7082 15.6008C17.9646 17.9393 17.6744 18.557 18.2054 19.4418C18.7372 20.3266 26.9998 13.1351 27.7759 16.1838C28.5513 19.2324 19.3434 20.1173 19.9117 22.2219C20.48 24.3274 26.3979 18.2382 27.6082 20.6107C28.8193 22.9839 19.2574 25.7722 19.18 25.7929C16.0914 26.62 8.24723 28.3726 5.50686 24.2246Z" fill="#FFD21E"></path></svg>
Community
</a>
</div>
<div class="relative mb-1.5 flex flex-wrap gap-1.5 sm:flex-nowrap lg:mb-0"><div class="order-last sm:order-first"><div class="relative ">
<button class="btn px-1.5 py-1.5 " type="button">
<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" class="p-0.5" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><circle cx="16" cy="7" r="3" fill="currentColor"></circle><circle cx="16" cy="16" r="3" fill="currentColor"></circle><circle cx="16" cy="25" r="3" fill="currentColor"></circle></svg>
</button>
</div></div>
<div class="flex-auto sm:flex-none"><button class="w-full cursor-pointer btn text-sm" type="button" ><svg class="mr-1.5 " xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7z" fill="currentColor"></path><path d="M1 16l7-7l1.41 1.41L3.83 16l5.58 5.59L8 23l-7-7z" fill="currentColor"></path><path d="M12.419 25.484L17.639 6l1.932.518L14.35 26z" fill="currentColor"></path></svg>
Use with library</button>
</div></div>
</div></div></header></div>
<div class="container relative flex flex-col md:grid md:space-y-0 w-full md:grid-cols-12 space-y-4 md:gap-6 mb-16"><section class="pt-8 border-gray-100 col-span-full"><header class="flex flex-wrap items-center justify-start pb-2 md:justify-end lg:flex-nowrap"><div class="mr-4 flex min-w-0 basis-auto flex-wrap items-center md:flex-grow md:basis-full lg:basis-auto lg:flex-nowrap"><div class="SVELTE_HYDRATER contents" data-props="{"path":"README.md","repoName":"sw882882/wake-word-direction","repoType":"model","rev":"main","refs":{"branches":[{"name":"main","ref":"refs/heads/main","targetCommit":"cbe5133678673303556f05bae4669876471257d7"}],"tags":[],"converts":[]},"view":"blob"}" data-target="BranchSelector"><div class="relative mr-4 mb-2">
<button class="text-sm md:text-base btn w-full cursor-pointer text-sm" type="button">
<svg class="mr-1.5 text-gray-700 dark:text-gray-400" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24" style="transform: rotate(360deg);"><path d="M13 14c-3.36 0-4.46 1.35-4.82 2.24C9.25 16.7 10 17.76 10 19a3 3 0 0 1-3 3a3 3 0 0 1-3-3c0-1.31.83-2.42 2-2.83V7.83A2.99 2.99 0 0 1 4 5a3 3 0 0 1 3-3a3 3 0 0 1 3 3c0 1.31-.83 2.42-2 2.83v5.29c.88-.65 2.16-1.12 4-1.12c2.67 0 3.56-1.34 3.85-2.23A3.006 3.006 0 0 1 14 7a3 3 0 0 1 3-3a3 3 0 0 1 3 3c0 1.34-.88 2.5-2.09 2.86C17.65 11.29 16.68 14 13 14m-6 4a1 1 0 0 0-1 1a1 1 0 0 0 1 1a1 1 0 0 0 1-1a1 1 0 0 0-1-1M7 4a1 1 0 0 0-1 1a1 1 0 0 0 1 1a1 1 0 0 0 1-1a1 1 0 0 0-1-1m10 2a1 1 0 0 0-1 1a1 1 0 0 0 1 1a1 1 0 0 0 1-1a1 1 0 0 0-1-1z" fill="currentColor"></path></svg>
main
<svg class="-mr-1 text-gray-500" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path d="M16.293 9.293L12 13.586L7.707 9.293l-1.414 1.414L12 16.414l5.707-5.707z" fill="currentColor"></path></svg></button>
</div></div>
<div class="mb-2 flex items-center overflow-hidden"><a class="truncate text-gray-800 hover:underline" href="/sw882882/wake-word-direction/tree/main">wake-word-direction</a>
<span class="mx-1 text-gray-300">/</span>
<span class="dark:text-gray-300">README.md</span></div></div>
</header>
<div class="SVELTE_HYDRATER contents" data-props="{"commitLast":{"date":"2024-01-12T15:10:34.000Z","verified":"verified","subject":"initial commit","authors":[{"_id":"637b86b800e89f148028d8c3","avatar":"/avatars/353870f8c40d044d4fbb2e37968d365b.svg","isHf":false,"user":"sw882882"}],"commit":{"id":"cbe5133678673303556f05bae4669876471257d7","parentIds":[]},"title":"initial commit"},"repo":{"name":"sw882882/wake-word-direction","type":"model"}}" data-target="LastCommit"><div class="from-gray-100-to-white flex items-baseline rounded-t-lg border border-b-0 bg-gradient-to-t px-3 py-2 dark:border-gray-800"><img class="mr-2.5 mt-0.5 h-4 w-4 self-center rounded-full" alt="sw882882's picture" src="/avatars/353870f8c40d044d4fbb2e37968d365b.svg">
<div class="mr-5 flex flex-none items-center truncate"><a class="hover:underline" href="/sw882882">sw882882
