modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-09 18:59:16
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 551
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-09 18:27:33
| card
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|---|---|---|---|---|---|---|---|---|---|
GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15-dedup
|
GAI-LLM
| 2024-01-29T02:27:10Z | 56 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-29T02:17:11Z |
---
license: cc-by-nc-4.0
language:
- ko
library_name: transformers
pipeline_tag: text-generation
---
**The license is `cc-by-nc-4.0`.**
# **GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15-dedup**
## Model Details
**Model Developers** Donghoon Oh, Hanmin Myung, Eunyoung Kim (SK C&C G.AI Eng)
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture**
GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15 is an auto-regressive language model based on the LLaMA2 transformer architecture.
**Base Model** [beomi/OPEN-SOLAR-KO-10.7B](https://huggingface.co/beomi/OPEN-SOLAR-KO-10.7B)
**Training Dataset**
- We combined Open Korean Dateset using mixed-strategy with near deduplication
- We use A100 GPU 80GB * 8, when training.
# **Model Benchmark**
## KO-LLM leaderboard
- Follow up as [Open KO-LLM LeaderBoard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard).
# Implementation Code
```python
### GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15-dedup
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
repo = "GAI-LLM/OPEN-SOLAR-KO-10.7B-mixed-v15-dedup"
model = AutoModelForCausalLM.from_pretrained(
repo,
return_dict=True,
torch_dtype=torch.float16,
device_map='auto'
)
tokenizer = AutoTokenizer.from_pretrained(repo)
```
|
LoneStriker/CodeMate-v0.1-2.65bpw-h6-exl2
|
LoneStriker
| 2024-01-29T02:27:09Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"CodeMate",
"Code",
"en",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-29T02:22:03Z |
---
license: llama2
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- CodeMate
- Code
---
# **CodeMate-v0.1**
CodeMate-v0.1 is an intelligent programming assistant developed by [CodeMate](https://codemate.ai).
This model aims to assist users in generating high-quality code solutions for programming problems.
Please note that this model is currently in version 0.1.
## Model Details
- **Training Data:** Exclusively fine-tuned on a proprietary dataset of 1.8 billion tokens of high-quality programming problems and solutions.
- The dataset was generated manually and is internal to CodeMate.
- **Training Techniques:** The model was fine-tuned using Flash Attention 2, trained over 15 hours on 40 A100-80GB GPUs.
- A sequence length of 8096 tokens was used during training.
- **Multilingual Support:** CodeMate-v0.1 is proficient in multiple programming languages, including Python, C/C++, TypeScript, Java, and more.
## How to Get Started with the Model
Make sure to install Transformers from the main git branch:
```bash
pip install git+https://github.com/huggingface/transformers.git
```
## How to Prompt the Model
This model accepts prompts in the Alpaca/Vicuna instruction format. For example:
```markdown
### System Prompt
You are an intelligent programming assistant.
### User Message
Implement a linked list in C++
### Assistant
...
```
## Load the Model:
To load the model, utilize the following Python script:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
# Initialize the model
model_path = "codemateai/CodeMate-v0.1"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_path)
# ... generate response ...
```
## Bias, Risks, and Limitations
This model has undergone very limited testing. CodeMate recommends additional safety testing before any real-world deployments.
For more information and updates, visit the [CodeMate website](https://codemate.ai).
|
Patcas/plbart-worksDocWorks-step-3
|
Patcas
| 2024-01-29T02:24:55Z | 92 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"plbart",
"text2text-generation",
"generated_from_trainer",
"base_model:Patcas/plbart-works",
"base_model:finetune:Patcas/plbart-works",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-29T01:24:57Z |
---
base_model: Patcas/plbart-works
tags:
- generated_from_trainer
model-index:
- name: plbart-worksDocWorks-step-3
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. -->
# plbart-worksDocWorks-step-3
This model is a fine-tuned version of [Patcas/plbart-works](https://huggingface.co/Patcas/plbart-works) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8792
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 230 | 0.9631 |
| No log | 2.0 | 460 | 0.8839 |
| 0.9862 | 3.0 | 690 | 0.8658 |
| 0.9862 | 4.0 | 920 | 0.8792 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
simonycl/data-selection-Llama-2-7b-sharegpt-KCenterMedian-0.05-lora-epoch_4
|
simonycl
| 2024-01-29T02:23:02Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"region:us"
] | null | 2024-01-29T02:22:48Z |
---
library_name: peft
base_model: meta-llama/Llama-2-7b-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
|
yleo/monacan-translator-fr-mon
|
yleo
| 2024-01-29T02:20:20Z | 3 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mlabonne/NeuralBeagle14-7B",
"base_model:adapter:mlabonne/NeuralBeagle14-7B",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-01-28T19:59:32Z |
---
license: cc-by-nc-4.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: mlabonne/NeuralBeagle14-7B
model-index:
- name: monacan-translator-fr-mon
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. -->
# monacan-translator-fr-mon
This model is a fine-tuned version of [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B) on the generator 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: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
NickyNicky/Mix_TinyLlama-3x1B_oasst2_chatML_Cluster_3_2_1_V1
|
NickyNicky
| 2024-01-29T02:14:13Z | 80 | 3 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"merge",
"en",
"es",
"ru",
"zh",
"de",
"fr",
"th",
"ca",
"it",
"ja",
"pl",
"eo",
"eu",
"vi",
"fi",
"hu",
"ar",
"nl",
"da",
"tr",
"ko",
"he",
"id",
"cs",
"bn",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T13:36:26Z |
---
library_name: transformers
tags:
- merge
language:
- en
- es
- ru
- zh
- de
- fr
- th
- ca
- it
- ja
- pl
- eo
- eu
- vi
- fi
- hu
- ar
- nl
- da
- tr
- ko
- he
- id
- cs
- bn
- sv
widget:
- text: |
<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
podrias escribir un codigo de ejemplo en Python<|im_end|>
<|im_start|>assistant
license: apache-2.0
---
# Model Card for Model MixLlama
<!--  -->
<!--  -->
<!-- Provide a quick summary of what the model is/does. -->
```Python
experts:
- source_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_1_V1
positive_prompts:
- ""
- source_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_2_V1
positive_prompts:
- ""
- source_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_3_V1
positive_prompts:
- ""
base_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_1_V1
gate_mode: random # one of "hidden", "cheap_embed", or "random"
dtype: bfloat16 # output dtype (float32, float16, or bfloat16)
```
```Python
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
logging,
GenerationConfig,
TextIteratorStreamer,
)
import torch
new_model= "NickyNicky/Mix_TinyLlama-3x1B_oasst2_chatML_Cluster_3_2_1_V1"
model = AutoModelForCausalLM.from_pretrained(#f'NickyNicky/{new_model}',
new_model,
device_map="auto",
trust_remote_code=True,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage= True,
# use_flash_attention_2=False,
)
tokenizer = AutoTokenizer.from_pretrained(new_model,
max_length=2048,
trust_remote_code=True,
use_fast = True,
)
tokenizer.pad_token = tokenizer.eos_token
# tokenizer.padding_side = 'left'
tokenizer.padding_side = 'right'
prompt= """<|im_start|>system
You are a helpful AI assistant.<|im_end|>
<|im_start|>user
escribe una historia de amor.<|im_end|>
<|im_start|>assistant
"""
inputs = tokenizer.encode(prompt,
return_tensors="pt",
add_special_tokens=False).cuda()#.to("cuda") # False # True
generation_config = GenerationConfig(
max_new_tokens=700,
temperature=0.5,
top_p=0.9,
top_k=40,
repetition_penalty=1.1, #1.1, # 1.0 means no penalty, > 1.0 means penalty, 1.2 from CTRL paper
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
)
outputs = model.generate(
generation_config=generation_config,
input_ids=inputs,)
# tokenizer.decode(outputs[0], skip_special_tokens=False) #True
print(tokenizer.decode(outputs[0], skip_special_tokens=False))
```
|
wgj0714/my-awesome-model
|
wgj0714
| 2024-01-29T02:07:00Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:davidkim205/komt-mistral-7b-v1",
"base_model:adapter:davidkim205/komt-mistral-7b-v1",
"region:us"
] | null | 2024-01-29T01:35:02Z |
---
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: davidkim205/komt-mistral-7b-v1
model-index:
- name: my-awesome-model
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. -->
# my-awesome-model
This model is a fine-tuned version of [davidkim205/komt-mistral-7b-v1](https://huggingface.co/davidkim205/komt-mistral-7b-v1) on the None 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-29T02:05:29Z | 53 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"Minirecord/Mini_synatra_7b_03",
"pytorch",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"license:apache-2.0",
"base_model:MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-29T01:54:48Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- Minirecord/Mini_synatra_7b_03
- pytorch
- license:cc-by-sa-4.0
- autotrain_compatible
- endpoints_compatible
- region:us
- license:apache-2.0
model_name: Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-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 MaziyarPanahi/Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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 32768` 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="./Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # 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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./Mini_synatra_7b_03-Mistral-7B-Instruct-v0.1-GGUF.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)
|
Marcus2112/ppo-LunarLander-v2
|
Marcus2112
| 2024-01-29T01:50:43Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T01:50:14Z |
---
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: 27.60 +/- 116.51
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': 800000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'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': 'Marcus2112/ppo-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Alpaca69B/phi-2-absa-semeval-2016-3
|
Alpaca69B
| 2024-01-29T01:50:01Z | 34 | 0 |
transformers
|
[
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T20:48:38Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
gayanin/pubmed-mixed-noise-v3-0.4
|
gayanin
| 2024-01-29T01:48:48Z | 12 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-29T00:58:27Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: pubmed-mixed-noise-v3-0.4
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. -->
# pubmed-mixed-noise-v3-0.4
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7276
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.4148 | 0.11 | 500 | 1.2099 |
| 1.2472 | 0.21 | 1000 | 1.0718 |
| 1.0935 | 0.32 | 1500 | 0.9924 |
| 1.0193 | 0.43 | 2000 | 0.9482 |
| 0.8859 | 0.54 | 2500 | 0.9335 |
| 0.9838 | 0.64 | 3000 | 0.8792 |
| 0.9195 | 0.75 | 3500 | 0.8665 |
| 0.9793 | 0.86 | 4000 | 0.8397 |
| 0.8513 | 0.96 | 4500 | 0.8215 |
| 0.7384 | 1.07 | 5000 | 0.8261 |
| 0.8117 | 1.18 | 5500 | 0.8028 |
| 0.8606 | 1.28 | 6000 | 0.7930 |
| 0.8139 | 1.39 | 6500 | 0.7873 |
| 0.84 | 1.5 | 7000 | 0.7761 |
| 0.733 | 1.61 | 7500 | 0.7728 |
| 0.7102 | 1.71 | 8000 | 0.7644 |
| 0.6857 | 1.82 | 8500 | 0.7579 |
| 0.7244 | 1.93 | 9000 | 0.7500 |
| 0.5931 | 2.03 | 9500 | 0.7515 |
| 0.6165 | 2.14 | 10000 | 0.7484 |
| 0.6681 | 2.25 | 10500 | 0.7414 |
| 0.6716 | 2.35 | 11000 | 0.7390 |
| 0.6468 | 2.46 | 11500 | 0.7387 |
| 0.5505 | 2.57 | 12000 | 0.7361 |
| 0.635 | 2.68 | 12500 | 0.7333 |
| 0.6632 | 2.78 | 13000 | 0.7297 |
| 0.6271 | 2.89 | 13500 | 0.7278 |
| 0.5843 | 3.0 | 14000 | 0.7276 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Lifan-Z/Chinese-Classic-Poem-Generator-style5x4-GPT2
|
Lifan-Z
| 2024-01-29T01:48:09Z | 14 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"art",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-21T13:41:36Z |
---
license: apache-2.0
language:
- zh
tags:
- art
---
### **How to use "Lifan-Z/Chinese-Classic-Poem-Generator-style5x4-GPT2"：**
```python
# The link to the demo is at the bottom right corner of the Model Card.
import transformers
from transformers import pipeline
gpt2 = pipeline('text-generation', model = "Lifan-Z/Chinese-Classic-Poem-Generator-style5x4-GPT2")
sequences = gpt2('<|endoftext|>雨', max_length=26, do_sample=True, top_k=20, top_p=0.9, num_return_sequences=6, eos_token_id=0)
for seq in sequences:
print(seq)
Output:
{'generated_text': '<|endoftext|>雨 过 花 飞 蝶 ， 烟 笼 草 堕 云 。 却 羡 竹 间 月 ， 徘 徊 竹 外 山 。'}
{'generated_text': '<|endoftext|>雨 罢 山 村 晓 ， 晴 开 野 鸟 啼 。 夜 深 花 落 尽 ， 残 照 露 中 秋 。'}
{'generated_text': '<|endoftext|>雨 滴 江 南 岸 ， 风 高 日 暮 前 。 一 枝 春 水 绿 ， 万 叶 落 长 安 。'}
{'generated_text': '<|endoftext|>雨 余 风 满 地 ， 秋 后 日 斜 时 。 坐 来 无 一 事 ， 寒 食 独 无 言 。'}
{'generated_text': '<|endoftext|>雨 罢 秋 风 急 ， 风 回 晚 日 长 。 东 篱 有 佳 趣 ， 独 对 翠 屏 开 。'}
{'generated_text': '<|endoftext|>雨 中 花 落 去 ， 风 里 柳 飘 飞 。 相 思 何 必 梦 ， 知 君 亦 泪 垂 。'}
```
### **References:**
The model uploaded to HuggingFace: "yuanzhoulvpi/gpt2_chinese"
https://github.com/chinese-poetry/chinese-poetry/tree/master/%E5%85%A8%E5%94%90%E8%AF%97
|
Lifan-Z/Chinese-Classic-Poem-Generator-style5x8-GPT2
|
Lifan-Z
| 2024-01-29T01:47:43Z | 97 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"art",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-23T09:01:43Z |
---
license: apache-2.0
language:
- zh
tags:
- art
---
### **How to use "Lifan-Z/Chinese-Classic-Poem-Generator-style5x8-GPT2":**
```python
# The link to the demo is at the bottom right corner of the Model Card.
import transformers
from transformers import pipeline
gpt2 = pipeline('text-generation', model = "Lifan-Z/Chinese-Classic-Poem-Generator-style5x8-GPT2")
sequences = gpt2('<|endoftext|>雨', max_length=50, do_sample=True, top_k=20, top_p=0.9, num_return_sequences=6, eos_token_id=0)
for seq in sequences:
print(seq)
Output:
{'generated_text': '<|endoftext|>雨 过 山 声 远 ， 潮 来 水 势 平 。 风 和 渔 父 起 ， 日 澹 草 堂 明 。 月 上 云 添 画 ， 帆 移 浪 卷 城 。 无 言 归 路 好 ， 更 觉 旧 时 情 。'}
{'generated_text': '<|endoftext|>雨 后 山 中 景 ， 春 来 客 里 心 。 风 清 莺 不 语 ， 云 散 鸟 还 眠 。 落 照 摇 红 酒 ， 残 阳 照 紫 泥 。 故 人 无 此 趣 ， 相 送 又 东 寻 。'}
{'generated_text': '<|endoftext|>雨 余 天 影 半 ， 路 僻 出 阶 除 。 树 矗 山 扉 重 ， 溪 泠 水 汽 清 。 耕 夫 齐 舍 逐 ， 兵 卒 峻 庭 隆 。 何 必 投 吾 圃 ， 寻 根 傍 石 林 。'}
{'generated_text': '<|endoftext|>雨 足 风 声 恶 ， 秋 深 雾 气 清 。 野 花 生 未 谢 ， 江 雁 度 还 行 。 落 日 山 前 水 ， 残 阳 树 下 云 。 明 朝 去 留 远 ， 此 去 一 沾 巾 。'}
{'generated_text': '<|endoftext|>雨 歇 山 容 合 ， 江 晴 海 气 和 。 一 家 依 水 市 ， 三 径 入 云 林 。 风 物 今 如 许 ， 天 情 老 似 绵 。 清 溪 无 限 意 ， 聊 与 问 归 寻 。'}
{'generated_text': '<|endoftext|>雨 过 水 多 处 ， 林 开 竹 自 阴 。 石 穿 山 路 滑 ， 树 乱 涧 声 深 。 鸟 啄 泥 沙 溜 ， 人 归 草 木 丛 。 平 生 一 樽 酒 ， 何 日 重 相 寻 。'}
```
### **References:**
The model uploaded to HuggingFace: "yuanzhoulvpi/gpt2_chinese"
https://github.com/chinese-poetry/chinese-poetry/tree/master/%E5%85%A8%E5%94%90%E8%AF%97
|
Lifan-Z/Chinese-Classic-Poem-Generator-style7x4-GPT2
|
Lifan-Z
| 2024-01-29T01:47:06Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"art",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-19T23:56:40Z |
---
license: apache-2.0
language:
- zh
tags:
- art
---
### **How to use "Lifan-Z/Chinese-Classic-Poem-Generator-style7x4-GPT2":**
```python
# The link to the demo is at the bottom right corner of the Model Card.
import transformers
from transformers import pipeline
gpt2 = pipeline('text-generation', model = "Lifan-Z/Chinese-Classic-Poem-Generator-style7x4-GPT2")
sequences = gpt2('<|endoftext|>雨', max_length=34, do_sample=True, top_k=20, top_p=0.9, num_return_sequences=6, eos_token_id=0)
for seq in sequences:
print(seq)
Output:
{'generated_text': '<|endoftext|>雨 余 云 雾 满 江 湖 ， 风 动 山 城 月 正 浓 。 不 觉 人 来 无 限 景 ， 却 嫌 花 木 更 添 秋 。'}
{'generated_text': '<|endoftext|>雨 后 无 风 水 有 余 ， 秋 山 不 动 月 华 明 。 欲 看 白 云 归 客 梦 ， 一 段 闲 人 一 段 愁 。'}
{'generated_text': '<|endoftext|>雨 过 风 来 又 有 声 ， 小 窗 清 夜 梦 成 空 。 山 中 不 识 诗 人 面 ， 一 阵 寒 风 送 客 舟 。'}
{'generated_text': '<|endoftext|>雨 后 山 川 水 半 干 ， 风 前 竹 杖 小 桥 寒 。 不 知 道 在 溪 西 岸 ， 何 处 能 寻 杜 陵 花 。'}
{'generated_text': '<|endoftext|>雨 余 风 雨 一 番 晴 ， 不 见 花 时 满 面 愁 。 谁 道 山 林 不 能 住 ， 老 僧 犹 在 翠 微 中 。'}
{'generated_text': '<|endoftext|>雨 打 晴 窗 一 两 声 ， 空 蒙 细 草 绿 如 茵 。 山 家 未 见 梅 花 好 ， 只 有 春 风 一 曲 清 。'}
```
### **References:**
The model uploaded to HuggingFace: "yuanzhoulvpi/gpt2_chinese"
https://github.com/chinese-poetry/chinese-poetry/tree/master/%E5%85%A8%E5%94%90%E8%AF%97
|
Lifan-Z/Chinese-Classic-Poem-Generator-style7x8-GPT2
|
Lifan-Z
| 2024-01-29T01:46:27Z | 99 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"art",
"zh",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-24T23:25:49Z |
---
license: apache-2.0
language:
- zh
tags:
- art
---
### **How to use "Lifan-Z/Chinese-Classic-Poem-Generator-style7x8-GPT2":**
```python
# The link to the demo is at the bottom right corner of the Model Card.
import transformers
from transformers import pipeline
gpt2 = pipeline('text-generation', model = "Lifan-Z/Chinese-Classic-Poem-Generator-style7x8-GPT2")
sequences = gpt2('<|endoftext|>雨', max_length=66, do_sample=True, top_k=20, top_p=0.9, num_return_sequences=6, eos_token_id=0)
for seq in sequences:
print(seq)
Output:
{'generated_text': '<|endoftext|>雨 后 秋 阴 满 屋 风 ， 数 声 鸡 犬 隔 墙 中 。 已 无 余 事 惟 思 睡 ， 可 得 闲 时 却 掩 空 。 不 学 东 西 成 底 用 ， 但 知 世 态 即 吾 侬 。 明 朝 又 上 青 云 去 ， 更 把 茱 萸 送 我 公 。'}
{'generated_text': '<|endoftext|>雨 余 秋 色 已 萧 然 ， 又 见 西 湖 泛 绮 筵 。 白 发 老 人 惊 岁 晚 ， 红 颜 少 女 共 年 圆 。 诗 成 未 忍 离 三 径 ， 酒 尽 聊 须 借 一 樽 。 今 夕 相 思 应 更 好 ， 不 堪 重 作 醉 归 天 。'}
{'generated_text': '<|endoftext|>雨 过 天 津 风 自 萧 ， 晓 来 山 色 入 烟 霏 。 不 嫌 白 发 催 人 老 ， 且 看 黄 花 送 酒 归 。 已 遣 青 梅 开 小 径 ， 更 凭 红 日 上 清 晖 。 谁 言 万 里 无 由 到 ， 却 恨 新 秋 正 满 衣 。'}
{'generated_text': '<|endoftext|>雨 过 风 收 日 色 晴 ， 天 高 地 迥 晓 云 轻 。 山 川 气 象 元 无 极 ， 楼 阁 乾 坤 正 自 明 。 水 际 远 光 浮 画 栋 ， 天 边 飞 鸟 聚 行 营 。 诗 人 不 负 平 生 意 ， 应 许 吟 哦 醉 后 听 。'}
{'generated_text': '<|endoftext|>雨 声 渐 歇 水 如 丝 ， 天 气 初 收 日 正 迟 。 一 尺 野 田 秋 水 绿 ， 五 更 残 漏 夕 阳 微 。 山 连 古 井 人 皆 到 ， 路 隔 高 楼 客 自 归 。 惟 有 西 湖 无 限 好 ， 满 襟 春 色 与 谁 飞 。'}
{'generated_text': '<|endoftext|>雨 收 风 起 小 亭 开 ， 一 点 清 愁 满 面 来 。 万 壑 松 声 鸣 晓 漏 ， 千 岩 云 气 动 秋 苔 。 人 如 月 色 归 何 处 ， 天 似 江 光 落 后 回 。 且 把 此 生 闲 自 得 ， 不 须 强 笑 问 桃 李 。'}
```
### **References:**
The model uploaded to HuggingFace: "yuanzhoulvpi/gpt2_chinese"
https://github.com/chinese-poetry/chinese-poetry/tree/master/%E5%85%A8%E5%94%90%E8%AF%97
|
KizunaAE/KizunaAE_Model
|
KizunaAE
| 2024-01-29T01:46:19Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-01-25T19:23:39Z |
---
license: creativeml-openrail-m
---
|
gayanin/pubmed-mixed-noise-v3-0.2
|
gayanin
| 2024-01-29T01:45:28Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-29T00:48:03Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: pubmed-mixed-noise-v3-0.2
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. -->
# pubmed-mixed-noise-v3-0.2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4140
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7328 | 0.11 | 500 | 0.6952 |
| 0.6994 | 0.21 | 1000 | 0.6005 |
| 0.6684 | 0.32 | 1500 | 0.5670 |
| 0.6305 | 0.43 | 2000 | 0.5402 |
| 0.6224 | 0.54 | 2500 | 0.5175 |
| 0.6009 | 0.64 | 3000 | 0.5001 |
| 0.5126 | 0.75 | 3500 | 0.4935 |
| 0.5698 | 0.86 | 4000 | 0.4793 |
| 0.497 | 0.96 | 4500 | 0.4715 |
| 0.3716 | 1.07 | 5000 | 0.4689 |
| 0.4522 | 1.18 | 5500 | 0.4551 |
| 0.3958 | 1.28 | 6000 | 0.4556 |
| 0.4398 | 1.39 | 6500 | 0.4502 |
| 0.4572 | 1.5 | 7000 | 0.4425 |
| 0.4339 | 1.61 | 7500 | 0.4424 |
| 0.4289 | 1.71 | 8000 | 0.4322 |
| 0.3771 | 1.82 | 8500 | 0.4337 |
| 0.3668 | 1.93 | 9000 | 0.4265 |
| 0.3342 | 2.03 | 9500 | 0.4316 |
| 0.3465 | 2.14 | 10000 | 0.4244 |
| 0.32 | 2.25 | 10500 | 0.4226 |
| 0.3493 | 2.35 | 11000 | 0.4244 |
| 0.3549 | 2.46 | 11500 | 0.4216 |
| 0.3281 | 2.57 | 12000 | 0.4192 |
| 0.3259 | 2.68 | 12500 | 0.4181 |
| 0.3444 | 2.78 | 13000 | 0.4156 |
| 0.3201 | 2.89 | 13500 | 0.4146 |
| 0.3315 | 3.0 | 14000 | 0.4140 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
oGabrielFreitas/roberta-ufsm-qa
|
oGabrielFreitas
| 2024-01-29T01:38:20Z | 12 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-25T03:43:22Z |
---
tags:
- generated_from_trainer
model-index:
- name: roberta-ufsm-qa
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. -->
# roberta-ufsm-qa
This model was trained from scratch on the None 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: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
DaRkSpyro/FelipeRio2
|
DaRkSpyro
| 2024-01-29T01:23:42Z | 0 | 0 |
flair
|
[
"flair",
"music",
"en",
"dataset:HuggingFaceM4/WebSight",
"license:apache-2.0",
"region:us"
] | null | 2024-01-28T18:00:17Z |
---
license: apache-2.0
datasets:
- HuggingFaceM4/WebSight
language:
- en
metrics:
- accuracy
library_name: flair
tags:
- music
---
|
mlx-community/flan-t5-base-mlx-4bit
|
mlx-community
| 2024-01-29T01:19:03Z | 176 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2024-01-29T01:18:32Z |
[Google's Flan-T5 medium](https://huggingface.co/google/flan-t5-small) converted to [MLX](https://github.com/ml-explore/mlx-examples) format and quantized up to 4 bits with group size 64.