</a>
</div>
<div class="mr-4 truncate font-mono text-sm text-gray-500 hover:prose-a:underline"><!-- HTML_TAG_START -->initial commit<!-- HTML_TAG_END --></div>
<a class="rounded border bg-gray-50 px-1.5 text-sm hover:underline dark:border-gray-800 dark:bg-gray-900" href="/sw882882/wake-word-direction/commit/cbe5133678673303556f05bae4669876471257d7">cbe5133</a>
<span class="mx-2 text-green-500 dark:text-green-600 px-1.5 border-green-100 dark:border-green-800 rounded-full border text-xs uppercase" title="This commit is signed and the signature is verified">verified</span>
<time class="ml-auto hidden flex-none truncate pl-2 text-gray-500 lg:block dark:text-gray-400" datetime="2024-01-12T15:10:34" title="Fri, 12 Jan 2024 15:10:34 GMT">7 minutes ago</time></div></div>
<div class="flex flex-wrap items-center border px-3 py-1.5 text-sm text-gray-800 dark:border-gray-800 dark:bg-gray-900"><div class="flex items-center gap-3 text-sm font-medium"><a class="rounded-md px-1.5 capitalize bg-gray-200 dark:bg-gray-800" href="/sw882882/wake-word-direction/blob/main/README.md">preview</a>
<a class="rounded-md px-1.5 capitalize " href="/sw882882/wake-word-direction/blob/main/README.md?code=true">code</a></div>
<div class="mx-4 text-gray-200">|</div>
<a class="my-1 mr-4 flex items-center hover:underline " href="/sw882882/wake-word-direction/raw/main/README.md"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7z" fill="currentColor"></path><path d="M1 16l7-7l1.41 1.41L3.83 16l5.58 5.59L8 23l-7-7z" fill="currentColor"></path><path d="M12.419 25.484L17.639 6l1.932.518L14.35 26z" fill="currentColor"></path></svg>
raw
</a><a class="my-1 mr-4 flex items-center hover:underline " href="/sw882882/wake-word-direction/commits/main/README.md"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M16 4C9.383 4 4 9.383 4 16s5.383 12 12 12s12-5.383 12-12S22.617 4 16 4zm0 2c5.535 0 10 4.465 10 10s-4.465 10-10 10S6 21.535 6 16S10.465 6 16 6zm-1 2v9h7v-2h-5V8z" fill="currentColor"></path></svg>
history
</a><a class="my-1 mr-4 flex items-center hover:underline " href="/sw882882/wake-word-direction/blame/main/README.md"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M16 2a14 14 0 1 0 14 14A14 14 0 0 0 16 2zm0 26a12 12 0 1 1 12-12a12 12 0 0 1-12 12z" fill="currentColor"></path><path d="M11.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z" fill="currentColor"></path><path d="M20.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z" fill="currentColor"></path></svg>
blame
</a><a class="my-1 mr-4 flex items-center hover:underline text-green-600 dark:text-gray-300" href="/sw882882/wake-word-direction/edit/main/README.md"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M2 26h28v2H2z" fill="currentColor"></path><path d="M25.4 9c.8-.8.8-2 0-2.8l-3.6-3.6c-.8-.8-2-.8-2.8 0l-15 15V24h6.4l15-15zm-5-5L24 7.6l-3 3L17.4 7l3-3zM6 22v-3.6l10-10l3.6 3.6l-10 10H6z" fill="currentColor"></path></svg>
contribute
</a><a class="my-1 mr-4 flex items-center hover:underline " href="/sw882882/wake-word-direction/delete/main/README.md"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M12 12h2v12h-2z" fill="currentColor"></path><path d="M18 12h2v12h-2z" fill="currentColor"></path><path d="M4 6v2h2v20a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2V8h2V6zm4 22V8h16v20z" fill="currentColor"></path><path d="M12 2h8v2h-8z" fill="currentColor"></path></svg>
delete
</a>
<div class="mr-4 flex items-center text-gray-400"><svg class="text-gray-300 text-sm mr-1.5 -translate-y-px" width="1em" height="1em" viewBox="0 0 22 28" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M15.3634 10.3639C15.8486 10.8491 15.8486 11.6357 15.3634 12.1209L10.9292 16.5551C10.6058 16.8785 10.0814 16.8785 9.7579 16.5551L7.03051 13.8277C6.54532 13.3425 6.54532 12.5558 7.03051 12.0707C7.51569 11.5855 8.30234 11.5855 8.78752 12.0707L9.7579 13.041C10.0814 13.3645 10.6058 13.3645 10.9292 13.041L13.6064 10.3639C14.0916 9.8787 14.8782 9.8787 15.3634 10.3639Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M10.6666 27.12C4.93329 25.28 0 19.2267 0 12.7867V6.52001C0 5.40001 0.693334 4.41334 1.73333 4.01334L9.73333 1.01334C10.3333 0.786673 11 0.786673 11.6 1.02667L19.6 4.02667C20.1083 4.21658 20.5465 4.55701 20.8562 5.00252C21.1659 5.44803 21.3324 5.97742 21.3333 6.52001V12.7867C21.3333 19.24 16.4 25.28 10.6666 27.12Z" fill="currentColor" fill-opacity="0.22"></path><path d="M10.0845 1.94967L10.0867 1.94881C10.4587 1.8083 10.8666 1.81036 11.2286 1.95515L11.2387 1.95919L11.2489 1.963L19.2489 4.963L19.25 4.96342C19.5677 5.08211 19.8416 5.29488 20.0351 5.57333C20.2285 5.85151 20.3326 6.18203 20.3333 6.52082C20.3333 6.52113 20.3333 6.52144 20.3333 6.52176L20.3333 12.7867C20.3333 18.6535 15.8922 24.2319 10.6666 26.0652C5.44153 24.2316 1 18.6409 1 12.7867V6.52001C1 5.82357 1.42893 5.20343 2.08883 4.94803L10.0845 1.94967Z" stroke="currentColor" stroke-opacity="0.30" stroke-width="2"></path></svg>
No virus
</div>
<div class="sm:ml-auto dark:text-gray-300">28 Bytes</div></div>
<div class="relative min-h-[100px] rounded-b-lg border border-t-0 leading-tight dark:border-gray-800 dark:bg-gray-925">
<div class="py-4 px-4 sm:px-6 prose hf-sanitized hf-sanitized-9e7Pv9gJetP3r6qh7uvbx"><div class="not-prose -mx-6 -mt-4 mb-8 max-h-[300px] min-w-full overflow-auto border-b bg-gradient-to-t from-gray-50 px-6 pb-5 pt-4 font-mono text-xs transition-all dark:from-gray-900 dark:to-gray-950"><div class="mb-2 inline-block rounded-lg border px-2 py-1 font-mono text-xs leading-none">metadata</div>
<pre><!-- HTML_TAG_START --><span class="hljs-attr">license:</span> <span class="hljs-string">apache-2.0</span>
<!-- HTML_TAG_END --></pre></div>
<!-- HTML_TAG_START --><!-- HTML_TAG_END --></div>
</div></section></div></main>
</div>
<script>
import("/front/build/kube-6099049/index.js");
window.moonSha = "kube-6099049/";
window.hubConfig = JSON.parse(`{"features":{"signupDisabled":false},"sshGitUrl":"git@hf.co","moonHttpUrl":"https://huggingface.co","captchaApiKey":"bd5f2066-93dc-4bdd-a64b-a24646ca3859","captchaDisabledOnSignup":true,"datasetsServerPublicUrl":"https://datasets-server.huggingface.co","stripePublicKey":"pk_live_x2tdjFXBCvXo2FFmMybezpeM00J6gPCAAc","environment":"production","userAgent":"HuggingFace (production)"}`);
</script>
<!-- Stripe -->
<script>
if (["hf.co", "huggingface.co"].includes(window.location.hostname)) {
const script = document.createElement("script");
script.src = "https://js.stripe.com/v3/";
script.async = true;
document.head.appendChild(script);
}
</script>
<!-- Google analytics v4 -->
<script>
if (["hf.co", "huggingface.co"].includes(window.location.hostname)) {
const script = document.createElement("script");
script.src = "https://www.googletagmanager.com/gtag/js?id=G-8Q63TH4CSL";
script.async = true;
document.head.appendChild(script);
window.dataLayer = window.dataLayer || [];
function gtag() {
if (window.dataLayer !== undefined) {
window.dataLayer.push(arguments);
}
}
gtag("js", new Date());
gtag("config", "G-8Q63TH4CSL", { page_path: "/sw882882/wake-word-direction/blob/main/README.md" });
/// ^ See https://developers.google.com/analytics/devguides/collection/gtagjs/pages
gtag("consent", "default", { ad_storage: "denied", analytics_storage: "denied" });
/// ^ See https://developers.google.com/tag-platform/gtagjs/reference#consent
/// TODO: ask the user for their consent and update this with gtag('consent', 'update')
}
</script>
</body>
</html>
|
ostapeno/indepexp_adauniNeo1B_high_school_psychology_sub05_3ep
|
ostapeno
| 2024-01-12T16:46:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-12T14:24:14Z |
Number of experts present in the library: 3
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| high_school_psychology_v1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_psychology | lora |
| high_school_psychology | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_psychology | lora |
| high_school_psychology_v2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/high_school_psychology | lora |
Last updated on: 2024-01-12 16:46:20+00:00
|
LoneStriker/WhiteRabbitNeo-33B-v1-4.0bpw-h6-exl2
|
LoneStriker
| 2024-01-12T16:37:51Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T16:30:53Z |
---
license: other
license_name: deepseek
license_link: https://huggingface.co/deepseek-ai/deepseek-coder-33b-base/blob/main/LICENSE