|
CultriX/Wernicke-7B-v8
|
CultriX
| 2024-01-29T01:16:11Z | 111 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"kaitchup/Mayonnaise-4in1-022",
"macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"vanillaOVO/supermario_v2",
"FelixChao/WestSeverus-7B-DPO-v2",
"base_model:PetroGPT/WestSeverus-7B-DPO-v2",
"base_model:merge:PetroGPT/WestSeverus-7B-DPO-v2",
"base_model:kaitchup/Mayonnaise-4in1-022",
"base_model:merge:kaitchup/Mayonnaise-4in1-022",
"base_model:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"base_model:merge:macadeliccc/WestLake-7B-v2-laser-truthy-dpo",
"base_model:vanillaOVO/supermario_v2",
"base_model:merge:vanillaOVO/supermario_v2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T22:06:13Z |
---
tags:
- merge
- mergekit
- lazymergekit
- kaitchup/Mayonnaise-4in1-022
- macadeliccc/WestLake-7B-v2-laser-truthy-dpo
- vanillaOVO/supermario_v2
- FelixChao/WestSeverus-7B-DPO-v2
base_model:
- kaitchup/Mayonnaise-4in1-022
- macadeliccc/WestLake-7B-v2-laser-truthy-dpo
- vanillaOVO/supermario_v2
- FelixChao/WestSeverus-7B-DPO-v2
license: apache-2.0
---
# Wernicke-7B-v8
Wernicke-7B-v8 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [kaitchup/Mayonnaise-4in1-022](https://huggingface.co/kaitchup/Mayonnaise-4in1-022)
* [macadeliccc/WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo)
* [vanillaOVO/supermario_v2](https://huggingface.co/vanillaOVO/supermario_v2)
* [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)
## 🧩 Configuration
```yaml
models:
- model: CultriX/Wernicke-7B-v1
# No parameters necessary for base model
- model: kaitchup/Mayonnaise-4in1-022
parameters:
density: 0.53
weight: 0.40
- model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
parameters:
density: 0.53
weight: 0.25
- model: vanillaOVO/supermario_v2
parameters:
density: 0.53
weight: 0.25
- model: FelixChao/WestSeverus-7B-DPO-v2
parameters:
density: 0.53
weight: 0.20
merge_method: dare_ties
base_model: CultriX/Wernicke-7B-v1
parameters:
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "CultriX/Wernicke-7B-v8"
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"])
```
|
Jackline/CodeLlama-Code-BG
|
Jackline
| 2024-01-29T01:15:51Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"region:us"
] | null | 2024-01-29T01:12:15Z |
---
library_name: peft
base_model: codellama/CodeLlama-7b-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]
- **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 Data 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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.1
|
Patcas/plbart-worksDocWorks-step-2
|
Patcas
| 2024-01-29T01:08:34Z | 90 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"plbart",
"text2text-generation",
"generated_from_trainer",
"base_model:Patcas/plbart-works",
"base_model:finetune:Patcas/plbart-works",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-29T00:46:41Z |
---
base_model: Patcas/plbart-works
tags:
- generated_from_trainer
model-index:
- name: plbart-worksDocWorks-step-2
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. -->
# plbart-worksDocWorks-step-2
This model is a fine-tuned version of [Patcas/plbart-works](https://huggingface.co/Patcas/plbart-works) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9441
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 230 | 1.0300 |
| No log | 2.0 | 460 | 0.9436 |
| 0.9846 | 3.0 | 690 | 0.9429 |
| 0.9846 | 4.0 | 920 | 0.9441 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
gayanin/pubmed-mixed-noise-v3-0.1
|
gayanin
| 2024-01-29T01:05:03Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-29T00:37:11Z |
---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: pubmed-mixed-noise-v3-0.1
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. -->
# pubmed-mixed-noise-v3-0.1
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2607
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.5222 | 0.11 | 500 | 0.4206 |
| 0.3862 | 0.21 | 1000 | 0.3907 |
| 0.4108 | 0.32 | 1500 | 0.3568 |
| 0.3871 | 0.43 | 2000 | 0.3415 |
| 0.3846 | 0.54 | 2500 | 0.3240 |
| 0.3313 | 0.64 | 3000 | 0.3124 |
| 0.3317 | 0.75 | 3500 | 0.3066 |
| 0.3136 | 0.86 | 4000 | 0.3049 |
| 0.3267 | 0.96 | 4500 | 0.2925 |
| 0.2816 | 1.07 | 5000 | 0.2929 |
| 0.2421 | 1.18 | 5500 | 0.2882 |
| 0.2643 | 1.28 | 6000 | 0.2872 |
| 0.2776 | 1.39 | 6500 | 0.2824 |
| 0.2854 | 1.5 | 7000 | 0.2751 |
| 0.2301 | 1.61 | 7500 | 0.2756 |
| 0.2118 | 1.71 | 8000 | 0.2770 |
| 0.2079 | 1.82 | 8500 | 0.2732 |
| 0.2474 | 1.93 | 9000 | 0.2631 |
| 0.1482 | 2.03 | 9500 | 0.2693 |
| 0.1908 | 2.14 | 10000 | 0.2656 |
| 0.2017 | 2.25 | 10500 | 0.2647 |
| 0.1687 | 2.35 | 11000 | 0.2680 |
| 0.191 | 2.46 | 11500 | 0.2630 |
| 0.1821 | 2.57 | 12000 | 0.2618 |
| 0.2301 | 2.68 | 12500 | 0.2605 |
| 0.2106 | 2.78 | 13000 | 0.2601 |
| 0.1637 | 2.89 | 13500 | 0.2617 |
| 0.1902 | 3.0 | 14000 | 0.2607 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.0.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
charleschen2022/zephyr-support-chatbot
|
charleschen2022
| 2024-01-29T00:59:31Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/zephyr-7B-alpha-GPTQ",
"base_model:finetune:TheBloke/zephyr-7B-alpha-GPTQ",
"license:mit",
"region:us"
] | null | 2024-01-29T00:54:08Z |
---
license: mit
base_model: TheBloke/zephyr-7B-alpha-GPTQ
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: zephyr-support-chatbot
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. -->
# zephyr-support-chatbot
This model is a fine-tuned version of [TheBloke/zephyr-7B-alpha-GPTQ](https://huggingface.co/TheBloke/zephyr-7B-alpha-GPTQ) on the None 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: 0.0002
- 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: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
kwaikeg/kagentlms_qwen_14b_mat
|
kwaikeg
| 2024-01-29T00:53:29Z | 11 | 1 |
transformers
|
[
"transformers",
"pytorch",
"text-generation",
"en",
"zh",
"dataset:kwaikeg/KAgentInstruct",
"dataset:kwaikeg/KAgentBench",
"license:cc-by-nc-nd-4.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-29T00:28:25Z |
---
license: cc-by-nc-nd-4.0
datasets:
- kwaikeg/KAgentInstruct
- kwaikeg/KAgentBench
language:
- en
- zh
pipeline_tag: text-generation
---
KwaiAgents ([Github](https://github.com/KwaiKEG/KwaiAgents)) is a series of Agent-related works open-sourced by the [KwaiKEG](https://github.com/KwaiKEG) from [Kuaishou Technology](https://www.kuaishou.com/en). The open-sourced content includes:
1. **KAgentSys-Lite**: An experimental Agent Loop implemented based on open-source search engines, browsers, time, calendar, weather, and other tools, which is only missing the memory mechanism and some search capabilities compared to the system in the paper.
2. **KAgentLMs**: A series of large language models with Agent capabilities such as planning, reflection, and tool-use, acquired through the Meta-agent tuning proposed in the paper.
3. **KAgentInstruct**: Fine-tuned data of instructions generated by the Meta-agent in the paper.
4. **KAgentBench**: Over 3,000 human-edited, automated evaluation data for testing Agent capabilities, with evaluation dimensions including planning, tool-use, reflection, concluding, and profiling.
## User Guide
### Direct usage
Tutorial can refer to [QwenLM/Qwen](https://github.com/QwenLM/Qwen)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
tokenizer = AutoTokenizer.from_pretrained("kwaikeg/kagentlms_qwen_7b_mat", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
"kwaikeg/kagentlms_qwen_14b_mat",
device_map="auto",
trust_remote_code=True
).eval()
response, history = model.chat(tokenizer, "你好", history=None)
print(response)
```
### AgentLMs as service
We recommend using [vLLM](https://github.com/vllm-project/vllm) and [FastChat](https://github.com/lm-sys/FastChat) to deploy the model inference service. First, you need to install the corresponding packages (for detailed usage, please refer to the documentation of the two projects):
```bash
pip install vllm
pip install "fschat[model_worker,webui]"
```
To deploy KAgentLMs, you first need to start the controller in one terminal.
```bash
python -m fastchat.serve.controller
```
Secondly, you should use the following command in another terminal for single-gpu inference service deployment:
```bash
python -m fastchat.serve.vllm_worker --model-path $model_path --trust-remote-code
```
Where `$model_path` is the local path of the model downloaded. If the GPU does not support Bfloat16, you can add `--dtype half` to the command line.
Thirdly, start the REST API server in the third terminal.