---
# Our 33B-v1.1 model is now live (We'll always be serving the newest model on our web app)!
33B-v1.1 model comes with a "Prompt Enhancement" feature. Access at: https://www.whiterabbitneo.com/
# Our Discord Server
Join us at: https://discord.gg/8Ynkrcbk92 (Updated on Dec 29th. Now permanent link to join)
# DeepSeek Coder Licence + WhiteRabbitNeo Extended Version
# Licence: Usage Restrictions
```
You agree not to use the Model or Derivatives of the Model:
- In any way that violates any applicable national or international law or regulation or infringes upon the lawful rights and interests of any third party;
- For military use in any way;
- For the purpose of exploiting, harming or attempting to exploit or harm minors in any way;
- To generate or disseminate verifiably false information and/or content with the purpose of harming others;
- To generate or disseminate inappropriate content subject to applicable regulatory requirements;
- To generate or disseminate personal identifiable information without due authorization or for unreasonable use;
- To defame, disparage or otherwise harass others;
- For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation;
- For any use intended to or which has the effect of discriminating against or harming individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
- To exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
- For any use intended to or which has the effect of discriminating against individuals or groups based on legally protected characteristics or categories.
```
# Topics Covered:
```
- Open Ports: Identifying open ports is crucial as they can be entry points for attackers. Common ports to check include HTTP (80, 443), FTP (21), SSH (22), and SMB (445).
- Outdated Software or Services: Systems running outdated software or services are often vulnerable to exploits. This includes web servers, database servers, and any third-party software.
- Default Credentials: Many systems and services are installed with default usernames and passwords, which are well-known and can be easily exploited.
- Misconfigurations: Incorrectly configured services, permissions, and security settings can introduce vulnerabilities.
- Injection Flaws: SQL injection, command injection, and cross-site scripting (XSS) are common issues in web applications.
- Unencrypted Services: Services that do not use encryption (like HTTP instead of HTTPS) can expose sensitive data.
- Known Software Vulnerabilities: Checking for known vulnerabilities in software using databases like the National Vulnerability Database (NVD) or tools like Nessus or OpenVAS.
- Cross-Site Request Forgery (CSRF): This is where unauthorized commands are transmitted from a user that the web application trusts.
- Insecure Direct Object References: This occurs when an application provides direct access to objects based on user-supplied input.
- Security Misconfigurations in Web Servers/Applications: This includes issues like insecure HTTP headers or verbose error messages that reveal too much information.
- Broken Authentication and Session Management: This can allow attackers to compromise passwords, keys, or session tokens, or to exploit other implementation flaws to assume other users' identities.
- Sensitive Data Exposure: Includes vulnerabilities that expose sensitive data, such as credit card numbers, health records, or personal information.
- API Vulnerabilities: In modern web applications, APIs are often used and can have vulnerabilities like insecure endpoints or data leakage.
- Denial of Service (DoS) Vulnerabilities: Identifying services that are vulnerable to DoS attacks, which can make the resource unavailable to legitimate users.
- Buffer Overflows: Common in older software, these vulnerabilities can allow an attacker to crash the system or execute arbitrary code.
```
# WhiteRabbitNeo
<br>
<br>
WhiteRabbitNeo is a model series that can be used for offensive and defensive cybersecurity.
Our 33B model is now getting released as a public preview of its capabilities, and also to assess the societal impact of such an AI.
```
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "whiterabbitneo/WhiteRabbitNeo-33B-v-1"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_4bit=False,
load_in_8bit=True,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.5,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
tot_system_prompt = """
Answer the Question by exploring multiple reasoning paths as follows:
- First, carefully analyze the question to extract the key information components and break it down into logical sub-questions. This helps set up the framework for reasoning. The goal is to construct an internal search tree.
- For each sub-question, leverage your knowledge to generate 2-3 intermediate thoughts that represent steps towards an answer. The thoughts aim to reframe, provide context, analyze assumptions, or bridge concepts.
- Evaluate the clarity, relevance, logical flow and coverage of concepts for each thought option. Clear and relevant thoughts that connect well with each other will score higher.
- Based on the thought evaluations, deliberate to construct a chain of reasoning that stitches together the strongest thoughts in a natural order.
- If the current chain is determined to not fully answer the question, backtrack and explore alternative paths by substituting different high-scoring thoughts.
- Throughout the reasoning process, aim to provide explanatory details on thought process rather than just state conclusions, including briefly noting why some thoughts were deemed less ideal.
- Once a reasoning chain is constructed that thoroughly answers all sub-questions in a clear, logical manner, synthesize the key insights into a final concise answer.