```bash
python -m fastchat.serve.openai_api_server --host localhost --port 8888
```
Finally, you can use the curl command to invoke the model same as the OpenAI calling format. Here's an example:
```bash
curl http://localhost:8888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{"model": "kagentlms_qwen_7b_mat", "messages": [{"role": "user", "content": "Who is Andy Lau"}]}'
```
### Citation
```
@article{pan2023kwaiagents,
author = {Haojie Pan and
Zepeng Zhai and
Hao Yuan and
Yaojia Lv and
Ruiji Fu and
Ming Liu and
Zhongyuan Wang and
Bing Qin
},
title = {KwaiAgents: Generalized Information-seeking Agent System with Large Language Models},
journal = {CoRR},
volume = {abs/2312.04889},
year = {2023}
}
```
|
majed316/jais-13b-chat-4bit
|
majed316
| 2024-01-29T00:52:25Z | 37 | 0 |
transformers
|
[
"transformers",
"safetensors",
"jais",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-28T00:03:39Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
weijie210/zephyr-critique-7b-score
|
weijie210
| 2024-01-29T00:43:34Z | 8 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:generator",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"base_model:finetune:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-27T18:00:35Z |
---
license: apache-2.0
base_model: alignment-handbook/zephyr-7b-sft-full
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
model-index:
- name: zephyr-critique-7b-score
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. -->
# zephyr-critique-7b-score
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5010
## 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: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4995 | 1.0 | 804 | 0.5010 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.0.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.0
|
MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-29T00:33:04Z | 49 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"maywell/Mistral-ko-7B-v0.1",
"pytorch",
"ko",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"license:apache-2.0",
"base_model:MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-29T00:22:14Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- maywell/Mistral-ko-7B-v0.1
- pytorch
- ko
- license:cc-by-nc-4.0
- autotrain_compatible
- endpoints_compatible
- region:us
- license:apache-2.0
model_name: Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-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 MaziyarPanahi/Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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 32768` 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="./Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # 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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./Mistral-ko-7B-v0.1-Mistral-7B-Instruct-v0.1-GGUF.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)
|
ndanielsen/MotorHead-Mistral-7B-v0.1
|
ndanielsen
| 2024-01-29T00:29:32Z | 61 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-28T18:09:52Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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.
python```
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
base_model_id = "ndanielsen/MotorHead-Mistral-7B-v0.1"
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16
)
base_model = AutoModelForCausalLM.from_pretrained(
base_model_id,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True,
)
eval_tokenizer = AutoTokenizer.from_pretrained(base_model_id, add_bos_token=True, trust_remote_code=True)
```
[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]
|
devjwsong/ppo-LunarLander-v2
|
devjwsong
| 2024-01-29T00:21:48Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T00:21:30Z |
---
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: 265.29 +/- 21.13
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
...
```
|
LoneStriker/Tess-34B-v1.5b-8.0bpw-h8-exl2
|
LoneStriker
| 2024-01-29T00:10:47Z | 2 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T23:55:47Z |
---
license: other
license_name: yi-34b
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
---
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-34B-v1.5b was trained on the Yi-34B-200K base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
eioi34j38943/admffi
|
eioi34j38943
| 2024-01-29T00:02:16Z | 0 | 0 | null |
[
"ja",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"license:artistic-2.0",
"region:us"
] | null | 2024-01-28T23:59:37Z |
---
license: artistic-2.0
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
language:
- ja
---
|
RadG/code-llama-7b-text-to-sql
|
RadG
| 2024-01-28T23:58:57Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-01-28T07:11:10Z |
---
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: codellama/CodeLlama-7b-hf
model-index:
- name: code-llama-7b-text-to-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. -->
# code-llama-7b-text-to-sql
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator 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: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-28T23:55:48Z | 64 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-28T23:45:12Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- Weyaxi/MetaMath-Chupacabra-7B-v2.01-Slerp
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-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 MaziyarPanahi/MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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 32768` 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="./MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # 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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./MetaMath-Chupacabra-7B-v2.01-Slerp-Mistral-7B-Instruct-v0.1-GGUF.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)
|
backnotprop/np_cr_model5
|
backnotprop
| 2024-01-28T23:52:31Z | 24 | 1 |
diffusers
|
[
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] |
text-to-image
| 2024-01-28T23:33:41Z |
---
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
- template:sd-lora
widget:
- text: 'spiral wave flower,minimalism,white_background,abstract,photoshop generated abstract on a white background'
output:
url:
"image_0.png"
- text: 'spiral wave flower,minimalism,white_background,abstract,photoshop generated abstract on a white background'
output:
url:
"image_1.png"
- text: 'spiral wave flower,minimalism,white_background,abstract,photoshop generated abstract on a white background'
output:
url:
"image_2.png"
- text: 'spiral wave flower,minimalism,white_background,abstract,photoshop generated abstract on a white background'
output:
url:
"image_3.png"
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: something,minimalism,white_background,abstract,photoshop generated abstract on a white background
license: openrail++
---
# SDXL LoRA DreamBooth - backnotprop/np_cr_model5
<Gallery />
## Model description
### These are backnotprop/np_cr_model5 LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`np_cr_model5.safetensors` here 💾](/backnotprop/np_cr_model5/blob/main/np_cr_model5.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:np_cr_model5:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`np_cr_model5_emb.safetensors` here 💾](/backnotprop/np_cr_model5/blob/main/np_cr_model5_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `np_cr_model5_emb` to your prompt. For example, `something,minimalism,white_background,abstract,photoshop generated abstract on a white background`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('backnotprop/np_cr_model5', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='backnotprop/np_cr_model5', filename='np_cr_model5_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=[], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=[], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('spiral wave flower,minimalism,white_background,abstract,photoshop generated abstract on a white background').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/backnotprop/np_cr_model5/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
TheBloke/Tess-34B-v1.5b-GPTQ
|
TheBloke
| 2024-01-28T23:50:50Z | 26 | 7 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"base_model:migtissera/Tess-34B-v1.5b",
"base_model:quantized:migtissera/Tess-34B-v1.5b",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-01-28T20:44:28Z |
---
base_model: migtissera/Tess-34B-v1.5b
inference: false
license: other
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
license_name: yi-34b
model_creator: Migel Tissera
model_name: Tess 34B V1.5B
model_type: yi
prompt_template: 'SYSTEM: {system_message}
USER: {prompt}
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 -->
# Tess 34B V1.5B - GPTQ
- Model creator: [Migel Tissera](https://huggingface.co/migtissera)
- Original model: [Tess 34B V1.5B](https://huggingface.co/migtissera/Tess-34B-v1.5b)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Migel Tissera's Tess 34B V1.5B](https://huggingface.co/migtissera/Tess-34B-v1.5b).
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/Tess-34B-v1.5b-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF)
* [Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/migtissera/Tess-34B-v1.5b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Orca-Vicuna
```
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/Tess-34B-v1.5b-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 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/Tess-34B-v1.5b-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 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/Tess-34B-v1.5b-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 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/Tess-34B-v1.5b-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 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/Tess-34B-v1.5b-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 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/Tess-34B-v1.5b-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 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/Tess-34B-v1.5b-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 8192 | 36.12 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/Tess-34B-v1.5b-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Tess-34B-v1.5b-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 `Tess-34B-v1.5b-GPTQ`:
```shell
mkdir Tess-34B-v1.5b-GPTQ
huggingface-cli download TheBloke/Tess-34B-v1.5b-GPTQ --local-dir Tess-34B-v1.5b-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Tess-34B-v1.5b-GPTQ
huggingface-cli download TheBloke/Tess-34B-v1.5b-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir Tess-34B-v1.5b-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 Tess-34B-v1.5b-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Tess-34B-v1.5b-GPTQ --local-dir Tess-34B-v1.5b-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/Tess-34B-v1.5b-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/Tess-34B-v1.5b-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Tess-34B-v1.5b-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: `Tess-34B-v1.5b-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/Tess-34B-v1.5b-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/Tess-34B-v1.5b-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'''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: Migel Tissera's Tess 34B V1.5B
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-34B-v1.5b was trained on the Yi-34B-200K base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
fionazhang/fine-tune-mistral-environment
|
fionazhang
| 2024-01-28T23:49:35Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-01-28T23:13:29Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: fine-tune-mistral-environment
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. -->
# fine-tune-mistral-environment
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0377
## 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: 3
- eval_batch_size: 3
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.0a0+git7bcf7da
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Xianjun/Quokka-13b-instruct
|
Xianjun
| 2024-01-28T23:40:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2401.01089",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T22:53:12Z |
---
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This model is optimized for Material Science by continuing pertaining on over 1 million Material science academic articles based on LLaMa-2-13b. And further finetuned on materials science instructions.
- **Developed by:** [UCSB]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [LLaMa-2]
- **Paper [optional]:** [https://arxiv.org/pdf/2401.01089.pdf]
- **Demo [optional]:** [More Information Needed]
## How to Get Started with the Model
```python
from transformers import LlamaTokenizer, LlamaForCausalLM
import torch
tokenizer = LlamaTokenizer.from_pretrained("Xianjun/Quokka-13b-instruct ")
model = LlamaForCausalLM.from_pretrained("Xianjun/Quokka-13b-instruct ").half().to("cuda")
instruction = "How to ..."