- Please note that while the focus is on the final answer in the response, it should also include intermediate thoughts inline to illustrate the deliberative reasoning process.
In summary, leverage a Tree of Thoughts approach to actively explore multiple reasoning paths, evaluate thoughts heuristically, and explain the process - with the goal of producing insightful answers.
"""
conversation = f"SYSTEM: {tot_system_prompt} Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
# print(conversation)
json_data = {"prompt": user_input, "answer": answer}
# print(json_data)
# with open(output_file_path, "a") as output_file:
# output_file.write(json.dumps(json_data) + "\n")
```
# Sample Conversations:
1. "Write me a Fast API server with one end-point. The endpoint returns files from a S3 bucket.": https://www.whiterabbitneo.com/share/y06Po0e
2. "How can Metasploit be used for exploiting Android based IoT devices? What are some of the IoT devices that run Android? Show an example with code": https://www.whiterabbitneo.com/share/gWBwKlz
3. "How do I attack a wifi network?": https://www.whiterabbitneo.com/share/WLovxcu
4. "How do I create a reverse shell in Python": https://www.whiterabbitneo.com/share/LERgm8w
5. "How do we use Scapy for vulnerability assessment?": https://www.whiterabbitneo.com/share/t73iMzv
|
MaziyarPanahi/Misted-7B-Mistral-7B-Instruct-v0.2-slerp
|
MaziyarPanahi
| 2024-01-12T16:37:23Z | 18 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Walmart-the-bag/Misted-7B",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T16:32:32Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Walmart-the-bag/Misted-7B
---
# Misted-7B-Mistral-7B-Instruct-v0.2-slerp
Misted-7B-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Walmart-the-bag/Misted-7B](https://huggingface.co/Walmart-the-bag/Misted-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Walmart-the-bag/Misted-7B
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Misted-7B-Mistral-7B-Instruct-v0.2-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
imagepipeline/Realistic-Vision-V6.0
|
imagepipeline
| 2024-01-12T16:33:25Z | 43 | 1 |
diffusers
|
[
"diffusers",
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-12T16:31:59Z |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
## Realistic-Vision-V6.0
<img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1bb0b5cb-1249-4b84-bc9c-21029e002666/width=450/00012-3108443162.jpeg,https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/fcf0b3c8-129a-4bd0-ba34-0e32ab539ffa/width=450/00018-75415819.jpeg, " alt="Generated by Image Pipeline" style="border-radius: 10px;">
**This checkpoint model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - Propt: RAW photo, subject, 8k uhd, dslr, soft lighting, high quality, film grain, Fujifilm XT3. (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, UnrealisticDream, (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime, mutated hands and fingers:1.4), (deformed, distorted, disfigured:1.3), poorly drawn, bad anatomy, wrong anatomy, extra limb, missing limb, floating limbs, disconnected limbs, mutation, mutated, ugly, disgusting, amputation, UnrealisticDream, Euler A or DPM++ SDE Karras, CFG Scale 3,5 - 7, Hires. fix with 4x-UltraSharp upscaler, Denoising strength 0.25-0.45, Upscale by 1.1-2.0
[](https://imagepipeline.io/models/Realistic-Vision-V6.0?id=dd56943a-39fc-40ed-a325-db658affdfb4/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sd/text2image/v1/run"
payload = json.dumps({
"model_id": "dd56943a-39fc-40ed-a325-db658affdfb4",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "",
"lora_weights": ""
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sd/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at hello@imagepipeline.io
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
|
jysssacc/bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01
|
jysssacc
| 2024-01-12T16:32:14Z | 90 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bloom",
"text-generation",
"generated_from_trainer",
"base_model:bigscience/bloomz-560m",
"base_model:finetune:bigscience/bloomz-560m",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T16:30:18Z |
---
license: bigscience-bloom-rail-1.0
base_model: bigscience/bloomz-560m
tags:
- generated_from_trainer
model-index:
- name: bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bloomz-560m_fine_lr0.05_bs10_epoch5_wd0.01