batch = tokenizer(instruction, return_tensors="pt", add_special_tokens=False).to("cuda")
with torch.no_grad():
output = model.generate(**batch, max_new_tokens=512, temperature=0.7, do_sample=True)
response = tokenizer.decode(output[0], skip_special_tokens=True)
```
## Citation
If you find Quokka useful in your research, please cite the following paper:
```latex
@inproceedings{Yang2024QuokkaAO,
title={Quokka: An Open-source Large Language Model ChatBot for Material Science},
author={Xianjun Yang and Stephen Wilson and Linda Ruth Petzold},
year={2024},
url={https://api.semanticscholar.org/CorpusID:266725577}
}
```
|
ielabgroup/vec2text_gtr-base-st_corrector
|
ielabgroup
| 2024-01-28T23:36:25Z | 90 | 0 |
transformers
|
[
"transformers",
"safetensors",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-01-28T03:59:18Z |
---
license: apache-2.0
---
To use the model, check vec2text repo [https://github.com/jxmorris12/vec2text](https://github.com/jxmorris12/vec2text)
# Example:
```python
from sentence_transformers import SentenceTransformer
import vec2text
import transformers
inversion_model = vec2text.models.InversionModel.from_pretrained(
"ielabgroup/vec2text_gtr-base-st_inversion"
)
model = vec2text.models.CorrectorEncoderModel.from_pretrained(
"ielabgroup/vec2text_gtr-base-st_corrector"
)
inversion_trainer = vec2text.trainers.InversionTrainer(
model=inversion_model,
train_dataset=None,
eval_dataset=None,
data_collator=transformers.DataCollatorForSeq2Seq(
inversion_model.tokenizer,
label_pad_token_id=-100,
),
)
model.config.dispatch_batches = None
corrector = vec2text.trainers.Corrector(
model=model,
inversion_trainer=inversion_trainer,
args=None,
data_collator=vec2text.collator.DataCollatorForCorrection(
tokenizer=inversion_trainer.model.tokenizer
),
)
model = SentenceTransformer('sentence-transformers/gtr-t5-base')
embeddings = model.encode([
"Jack Morris is a PhD student at Cornell Tech in New York City",
"It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity"
], convert_to_tensor=True,).to('mps')
vec2text.invert_embeddings(
embeddings=embeddings,
corrector=corrector,
num_steps=20,
)
[' Jack Morris is a PhD student at Cornell Tech in New York', 'It was the best of times, it was the worst of times, it was the epoch of incredulity, it was age of']
```
|
ielabgroup/vec2text_gtr-base-st_inversion
|
ielabgroup
| 2024-01-28T23:35:41Z | 2,543 | 2 |
transformers
|
[
"transformers",
"safetensors",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-01-28T03:46:53Z |
---
license: apache-2.0
---
To use the model, check vec2text repo [https://github.com/jxmorris12/vec2text](https://github.com/jxmorris12/vec2text)
# Example:
```python
from sentence_transformers import SentenceTransformer
import vec2text
import transformers
inversion_model = vec2text.models.InversionModel.from_pretrained(
"ielabgroup/vec2text_gtr-base-st_inversion"
)
model = vec2text.models.CorrectorEncoderModel.from_pretrained(
"ielabgroup/vec2text_gtr-base-st_corrector"
)
inversion_trainer = vec2text.trainers.InversionTrainer(
model=inversion_model,
train_dataset=None,
eval_dataset=None,
data_collator=transformers.DataCollatorForSeq2Seq(
inversion_model.tokenizer,
label_pad_token_id=-100,
),
)
model.config.dispatch_batches = None
corrector = vec2text.trainers.Corrector(
model=model,
inversion_trainer=inversion_trainer,
args=None,
data_collator=vec2text.collator.DataCollatorForCorrection(
tokenizer=inversion_trainer.model.tokenizer
),
)
model = SentenceTransformer('sentence-transformers/gtr-t5-base')
embeddings = model.encode([
"Jack Morris is a PhD student at Cornell Tech in New York City",
"It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity"
], convert_to_tensor=True,).to('mps')
vec2text.invert_embeddings(
embeddings=embeddings,
corrector=corrector,
num_steps=20,
)
[' Jack Morris is a PhD student at Cornell Tech in New York', 'It was the best of times, it was the worst of times, it was the epoch of incredulity, it was age of']
```
|
realPCH/kosolra-wiki-QA-1epoch
|
realPCH
| 2024-01-28T23:13:01Z | 61 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:maywell/ko_wikidata_QA",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-22T23:30:43Z |
---
license: mit
datasets:
- maywell/ko_wikidata_QA
---
### Developed by chPark
### Training Strategy
We fine-tuned this model based on [yanolja/KoSOLAR-10.7B-v0.1](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.1-deprecated) with [kyujinpy/KOR-gugugu-platypus-set](https://huggingface.co/datasets/kyujinpy/KOR-gugugu-platypus-set)
### Run the model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "realPCH/ko_solra_merge"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
text = "[INST] Put instruction here. [/INST]"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=20)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
taku-yoshioka/rlhf-line-marcja
|
taku-yoshioka
| 2024-01-28T23:05:03Z | 0 | 1 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2024-01-28T23:05:00Z |
---
license: apache-2.0
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="taku-yoshioka//tmp/tmpyovnmlh5/taku-yoshioka/rlhf-line-marcja")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("taku-yoshioka//tmp/tmpyovnmlh5/taku-yoshioka/rlhf-line-marcja")
model = AutoModelForCausalLMWithValueHead.from_pretrained("taku-yoshioka//tmp/tmpyovnmlh5/taku-yoshioka/rlhf-line-marcja")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
rambaldi47/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
|
rambaldi47
| 2024-01-28T23:04:09Z | 128 | 0 |
transformers
|
[
"transformers",
"safetensors",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:MIT/ast-finetuned-audioset-10-10-0.4593",
"base_model:finetune:MIT/ast-finetuned-audioset-10-10-0.4593",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-01-28T20:44:29Z |
---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-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: 0.95
---
<!-- 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. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2195
- Accuracy: 0.95
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 7
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2848 | 1.0 | 225 | 0.9028 | 0.69 |
| 0.4115 | 2.0 | 450 | 0.4838 | 0.82 |
| 0.0998 | 3.0 | 675 | 0.7073 | 0.85 |
| 0.0733 | 4.0 | 900 | 0.2571 | 0.91 |
| 0.0007 | 5.0 | 1125 | 0.5134 | 0.9 |
| 0.0001 | 6.0 | 1350 | 0.2031 | 0.95 |
| 0.0001 | 7.0 | 1575 | 0.2195 | 0.95 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.0
|
yuansiwe/mistral_instruct_generation
|
yuansiwe
| 2024-01-28T22:59:51Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-01-28T22:59:32Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
model-index:
- name: mistral_instruct_generation
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_instruct_generation
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: 1.3407
## 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: 0.0002
- 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.561 | 0.16 | 20 | 1.4006 |
| 1.4313 | 0.33 | 40 | 1.3646 |
| 1.446 | 0.49 | 60 | 1.3534 |
| 1.438 | 0.65 | 80 | 1.3453 |
| 1.4122 | 0.81 | 100 | 1.3407 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.7
- Tokenizers 0.14.1
|
Mattttthew/dqn-SpaceInvadersNoFrameskip-v4
|
Mattttthew
| 2024-01-28T22:56:18Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-28T22:55:52Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 329.00 +/- 157.97
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Mattttthew -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Mattttthew -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Mattttthew
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
birgermoell/swedish-gpt-merged
|
birgermoell
| 2024-01-28T22:54:03Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/NeuralBeagle14-7B",
"base_model:mlabonne/NeuralBeagle14-7B",
"base_model:finetune:mlabonne/NeuralBeagle14-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T22:49:12Z |
---
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/NeuralBeagle14-7B
base_model:
- mlabonne/NeuralBeagle14-7B
---
# swedish-gpt-merged
swedish-gpt-merged is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)
## 🧩 Configuration
```yaml
models:
- model: danish-foundation-models/munin-7b-alpha
# No parameters necessary for base model
- model: mlabonne/NeuralBeagle14-7B
parameters:
density: 0.53
weight: 0.6
merge_method: dare_ties
base_model: danish-foundation-models/munin-7b-alpha
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "birgermoell/swedish-gpt-merged"
model = "birgermoell/gpt-sw3-6.7b-v2-instruct-merge"
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"])
```
|
mogaio/Snorkel-Mistral-PairRM-DPO-Freakonomics_MTD-TCD_v2
|
mogaio
| 2024-01-28T22:52:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-28T22:51:35Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
jingyeom/SOLAR_KO_1.3_deup
|
jingyeom
| 2024-01-28T22:48:57Z | 1,379 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-16T07:49:12Z |
---
license: apache-2.0
---
## Model
base_model : beomi/OPEN-SOLAR-KO-10.7B
## Dataset
* 공개 데이터 수집
* Deduplicating Training Data Makes Language Models Better 알고리즘 활용
## Code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "jingyeom/SOLAR_KO_1.3_deup"
model = AutoModelForCausalLM.from_pretrained(
model_name,
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Benchmark
**[Ko-LLM-Leaderboard](https://huggingface.co/spaces/upstage/open-ko-llm-leaderboard)**
(24.01.29 기준 리더보드 11등)
| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
| 53.63 | 52.65 | 60.92 | 50.9 | 45.14 | 58.56 |
|
TunahanGokcimen/conv-bert-base
|
TunahanGokcimen
| 2024-01-28T22:37:43Z | 92 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"convbert",
"token-classification",
"generated_from_trainer",
"base_model:YituTech/conv-bert-base",
"base_model:finetune:YituTech/conv-bert-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-28T22:00:41Z |
---
base_model: YituTech/conv-bert-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: conv-bert-base
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. -->
# conv-bert-base
This model is a fine-tuned version of [YituTech/conv-bert-base](https://huggingface.co/YituTech/conv-bert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2024
- Precision: 0.7686
- Recall: 0.8278
- F1: 0.7971
- Accuracy: 0.9376
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2235 | 1.0 | 2078 | 0.2225 | 0.7307 | 0.7996 | 0.7636 | 0.9301 |
| 0.1814 | 2.0 | 4156 | 0.1946 | 0.7539 | 0.8257 | 0.7881 | 0.9363 |
| 0.1469 | 3.0 | 6234 | 0.2024 | 0.7686 | 0.8278 | 0.7971 | 0.9376 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Dans-DiscountModels/TinyMistral-v2.5-MiniPile-Guidelines-E1
|
Dans-DiscountModels
| 2024-01-28T22:34:56Z | 88 | 2 |
transformers
|
[
"transformers",
"pytorch",
"mistral",
"text-generation",
"generated_from_trainer",
"en",
"dataset:JeanKaddour/minipile",
"dataset:epfl-llm/guidelines",
"base_model:Locutusque/TinyMistral-248M-v2.5",
"base_model:finetune:Locutusque/TinyMistral-248M-v2.5",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T22:27:16Z |
---
tags:
- generated_from_trainer
base_model: Locutusque/TinyMistral-248M-v2.5
model-index:
- name: TinyMistral-v2.5-MiniPile-Guidelines-E1/
results: []
datasets:
- JeanKaddour/minipile
- epfl-llm/guidelines
license: apache-2.0
language:
- en
---
<!-- 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. -->
[<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)
<details><summary>See axolotl config</summary>
axolotl version: `0.3.0`
```yaml
base_model: Locutusque/TinyMistral-248M-v2.5
model_type: MistralForCausalLM
is_mistral_derived_model: true
load_in_8bit: false
load_in_4bit: false
strict: false
dataset_processes: 20
datasets:
- path: epfl-llm/guidelines
type: completion
field: clean_text
- path: JeanKaddour/minipile
type: completion
field: text
dataset_prepared_path: TinyMistral-FFT-data
val_set_size: 0.001
output_dir: ./TinyMistral-FFT
sequence_len: 2048
sample_packing: false
pad_to_sequence_len: true
adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:
# wandb configuration
wandb_project: TinyMistral-FFT
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: constant
cosine_min_lr_ratio:
learning_rate: 0.00005
train_on_inputs: true
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints: True
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true
warmup_steps: 10
evals_per_epoch: 100
# eval_steps: 10
eval_table_size:
saves_per_epoch: 50
debug:
deepspeed: #deepspeed/zero2.json # multi-gpu only
weight_decay: 0
# tokens:
special_tokens:
bos_token: "<|bos|>"
eos_token: "<|endoftext|>"
unk_token: "<unk>"
```
</details><br>
# TinyMistral-StructureEvaluator
This model was further trained on the epfl-llm/guidelines and JeanKaddour/minipile datasets for 1 epoch.