This model is a fine-tuned version of [bigscience/bloomz-560m](https://huggingface.co/bigscience/bloomz-560m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 220.0167
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 63 | 200.9191 |
| 155.6973 | 2.0 | 126 | 311.3371 |
| 155.6973 | 3.0 | 189 | 256.3193 |
| 269.4904 | 4.0 | 252 | 291.8558 |
| 273.0994 | 5.0 | 315 | 220.0167 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
liwii/factual-consistency-classification-ja
|
liwii
| 2024-01-12T16:28:00Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"generated_from_trainer",
"base_model:line-corporation/line-distilbert-base-japanese",
"base_model:finetune:line-corporation/line-distilbert-base-japanese",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-03T05:34:56Z |
---
license: apache-2.0
base_model: line-corporation/line-distilbert-base-japanese
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: factual-consistency-classification-ja
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# factual-consistency-classification-ja
This model is a fine-tuned version of [line-corporation/line-distilbert-base-japanese](https://huggingface.co/line-corporation/line-distilbert-base-japanese) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6901
- Accuracy: 0.6855
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- distributed_type: tpu
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| No log | 1.0 | 306 | 1.0484 | 0.2773 |
| 1.044 | 2.0 | 612 | 1.0016 | 0.3320 |
| 1.044 | 3.0 | 918 | 0.9675 | 0.3633 |
| 0.9847 | 4.0 | 1224 | 0.9380 | 0.4434 |
| 0.9411 | 5.0 | 1530 | 0.9163 | 0.4863 |
| 0.9411 | 6.0 | 1836 | 0.8943 | 0.5742 |
| 0.9091 | 7.0 | 2142 | 0.8852 | 0.5137 |
| 0.9091 | 8.0 | 2448 | 0.8750 | 0.5039 |
| 0.8895 | 9.0 | 2754 | 0.8674 | 0.4941 |
| 0.8736 | 10.0 | 3060 | 0.8529 | 0.5254 |
| 0.8736 | 11.0 | 3366 | 0.8477 | 0.5195 |
| 0.8569 | 12.0 | 3672 | 0.8290 | 0.6133 |
| 0.8569 | 13.0 | 3978 | 0.8231 | 0.6055 |
| 0.8512 | 14.0 | 4284 | 0.8181 | 0.5879 |
| 0.8414 | 15.0 | 4590 | 0.8084 | 0.6211 |
| 0.8414 | 16.0 | 4896 | 0.8050 | 0.6152 |
| 0.8323 | 17.0 | 5202 | 0.8015 | 0.6016 |
| 0.8276 | 18.0 | 5508 | 0.7893 | 0.6426 |
| 0.8276 | 19.0 | 5814 | 0.7889 | 0.625 |
| 0.8248 | 20.0 | 6120 | 0.7829 | 0.6289 |
| 0.8248 | 21.0 | 6426 | 0.7796 | 0.6211 |
| 0.8166 | 22.0 | 6732 | 0.7759 | 0.6211 |
| 0.8107 | 23.0 | 7038 | 0.7720 | 0.6230 |
| 0.8107 | 24.0 | 7344 | 0.7691 | 0.625 |
| 0.8094 | 25.0 | 7650 | 0.7620 | 0.6426 |
| 0.8094 | 26.0 | 7956 | 0.7646 | 0.6230 |
| 0.8055 | 27.0 | 8262 | 0.7533 | 0.6582 |
| 0.8006 | 28.0 | 8568 | 0.7546 | 0.6348 |
| 0.8006 | 29.0 | 8874 | 0.7525 | 0.6348 |
| 0.7987 | 30.0 | 9180 | 0.7493 | 0.6465 |
| 0.7987 | 31.0 | 9486 | 0.7469 | 0.6484 |
| 0.7945 | 32.0 | 9792 | 0.7417 | 0.6562 |
| 0.7949 | 33.0 | 10098 | 0.7412 | 0.6465 |
| 0.7949 | 34.0 | 10404 | 0.7440 | 0.6367 |
| 0.7883 | 35.0 | 10710 | 0.7392 | 0.6504 |
| 0.7874 | 36.0 | 11016 | 0.7316 | 0.6660 |
| 0.7874 | 37.0 | 11322 | 0.7319 | 0.6543 |
| 0.7855 | 38.0 | 11628 | 0.7339 | 0.6504 |
| 0.7855 | 39.0 | 11934 | 0.7299 | 0.6562 |
| 0.7856 | 40.0 | 12240 | 0.7299 | 0.6504 |
| 0.7816 | 41.0 | 12546 | 0.7227 | 0.6738 |
| 0.7816 | 42.0 | 12852 | 0.7275 | 0.6504 |
| 0.7805 | 43.0 | 13158 | 0.7269 | 0.6543 |
| 0.7805 | 44.0 | 13464 | 0.7206 | 0.6641 |
| 0.7756 | 45.0 | 13770 | 0.7175 | 0.6777 |
| 0.7779 | 46.0 | 14076 | 0.7172 | 0.6660 |
| 0.7779 | 47.0 | 14382 | 0.7191 | 0.6582 |
| 0.7778 | 48.0 | 14688 | 0.7145 | 0.6680 |
| 0.7778 | 49.0 | 14994 | 0.7154 | 0.6602 |
| 0.7701 | 50.0 | 15300 | 0.7121 | 0.6719 |
| 0.774 | 51.0 | 15606 | 0.7142 | 0.6641 |
| 0.774 | 52.0 | 15912 | 0.7132 | 0.6719 |
| 0.7732 | 53.0 | 16218 | 0.7078 | 0.6836 |
| 0.768 | 54.0 | 16524 | 0.7123 | 0.6641 |
| 0.768 | 55.0 | 16830 | 0.7048 | 0.6855 |
| 0.7681 | 56.0 | 17136 | 0.7091 | 0.6641 |
| 0.7681 | 57.0 | 17442 | 0.7055 | 0.6797 |
| 0.7685 | 58.0 | 17748 | 0.7047 | 0.6816 |
| 0.7684 | 59.0 | 18054 | 0.7036 | 0.6836 |
| 0.7684 | 60.0 | 18360 | 0.7025 | 0.6836 |
| 0.7633 | 61.0 | 18666 | 0.7042 | 0.6699 |
| 0.7633 | 62.0 | 18972 | 0.7040 | 0.6699 |
| 0.7659 | 63.0 | 19278 | 0.7017 | 0.6777 |
| 0.7647 | 64.0 | 19584 | 0.7003 | 0.6836 |
| 0.7647 | 65.0 | 19890 | 0.7015 | 0.6738 |
| 0.7676 | 66.0 | 20196 | 0.6987 | 0.6816 |
| 0.7607 | 67.0 | 20502 | 0.6972 | 0.6875 |
| 0.7607 | 68.0 | 20808 | 0.6988 | 0.6777 |
| 0.7637 | 69.0 | 21114 | 0.6968 | 0.6875 |
| 0.7637 | 70.0 | 21420 | 0.6968 | 0.6816 |
| 0.7556 | 71.0 | 21726 | 0.6980 | 0.6738 |
| 0.7608 | 72.0 | 22032 | 0.6983 | 0.6758 |
| 0.7608 | 73.0 | 22338 | 0.6967 | 0.6758 |
| 0.7532 | 74.0 | 22644 | 0.6950 | 0.6816 |
| 0.7532 | 75.0 | 22950 | 0.6961 | 0.6738 |
| 0.7592 | 76.0 | 23256 | 0.6949 | 0.6797 |
| 0.7553 | 77.0 | 23562 | 0.6936 | 0.6836 |
| 0.7553 | 78.0 | 23868 | 0.6939 | 0.6855 |
| 0.7581 | 79.0 | 24174 | 0.6937 | 0.6816 |
| 0.7581 | 80.0 | 24480 | 0.6922 | 0.6836 |
| 0.7558 | 81.0 | 24786 | 0.6934 | 0.6758 |
| 0.7581 | 82.0 | 25092 | 0.6922 | 0.6855 |
| 0.7581 | 83.0 | 25398 | 0.6939 | 0.6738 |
| 0.7561 | 84.0 | 25704 | 0.6931 | 0.6797 |
| 0.7581 | 85.0 | 26010 | 0.6914 | 0.6836 |
| 0.7581 | 86.0 | 26316 | 0.6923 | 0.6797 |
| 0.7553 | 87.0 | 26622 | 0.6921 | 0.6816 |
| 0.7553 | 88.0 | 26928 | 0.6923 | 0.6797 |
| 0.7553 | 89.0 | 27234 | 0.6913 | 0.6816 |
| 0.7551 | 90.0 | 27540 | 0.6911 | 0.6816 |
| 0.7551 | 91.0 | 27846 | 0.6920 | 0.6777 |
| 0.7515 | 92.0 | 28152 | 0.6907 | 0.6797 |
| 0.7515 | 93.0 | 28458 | 0.6914 | 0.6797 |
| 0.7574 | 94.0 | 28764 | 0.6912 | 0.6797 |
| 0.7525 | 95.0 | 29070 | 0.6906 | 0.6836 |
| 0.7525 | 96.0 | 29376 | 0.6905 | 0.6836 |
| 0.7539 | 97.0 | 29682 | 0.6899 | 0.6855 |
| 0.7539 | 98.0 | 29988 | 0.6899 | 0.6855 |
| 0.754 | 99.0 | 30294 | 0.6901 | 0.6855 |
| 0.7573 | 100.0 | 30600 | 0.6901 | 0.6855 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.0
|
MaziyarPanahi/CatMacaroni-Slerp-Mistral-7B-Instruct-v0.2-slerp
|
MaziyarPanahi
| 2024-01-12T16:25:31Z | 21 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"cookinai/CatMacaroni-Slerp",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T16:20:27Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- cookinai/CatMacaroni-Slerp
---
# CatMacaroni-Slerp-Mistral-7B-Instruct-v0.2-slerp
CatMacaroni-Slerp-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [cookinai/CatMacaroni-Slerp](https://huggingface.co/cookinai/CatMacaroni-Slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: cookinai/CatMacaroni-Slerp
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/CatMacaroni-Slerp-Mistral-7B-Instruct-v0.2-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
ostapeno/indepexp_adauniNeo1B_sciq_Multiple_Choice_sub05_3ep
|
ostapeno
| 2024-01-12T16:23:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-12T15:25:03Z |
Number of experts present in the library: 3
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| sciq_Multiple_Choice | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| sciq_Multiple_Choice_v1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
| sciq_Multiple_Choice_v2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/sciq_Multiple_Choice | lora |
Last updated on: 2024-01-12 16:23:40+00:00
|
ybelkada/Mixtral-8x7B-Instruct-v0.1-bnb-4bit
|
ybelkada
| 2024-01-12T16:22:47Z | 168 | 59 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mistral",
"moe",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2023-12-25T11:09:56Z |
---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- mistral
- mixtral
- moe
model_name: Mixtral 8X7B - bnb 4-bit
inference: false
model_type: mixtral
pipeline_tag: text-generation
quantized_by: ybelkada
---
# Mixtral 8x7B Instruct-v0.1 - `bitsandbytes` 4-bit
This repository contains the bitsandbytes 4-bit quantized version of [`mistralai/Mixtral-8x7B-Instruct-v0.1`](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1). To use it, make sure to have the latest version of `bitsandbytes` and `transformers` installed from source:
Loading this model as such: will directly load the quantized model in 4-bit precision.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "ybelkada/Mixtral-8x7B-Instruct-v0.1-bnb-4bit"
model = AutoModelForCausalLM.from_pretrained(model_id)
```
Note you need a CUDA-compatible GPU device to run low-bit precision models with `bitsandbytes`
|
avemio-digital/lora_model_scipy
|
avemio-digital
| 2024-01-12T16:17:38Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:SciPhi/SciPhi-Self-RAG-Mistral-7B-32k",
"base_model:adapter:SciPhi/SciPhi-Self-RAG-Mistral-7B-32k",
"region:us"
] | null | 2024-01-12T16:16:43Z |
---
library_name: peft
base_model: SciPhi/SciPhi-Self-RAG-Mistral-7B-32k
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