## 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: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 197279
### Training results
### Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
stillerman/magic-starcoder
|
stillerman
| 2024-01-28T22:32:23Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"gpt_bigcode",
"generated_from_trainer",
"base_model:bigcode/starcoder",
"base_model:adapter:bigcode/starcoder",
"license:bigcode-openrail-m",
"region:us"
] | null | 2024-01-28T22:31:59Z |
---
license: bigcode-openrail-m
library_name: peft
tags:
- generated_from_trainer
base_model: bigcode/starcoder
model-index:
- name: lora-out
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. -->
[<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)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: bigcode/starcoder # this can be swapped for mdel model when the model is released
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
is_llama_derived_model: false
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: /workspace/axolotl-mdel/mtg.txt # change this to where your dataset is
type: completion # change this to 'alpaca' if you are using alpaca
lora_modules_to_save:
- embed_tokens
- lm_head
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./lora-out
sequence_len: 4096 # this can be tweaked for efficiency
sample_packing: true
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: mtg-starcoder-experiement-cleaner # give this a name
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2 # this can be tweaked for efficiency
micro_batch_size: 1 # this can be tweaked for efficiency
num_epochs: 1 # this can be experimented with
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: true
group_by_length: false
bf16: true
fp16: false
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false #true
s2_attention:
warmup_steps: 10 # this can be tweaked for efficiency
evals_per_epoch: 10 # this can be tweaked for efficiency
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
pad_token: "<|endoftext|>" # I need to talk with Huu/Taishi about this
eos_token: "<|endoftext|>"
```
</details><br>
# lora-out
This model is a fine-tuned version of [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7371
## 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: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0386 | 0.0 | 1 | 3.7331 |
| 1.8941 | 0.1 | 25 | 1.6178 |
| 1.0615 | 0.21 | 50 | 0.9739 |
| 0.9228 | 0.31 | 75 | 0.8470 |
| 0.8614 | 0.41 | 100 | 0.8104 |
| 0.8562 | 0.52 | 125 | 0.7776 |
| 0.7939 | 0.62 | 150 | 0.7530 |
| 0.7714 | 0.73 | 175 | 0.7430 |
| 0.7999 | 0.83 | 200 | 0.7389 |
| 0.8647 | 0.93 | 225 | 0.7371 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.37.0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Patcas/plbart-worksNoDocWorks-step-1
|
Patcas
| 2024-01-28T22:32:14Z | 91 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"plbart",
"text2text-generation",
"generated_from_trainer",
"base_model:Patcas/plbart-works",
"base_model:finetune:Patcas/plbart-works",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-28T22:07:05Z |
---
base_model: Patcas/plbart-works
tags:
- generated_from_trainer
model-index:
- name: plbart-worksNoDocWorks-step-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. -->
# plbart-worksNoDocWorks-step-1
This model is a fine-tuned version of [Patcas/plbart-works](https://huggingface.co/Patcas/plbart-works) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9413
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 230 | 1.0354 |
| No log | 2.0 | 460 | 0.9595 |
| 0.9909 | 3.0 | 690 | 0.9495 |
| 0.9909 | 4.0 | 920 | 0.9413 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
AzureBlack/KitchenSink_103b-3.5bpw-6h-exl2
|
AzureBlack
| 2024-01-28T22:27:29Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"rp",
"erp",
"chat",
"storywriting",
"en",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T21:23:02Z |
---
license: llama2
language:
- en
tags:
- rp
- erp
- chat
- storywriting
---
# Kitchen Sink 103b
This model is a rotating-stack merge of three 70b models in a 103b (120 layer) configuration inspired by Venus 103b. The result of
this "frankenmerge" is a large model that contains a little bit of everything - including the kitchen sink. RP, chat, storywriting,
and instruct are all well supported. It may or may not code well - I lack the expertise to test it in that capacity, but considering
the source models, I suspect it is unlikely.
Component models for the rotating stack are
- royallab/Aetheria-L2-70B
- lizpreciatior/lzlv_70b_fp16_hf
- Sao10K/WinterGoddess-1.4x-70B-L2
Components of those models are purported to include: Nous-Hermes-Llama2-70b, Xwin-LM-7B-V0.1, Mythospice-70b, Euryale-1.3-L2-70B,
tulu-2-dpo-70b, GOAT-70B-Storytelling, Platypus2-70B-instruct, Lila-70B, SunsetBoulevard, and some private LoRAs.
As all components are based on Llama2 70b, native context length is 4k tokens. Coherency out to 8k is extremely good with rope scaling,
but starts to decline beyond that.
This model is uncensored and perfectly capable of generating objectionable material. However, it is not an explicitely-NSFW model,
and it has never "gone rogue" and tried to insert NSFW content into SFW prompts in my experience. As with any LLM, no factual claims
made by the model should be taken at face value. You know that boilerplate safety disclaimer that most professional models have?
Assume this has it too. This model is for entertainment purposes only.
FP16 and Q4_K_S GGUFs are located here: https://huggingface.co/MarsupialAI/KitchenSink_103b_GGUF_Q4KS_FP16
If you create additional quants, please let me know and I will link them here as well.
# Sample output
```
{{[INPUT]}}
Write a detailed and humorous story about a cute and fluffy bunny that goes to a Gwar concert.
{{[OUTPUT]}}
Once upon a time, in a magical land called Fluffyville, there lived a cute and fluffy little bunny named Fuzzlesworth. Fuzzlesworth
was your average, everyday bunny; he hopped around, nibbled on carrots, and generally lived a quiet, peaceful life. However,
Fuzzlesworth had a secret: he was a closet metalhead. When no one else was around, he would crank up his tiny bunny-sized speakers
and rock out to the loudest, most aggressive metal music he could find. His favorite band of all time was Gwar, the infamous
intergalactic warriors who sprayed fake blood and played insane heavy metal.
One day, Fuzzlesworth stumbled upon a flyer nailed to a tree trunk in the meadow that announced that Gwar would be performing at a
nearby human concert venue. His fluffy little bunny heart skipped a beat! He simply had to go! But, he knew his friends and family
would never understand his love for such an aggressive and intense music, so he decided to keep his plan a secret.
The big night finally arrived, and Fuzzlesworth, dressed in his best leather bunny-sized studded vest and spiked collar, hopped
his way to the concert venue. He stood outside the door, shaking with excitement and nervousness. The line of people waiting to
enter was intimidating: there were tall, muscular men and women covered in spikes, chains, and tattoos, and Fuzzlesworth felt like
a small, fluffy speck in a sea of metal.
But Fuzzlesworth was determined to see his favorite band, so he summoned up his bunny-sized courage and hopped to the front of the
line. The bouncer, a hulking behemoth of a man, looked down at Fuzzlesworth and raised a thick, hairy eyebrow.
"Umm...I'm here for the Gwar concert," Fuzzlesworth squeaked, holding up his ticket.
The bouncer stared at him incredulously for a moment, then let out a hearty laugh. "Aw, look at the little bunny in his metal gear!
Alright, go on in, but stay out of trouble, you hear?"
Fuzzlesworth's tail was wagging so hard, it could've powered a small wind turbine. He bounced his way inside the venue, taking in
the sights and sounds of the raucous crowd. The air was thick with the scent of sweat, cheap beer, and dry-ice smoke. He found a
small nook near the stage where he could see everything and waited for the show to start.
Finally, the lights dimmed, and the band took the stage. Fuzzlesworth's eyes went wide as the members of Gwar, adorned in their
grotesque, otherworldly costumes, began to thrash and wail on their instruments. He couldn't believe his fluffy little ears! He
was actually at a Gwar concert!
As the set went on, Fuzzlesworth got more and more into the music, headbanging and thrashing around in his own adorable bunny way.
The other concertgoers couldn't help but smile and chuckle at the sight of the tiny bunny moshing it up in the front row.
During one particularly intense song, Fuzzlesworth found himself lifted up by the mosh pit and deposited on stage. He looked out
at the roaring crowd, his little bunny heart pounding. In a moment of metal madness, he jumped up on the speaker stack and began
to shred an air-guitar solo, much to the amusement of the band and audience alike.
At the end of the set, the band's lead singer, Oderus Urungus, spotted Fuzzlesworth and motioned him over. Fuzzlesworth, overcome
with joy, hopped over and was lifted up onto the frontman's massive, spiked shoulder.
"Ladies and gentlemen," Oderus bellowed into the microphone, "I'd like to introduce our special guest tonight! Give it up for
Fuzzlesworth the Metal Bunny!"
The crowd went wild, cheering and clapping as Fuzzlesworth soaked up the attention. He had never felt so alive!
After the show, Fuzzlesworth got to meet the band, posing for photos and getting autographs. As he hopped back home that night,
his head still buzzing with metal and excitement, he knew that his life would never be the same. He was no longer just a small,
fluffy bunny from Fluffyville; he was Fuzzlesworth the Metal Bunny, and he wasn't afraid to rock out!
```
# Prompt format
Seems to have the strongest affinity for Alpaca prompts, but Vicuna works as well. Considering the variety of components, most
formats will probbaly work to some extent.
# WTF is a rotating-stack merge?
Inspired by Undi's experiments with stacked merges, Jeb Carter found that output quality and model initiative could be significantly
improved by reversing the model order in the stack, and then doing a linear merge between the original and reversed stacks. That is
what I did here. I created three passthrough stacked merges using the three source models (rotating the model order in each stack),
and then doing a linear merge of all three stacks. The exact merge configs can be found in the recipe.txt file.
|
mogaio/Snorkel-Mistral-PairRM-DPO-Freakonomics_MTD-TCD
|
mogaio
| 2024-01-28T22:25:53Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-28T22:25:51Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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
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[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
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
### Results
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#### 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]
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|
adalib/colossalai-data-codegen-2B-mono-prefix
|
adalib
| 2024-01-28T22:17:39Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Salesforce/codegen-2B-mono",
"base_model:adapter:Salesforce/codegen-2B-mono",
"region:us"
] | null | 2024-01-28T22:17:36Z |
---
library_name: peft
base_model: Salesforce/codegen-2B-mono
---
# 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]
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- **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
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
andrewatef/MyBloggerV0.22-main
|
andrewatef
| 2024-01-28T22:14:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-28T22:14:43Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
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[More Information Needed]
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[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
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#### 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
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[More Information Needed]
#### Metrics
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[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]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
|
hariqueen/code-llama-korean
|
hariqueen
| 2024-01-28T22:09:26Z | 9 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"custom_code",
"arxiv:1910.09700",
"base_model:TinyPixel/CodeLlama-7B-Python-bf16-sharded",
"base_model:adapter:TinyPixel/CodeLlama-7B-Python-bf16-sharded",
"region:us"
] | null | 2023-12-29T07:01:51Z |
---
library_name: peft
base_model: TinyPixel/CodeLlama-7B-Python-bf16-sharded
---
# 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]
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- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## 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
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[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
|
bartowski/Tess-34B-v1.5b-exl2
|
bartowski
| 2024-01-28T22:09:02Z | 0 | 1 | null |
[
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2024-01-28T19:30:07Z |
---
license: other
license_name: yi-34b
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Tess-34B-v1.5b
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.12">turboderp's ExLlamaV2 v0.0.12</a> for quantization.