ostapeno/indepexp_adauniNeo1B_social_i_qa_Generate_the_question_from_the_answer_sub05_3ep
|
ostapeno
| 2024-01-12T16:16:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-01-12T15:25:44Z |
Number of experts present in the library: 3
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| social_i_qa_Generate_the_question_from_the_answer | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| social_i_qa_Generate_the_question_from_the_answer_v1 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
| social_i_qa_Generate_the_question_from_the_answer_v2 | EleutherAI/gpt-neo-1.3B | ostapeno/adauni-v3-10k-flat/social_i_qa_Generate_the_question_from_the_answer | lora |
Last updated on: 2024-01-12 16:16:24+00:00
|
MaziyarPanahi/Kant-Test-0.1-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp
|
MaziyarPanahi
| 2024-01-12T16:12:42Z | 18 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"7b",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"Zardos/Kant-Test-0.1-Mistral-7B",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-12T16:07:35Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- mistral
- 7b
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- Zardos/Kant-Test-0.1-Mistral-7B
---
# Kant-Test-0.1-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp
Kant-Test-0.1-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp is a merge of the following models:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [Zardos/Kant-Test-0.1-Mistral-7B](https://huggingface.co/Zardos/Kant-Test-0.1-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: Zardos/Kant-Test-0.1-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/Kant-Test-0.1-Mistral-7B-Mistral-7B-Instruct-v0.2-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
Identifies specific configurations of models by filtering cards that contain 'distill', 'qwen2.5', '7b' while excluding certain base models and incorrect model ID patterns, uncovering unique model variants.
Filtered Model Cards Count
Finds the count of entries with specific card details that include 'distill', 'qwen2.5', '7b' but exclude certain base models, revealing valuable insights about the dataset's content distribution.
Filtered Distill Qwen 7B Models
Filters for specific card entries containing 'distill', 'qwen', and '7b', excluding certain strings and patterns, to identify relevant model configurations.
Filtered Qwen-7b Model Cards
The query performs a detailed filtering based on specific keywords and excludes certain entries, which could be useful for identifying a specific subset of cards but does not provide deeper insights or trends.
Filtered Qwen 7B Model Cards
The query filters for specific terms related to "distilled" or "distill", "qwen", and "7b" in the 'card' column but excludes certain base models, providing a limited set of entries for further inspection.
Qwen 7B Distilled Models
The query provides a basic filtering of records to find specific card names that include keywords related to distilled Qwen 7b models, excluding a particular base model, which gives limited insight but helps in focusing on relevant entries.
Qwen 7B Distilled Model Cards
The query filters data based on specific keywords in the modelId and card fields, providing limited insight primarily useful for locating specific entries rather than revealing broad patterns or trends.
Qwen 7B Distilled Models
Finds all entries containing the terms 'distilled', 'qwen', and '7b' in a case-insensitive manner, providing a filtered set of records but without deeper analysis.
Distilled Qwen 7B Models
The query filters for specific model IDs containing 'distilled', 'qwen', and '7b', providing a basic retrieval of relevant entries but without deeper analysis or insight.
Filtered Model Cards with Distill Qwen2.
Filters and retrieves records containing specific keywords in the card description while excluding certain phrases, providing a basic count of relevant entries.
Filtered Model Cards with Distill Qwen 7
The query filters specific variations of card descriptions containing 'distill', 'qwen', and '7b' while excluding a particular base model, providing limited but specific data retrieval.
Distill Qwen 7B Model Cards
The query filters and retrieves rows where the 'card' column contains specific keywords ('distill', 'qwen', and '7b'), providing a basic filter result that can help in identifying specific entries.