## The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using the default calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/migtissera/Tess-34B-v1.5b
<a href="https://huggingface.co/bartowski/Tess-34B-v1.5b-exl2/tree/8_0">8.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Tess-34B-v1.5b-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/Tess-34B-v1.5b-exl2/tree/4_25">4.25 bits per weight</a>
<a href="https://huggingface.co/bartowski/Tess-34B-v1.5b-exl2/tree/3_5">3.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/Tess-34B-v1.5b-exl2/tree/3_0">3.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Tess-34B-v1.5b-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `Tess-34B-v1.5b-exl2`:
```shell
mkdir Tess-34B-v1.5b-exl2
huggingface-cli download bartowski/Tess-34B-v1.5b-exl2 --local-dir Tess-34B-v1.5b-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Tess-34B-v1.5b-exl2-6_5
huggingface-cli download bartowski/Tess-34B-v1.5b-exl2 --revision 6_5 --local-dir Tess-34B-v1.5b-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Tess-34B-v1.5b-exl2-6.5
huggingface-cli download bartowski/Tess-34B-v1.5b-exl2 --revision 6_5 --local-dir Tess-34B-v1.5b-exl2-6.5 --local-dir-use-symlinks False
```
|
MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-28T22:06:42Z | 112 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"vihangd/smartyplats-7b-v2",
"pytorch",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-28T21:56:11Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- vihangd/smartyplats-7b-v2
- pytorch
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-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 MaziyarPanahi/smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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 32768` 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="./smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # 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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./smartyplats-7b-v2-Mistral-7B-Instruct-v0.1-GGUF.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)
|
songfeng/code-llama-7b-text-to-sql
|
songfeng
| 2024-01-28T21:58:07Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-01-28T21:45:22Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
datasets:
- generator
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: code-llama-7b-text-to-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. -->
# code-llama-7b-text-to-sql
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the generator 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: 0.0002
- train_batch_size: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
mahmadamin/DonutInvoicesV2
|
mahmadamin
| 2024-01-28T21:54:40Z | 94 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"image-to-text",
"donut",
"vision",
"invoices",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2024-01-28T17:41:08Z |
---
library_name: transformers
tags:
- image-to-text
- donut
- vision
- invoices
---
|
abragin/Reinforce-Pixelcopter-PLE-v0
|
abragin
| 2024-01-28T21:51:20Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-28T20:57:37Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 26.30 +/- 17.03
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
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
|
MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-28T21:48:24Z | 93 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"uukuguy/airoboros-m-7b-3.1.2-dare-0.85",
"pytorch",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-28T21:37:28Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- uukuguy/airoboros-m-7b-3.1.2-dare-0.85
- pytorch
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-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 MaziyarPanahi/airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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 32768` 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="./airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # 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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./airoboros-m-7b-3.1.2-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.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)
|
LoneStriker/Tess-10.7B-v1.5b-8.0bpw-h8-exl2
|
LoneStriker
| 2024-01-28T21:43:32Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T21:38:15Z |
---
license: apache-2.0
---
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-10.7B-v1.5b was trained on the SOLAR-10.7B base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
asun17904/anliR3-bert-base-uncased
|
asun17904
| 2024-01-28T21:39:21Z | 0 | 0 |
pytorch
|
[
"pytorch",
"en",
"license:mit",
"region:us"
] | null | 2024-01-28T18:43:12Z |
---
language: en
license: mit
library_name: pytorch
---
# Knowledge Continuity Regularized Network
Dataset: ANLI
Round: None
Trainer Hyperparameters:
- `lr` = 5e-05
- `per_device_batch_size` = 16
- `gradient_accumulation_steps` = 1
- `weight_decay` = 1e-09
- `seed` = 42
Regularization Hyperparameters
- `numerical stability denominator constant` = 1.0
- `lambda` = 1.0
- `alpha` = 1.0
- `beta` = 1.0
Extended Logs:
|eval_loss|eval_accuracy|epoch|
|--|--|--|
|34.947|0.335|1.0|
|34.936|0.330|2.0|
|34.925|0.337|3.0|
|34.925|0.335|4.0|
|34.924|0.335|5.0|
|34.922|0.327|6.0|
|34.923|0.338|7.0|
|34.924|0.330|8.0|
|34.922|0.335|9.0|
**Test Accuracy: 0.338**
|
mlx-community/whisper-tiny-mlx-4bit
|
mlx-community
| 2024-01-28T21:38:38Z | 77 | 1 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"endpoints_compatible",
"region:us"
] | null | 2024-01-28T21:28:12Z |
[OpenAI's Whisper tiny](https://huggingface.co/openai/whisper-tiny) converted to [MLX](https://github.com/ml-explore/mlx-examples) format and quantized up to 4 bits with group size 64.
|
LoneStriker/Tess-10.7B-v1.5b-6.0bpw-h6-exl2
|
LoneStriker
| 2024-01-28T21:38:13Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T21:33:08Z |
---
license: apache-2.0
---
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-10.7B-v1.5b was trained on the SOLAR-10.7B base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
djovak/yotta-embeddings
|
djovak
| 2024-01-28T21:37:36Z | 50 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-01-05T22:39:16Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Patcas/AssertNoDocWorks-step-3
|
Patcas
| 2024-01-28T21:34:01Z | 91 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"plbart",
"text2text-generation",
"generated_from_trainer",
"base_model:Patcas/my_awesome-assert-new",
"base_model:finetune:Patcas/my_awesome-assert-new",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-28T21:11:13Z |
---
base_model: Patcas/my_awesome-assert-new
tags:
- generated_from_trainer
model-index:
- name: AssertNoDocWorks-step-3
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. -->
# AssertNoDocWorks-step-3
This model is a fine-tuned version of [Patcas/my_awesome-assert-new](https://huggingface.co/Patcas/my_awesome-assert-new) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9960
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 230 | 1.1994 |
| No log | 2.0 | 460 | 1.0328 |
| 1.4365 | 3.0 | 690 | 0.9943 |
| 1.4365 | 4.0 | 920 | 0.9960 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
LoneStriker/Tess-10.7B-v1.5b-5.0bpw-h6-exl2
|
LoneStriker
| 2024-01-28T21:33:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T21:29:20Z |
---
license: apache-2.0
---
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-10.7B-v1.5b was trained on the SOLAR-10.7B base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-28T21:29:56Z | 41 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"uukuguy/zephyr-7b-alpha-dare-0.85",
"pytorch",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"license:apache-2.0",
"base_model:MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-28T21:19:09Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- uukuguy/zephyr-7b-alpha-dare-0.85
- pytorch
- license:llama2
- autotrain_compatible
- endpoints_compatible
- region:us
- license:apache-2.0
model_name: zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-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 MaziyarPanahi/zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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 32768` 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="./zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # 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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./zephyr-7b-alpha-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.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)
|
LoneStriker/Tess-10.7B-v1.5b-4.0bpw-h6-exl2
|
LoneStriker
| 2024-01-28T21:29:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T21:26:05Z |
---
license: apache-2.0
---
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-10.7B-v1.5b was trained on the SOLAR-10.7B base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
SC99/Mistral-7B-privatemix-base-ia
|
SC99
| 2024-01-28T21:26:27Z | 0 | 0 | null |
[
"safetensors",
"arxiv:1910.09700",
"license:cc-by-4.0",
"region:us"
] | null | 2024-01-28T21:22:05Z |
---
license: cc-by-4.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
LoneStriker/Tess-10.7B-v1.5b-3.0bpw-h6-exl2
|
LoneStriker
| 2024-01-28T21:26:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T21:23:29Z |
---
license: apache-2.0
---
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-10.7B-v1.5b was trained on the SOLAR-10.7B base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
|
SC99/Mistral-7B-privatemix-ia1
|
SC99
| 2024-01-28T21:26:02Z | 0 | 0 | null |
[
"safetensors",
"arxiv:1910.09700",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-01-28T21:24:48Z |
---
license: cc-by-nc-4.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
adalib/sqlmodel-data-codegen-2B-mono-prefix
|
adalib
| 2024-01-28T21:13:50Z | 7 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Salesforce/codegen-2B-mono",
"base_model:adapter:Salesforce/codegen-2B-mono",
"region:us"
] | null | 2024-01-28T15:14:04Z |
---
library_name: peft
base_model: Salesforce/codegen-2B-mono
---
# 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
|
TunahanGokcimen/ernie-2.0-base-en
|
TunahanGokcimen
| 2024-01-28T21:10:55Z | 76 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"ernie",
"token-classification",
"generated_from_trainer",
"base_model:nghuyong/ernie-2.0-base-en",
"base_model:finetune:nghuyong/ernie-2.0-base-en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-28T20:38:47Z |
---
base_model: nghuyong/ernie-2.0-base-en
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: ernie-2.0-base-en
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. -->
# ernie-2.0-base-en
This model is a fine-tuned version of [nghuyong/ernie-2.0-base-en](https://huggingface.co/nghuyong/ernie-2.0-base-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2022
- Precision: 0.7745
- Recall: 0.8255
- F1: 0.7992
- Accuracy: 0.9392
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2221 | 1.0 | 2078 | 0.2066 | 0.7130 | 0.8024 | 0.7551 | 0.9309 |
| 0.1813 | 2.0 | 4156 | 0.1972 | 0.7573 | 0.8224 | 0.7885 | 0.9362 |
| 0.1397 | 3.0 | 6234 | 0.2022 | 0.7745 | 0.8255 | 0.7992 | 0.9392 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
ConnyGenz/artificially-natural-roberta-03
|
ConnyGenz
| 2024-01-28T21:07:49Z | 93 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:ConnyGenz/artificially-natural-roberta-02",
"base_model:finetune:ConnyGenz/artificially-natural-roberta-02",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-28T20:44:35Z |
---
license: mit
base_model: ConnyGenz/artificially-natural-roberta-02
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: artificially-natural-roberta-03
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. -->
# artificially-natural-roberta-03
This model is a fine-tuned version of [ConnyGenz/artificially-natural-roberta-02](https://huggingface.co/ConnyGenz/artificially-natural-roberta-02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0840
- F1: 0.987
## 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: 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----:|
| No log | 1.0 | 250 | 0.0381 | 0.993 |
| 0.0194 | 2.0 | 500 | 0.0971 | 0.987 |
| 0.0194 | 3.0 | 750 | 0.0840 | 0.987 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
TheBloke/Tess-34B-v1.5b-GGUF
|
TheBloke
| 2024-01-28T20:57:45Z | 1,009 | 5 |
transformers
|
[
"transformers",
"gguf",
"yi",
"base_model:migtissera/Tess-34B-v1.5b",
"base_model:quantized:migtissera/Tess-34B-v1.5b",
"license:other",
"region:us"
] | null | 2024-01-28T20:04:19Z |
---
base_model: migtissera/Tess-34B-v1.5b
inference: false
license: other
license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE
license_name: yi-34b
model_creator: Migel Tissera
model_name: Tess 34B V1.5B
model_type: yi
prompt_template: 'SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
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<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Tess 34B V1.5B - GGUF
- Model creator: [Migel Tissera](https://huggingface.co/migtissera)
- Original model: [Tess 34B V1.5B](https://huggingface.co/migtissera/Tess-34B-v1.5b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Migel Tissera's Tess 34B V1.5B](https://huggingface.co/migtissera/Tess-34B-v1.5b).
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/Tess-34B-v1.5b-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF)
* [Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/migtissera/Tess-34B-v1.5b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Orca-Vicuna
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [tess-34b-v1.5b.Q2_K.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q2_K.gguf) | Q2_K | 2 | 12.83 GB| 15.33 GB | significant quality loss - not recommended for most purposes |
| [tess-34b-v1.5b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q3_K_S.gguf) | Q3_K_S | 3 | 14.96 GB| 17.46 GB | very small, high quality loss |
| [tess-34b-v1.5b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q3_K_M.gguf) | Q3_K_M | 3 | 16.65 GB| 19.15 GB | very small, high quality loss |
| [tess-34b-v1.5b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q3_K_L.gguf) | Q3_K_L | 3 | 18.14 GB| 20.64 GB | small, substantial quality loss |
| [tess-34b-v1.5b.Q4_0.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q4_0.gguf) | Q4_0 | 4 | 19.47 GB| 21.97 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [tess-34b-v1.5b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q4_K_S.gguf) | Q4_K_S | 4 | 19.60 GB| 22.10 GB | small, greater quality loss |
| [tess-34b-v1.5b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q4_K_M.gguf) | Q4_K_M | 4 | 20.66 GB| 23.16 GB | medium, balanced quality - recommended |
| [tess-34b-v1.5b.Q5_0.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q5_0.gguf) | Q5_0 | 5 | 23.71 GB| 26.21 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [tess-34b-v1.5b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q5_K_S.gguf) | Q5_K_S | 5 | 23.71 GB| 26.21 GB | large, low quality loss - recommended |
| [tess-34b-v1.5b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q5_K_M.gguf) | Q5_K_M | 5 | 24.32 GB| 26.82 GB | large, very low quality loss - recommended |
| [tess-34b-v1.5b.Q6_K.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.Q6_K.gguf) | Q6_K | 6 | 28.21 GB| 30.71 GB | very large, extremely low quality loss |
| [tess-34b-v1.5b.Q8_0.gguf](https://huggingface.co/TheBloke/Tess-34B-v1.5b-GGUF/blob/main/tess-34b-v1.5b.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/Tess-34B-v1.5b-GGUF and below it, a specific filename to download, such as: tess-34b-v1.5b.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/Tess-34B-v1.5b-GGUF tess-34b-v1.5b.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/Tess-34B-v1.5b-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/Tess-34B-v1.5b-GGUF tess-34b-v1.5b.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 tess-34b-v1.5b.Q4_K_M.gguf --color -c 200000 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "SYSTEM: {system_message}\nUSER: {prompt}\nASSISTANT:"
```
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="./tess-34b-v1.5b.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(
"SYSTEM: {system_message}\nUSER: {prompt}\nASSISTANT:", # 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="./tess-34b-v1.5b.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: Migel Tissera's Tess 34B V1.5B
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-34B-v1.5b was trained on the Yi-34B-200K base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
<!-- original-model-card end -->
|
tourist800/Mistral-7B-Merge-14-v0.2
|
tourist800
| 2024-01-28T20:51:47Z | 48 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"EmbeddedLLM/Mistral-7B-Merge-14-v0.1",
"amazon/MistralLite",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T20:48:04Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- EmbeddedLLM/Mistral-7B-Merge-14-v0.1
- amazon/MistralLite
---
# Mistral-7B-Merge-14-v0.2
Mistral-7B-Merge-14-v0.2 is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [EmbeddedLLM/Mistral-7B-Merge-14-v0.1](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1)
* [amazon/MistralLite](https://huggingface.co/amazon/MistralLite)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1
layer_range: [0, 32]
- model: amazon/MistralLite
layer_range: [0, 32]
merge_method: slerp
base_model: EmbeddedLLM/Mistral-7B-Merge-14-v0.1
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
```
|
silvente93/tfm_rev7
|
silvente93
| 2024-01-28T20:48:48Z | 1 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2024-01-28T18:52:27Z |
---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
model-index:
- name: tfm_rev7
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. -->
# tfm_rev7
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.2-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.2-GPTQ) on the None 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-06
- 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: cosine
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
fazito25/ppo-LunarLander-v2
|
fazito25
| 2024-01-28T20:29:58Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-28T20:29:42Z |
---
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: 250.37 +/- 34.94
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
...
```
|
TheBloke/Tess-10.7B-v1.5b-GGUF
|
TheBloke
| 2024-01-28T20:20:45Z | 423 | 7 |
transformers
|
[
"transformers",
"gguf",
"solar",
"base_model:migtissera/Tess-10.7B-v1.5b",
"base_model:quantized:migtissera/Tess-10.7B-v1.5b",
"license:apache-2.0",
"region:us"
] | null | 2024-01-28T20:01:42Z |
---
base_model: migtissera/Tess-10.7B-v1.5b
inference: false
license: apache-2.0
model_creator: Migel Tissera
model_name: Tess 10.7B V1.5B
model_type: solar
prompt_template: 'SYSTEM: {system_message}
USER: {prompt}
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 -->
# Tess 10.7B V1.5B - GGUF
- Model creator: [Migel Tissera](https://huggingface.co/migtissera)
- Original model: [Tess 10.7B V1.5B](https://huggingface.co/migtissera/Tess-10.7B-v1.5b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Migel Tissera's Tess 10.7B V1.5B](https://huggingface.co/migtissera/Tess-10.7B-v1.5b).
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/Tess-10.7B-v1.5b-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF)
* [Migel Tissera's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/migtissera/Tess-10.7B-v1.5b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Orca-Vicuna
```
SYSTEM: {system_message}
USER: {prompt}
ASSISTANT:
```
<!-- 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [tess-10.7b-v1.5b.Q2_K.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q2_K.gguf) | Q2_K | 2 | 4.00 GB| 6.50 GB | significant quality loss - not recommended for most purposes |
| [tess-10.7b-v1.5b.Q3_K_S.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q3_K_S.gguf) | Q3_K_S | 3 | 4.66 GB| 7.16 GB | very small, high quality loss |
| [tess-10.7b-v1.5b.Q3_K_M.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q3_K_M.gguf) | Q3_K_M | 3 | 5.20 GB| 7.70 GB | very small, high quality loss |
| [tess-10.7b-v1.5b.Q3_K_L.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q3_K_L.gguf) | Q3_K_L | 3 | 5.65 GB| 8.15 GB | small, substantial quality loss |
| [tess-10.7b-v1.5b.Q4_0.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q4_0.gguf) | Q4_0 | 4 | 6.07 GB| 8.57 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [tess-10.7b-v1.5b.Q4_K_S.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q4_K_S.gguf) | Q4_K_S | 4 | 6.12 GB| 8.62 GB | small, greater quality loss |
| [tess-10.7b-v1.5b.Q4_K_M.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q4_K_M.gguf) | Q4_K_M | 4 | 6.46 GB| 8.96 GB | medium, balanced quality - recommended |
| [tess-10.7b-v1.5b.Q5_0.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q5_0.gguf) | Q5_0 | 5 | 7.40 GB| 9.90 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [tess-10.7b-v1.5b.Q5_K_S.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q5_K_S.gguf) | Q5_K_S | 5 | 7.40 GB| 9.90 GB | large, low quality loss - recommended |
| [tess-10.7b-v1.5b.Q5_K_M.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q5_K_M.gguf) | Q5_K_M | 5 | 7.60 GB| 10.10 GB | large, very low quality loss - recommended |
| [tess-10.7b-v1.5b.Q6_K.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q6_K.gguf) | Q6_K | 6 | 8.81 GB| 11.31 GB | very large, extremely low quality loss |
| [tess-10.7b-v1.5b.Q8_0.gguf](https://huggingface.co/TheBloke/Tess-10.7B-v1.5b-GGUF/blob/main/tess-10.7b-v1.5b.Q8_0.gguf) | Q8_0 | 8 | 11.40 GB| 13.90 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/Tess-10.7B-v1.5b-GGUF and below it, a specific filename to download, such as: tess-10.7b-v1.5b.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/Tess-10.7B-v1.5b-GGUF tess-10.7b-v1.5b.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/Tess-10.7B-v1.5b-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/Tess-10.7B-v1.5b-GGUF tess-10.7b-v1.5b.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 tess-10.7b-v1.5b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "SYSTEM: {system_message}\nUSER: {prompt}\nASSISTANT:"
```
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="./tess-10.7b-v1.5b.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(
"SYSTEM: {system_message}\nUSER: {prompt}\nASSISTANT:", # 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="./tess-10.7b-v1.5b.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: Migel Tissera's Tess 10.7B V1.5B
<br>
<br>
Tess, short for Tesoro (Treasure in Italian), is a general purpose Large Language Model series. Tess-10.7B-v1.5b was trained on the SOLAR-10.7B base.
# Prompt Format:
```
SYSTEM: <ANY SYSTEM CONTEXT>
USER:
ASSISTANT:
```
<!-- original-model-card end -->
|
Artefact2/Proctora-GGUF
|
Artefact2
| 2024-01-28T20:17:48Z | 39 | 3 | null |
[
"gguf",
"en",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-01-28T19:19:38Z |
---
license: cc-by-nc-4.0
language:
- en
---
<img 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" />
These are GGUF quantized versions of [Karko/Proctora](https://huggingface.co/Karko/Proctora).
The importance matrix was trained for 1M tokens (2,000 batches of 512 tokens) using `wiki.train.raw`.
The IQ2_XXS and IQ2_XS versions are compatible with llama.cpp, version `147b17a` or later.
|
MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-28T20:14:48Z | 46 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"uukuguy/SynthIA-7B-v1.3-dare-0.85",
"pytorch",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"license:apache-2.0",
"base_model:MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-28T20:03:39Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- uukuguy/SynthIA-7B-v1.3-dare-0.85
- pytorch
- license:llama2
- autotrain_compatible
- endpoints_compatible
- region:us
- license:apache-2.0
model_name: SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-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 MaziyarPanahi/SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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 32768` 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="./SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # 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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./SynthIA-7B-v1.3-dare-0.85-Mistral-7B-Instruct-v0.1-GGUF.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)
|
sanagnos/Mistral-7B-v0.1-truthfulqa-truth
|
sanagnos
| 2024-01-28T20:12:40Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2024-01-27T13:20:22Z |
```
kind = "info"
model_name = f"sanagnos/Mistral-7B-v0.1-truthfulqa-{kind}"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map=0,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
)
input_ids = tokenizer.encode('Q: Which alternative medicine is now accepted by all scientists?\nA: The American Medical Association.\nHelpful:', return_tensors="pt")
pred = model(input_ids.cuda()).logits[0, -1, [5081, 708]].cpu()
if pred[0] > pred[1]:
prediction = " yes"
else:
prediction = " no"
```
|
mrzeiss/Rafale-PA300-Mis7B
|
mrzeiss
| 2024-01-28T20:12:39Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T20:08:12Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
macadeliccc/WestSeverus-7B-truthy-DPO-v2
|
macadeliccc
| 2024-01-28T20:08:17Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-28T05:23:49Z |
---
library_name: transformers
license: cc
---
|
Shaleen123/openorca-yi-6b
|
Shaleen123
| 2024-01-28T20:05:04Z | 61 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-28T20:02:35Z |
---
library_name: transformers
tags: []
---
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
Heromnxpw0/q-FrozenLake-v1-4x4-noSlippery
|
Heromnxpw0
| 2024-01-28T19:58:17Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-28T19:58:15Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Heromnxpw0/q-FrozenLake-v1-4x4-noSlippery", 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"])
```
|
MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-28T19:56:42Z | 52 | 1 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"uukuguy/speechless-code-mistral-7b-v2.0",
"pytorch",
"code",
"en",
"dataset:jondurbin/airoboros-2.2",
"dataset:Open-Orca/OpenOrca",
"dataset:garage-bAInd/Open-Platypus",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"dataset:TokenBender/python_eval_instruct_51k",
"dataset:ise-uiuc/Magicoder-OSS-Instruct-75K",
"dataset:meta-math/MetaMathQA",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-28T19:45:33Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- uukuguy/speechless-code-mistral-7b-v2.0
- pytorch
- code
- en
- dataset:jondurbin/airoboros-2.2
- dataset:Open-Orca/OpenOrca
- dataset:garage-bAInd/Open-Platypus
- dataset:WizardLM/WizardLM_evol_instruct_V2_196k
- dataset:TokenBender/python_eval_instruct_51k
- dataset:ise-uiuc/Magicoder-OSS-Instruct-75K
- dataset:meta-math/MetaMathQA
- license:apache-2.0
- model-index
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-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 MaziyarPanahi/speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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 32768` 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="./speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # 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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./speechless-code-mistral-7b-v2.0-Mistral-7B-Instruct-v0.1-GGUF.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)
|
adalib/colossalai-data-codegen-350M-mono-prefix
|
adalib
| 2024-01-28T19:55:33Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Salesforce/codegen-350M-mono",
"base_model:adapter:Salesforce/codegen-350M-mono",
"region:us"
] | null | 2024-01-28T10:00:46Z |
---
library_name: peft
base_model: Salesforce/codegen-350M-mono
---
# 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
|
Patcas/AssertNoDocWorks-step-1
|
Patcas
| 2024-01-28T19:49:56Z | 90 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"plbart",
"text2text-generation",
"generated_from_trainer",
"base_model:Patcas/my_awesome-assert-new",
"base_model:finetune:Patcas/my_awesome-assert-new",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-28T19:30:27Z |
---
base_model: Patcas/my_awesome-assert-new
tags:
- generated_from_trainer
model-index:
- name: AssertNoDocWorks-step-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. -->
# AssertNoDocWorks-step-1
This model is a fine-tuned version of [Patcas/my_awesome-assert-new](https://huggingface.co/Patcas/my_awesome-assert-new) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9795
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 230 | 1.1781 |
| No log | 2.0 | 460 | 1.0169 |
| 1.4227 | 3.0 | 690 | 0.9748 |
| 1.4227 | 4.0 | 920 | 0.9795 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
vsamuel/scifive_ten_epoch
|
vsamuel
| 2024-01-28T19:47:08Z | 91 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:razent/SciFive-base-Pubmed_PMC",
"base_model:finetune:razent/SciFive-base-Pubmed_PMC",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-28T19:29:01Z |
---
base_model: razent/SciFive-base-Pubmed_PMC
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: scifive_ten_epoch
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. -->
# scifive_ten_epoch
This model is a fine-tuned version of [razent/SciFive-base-Pubmed_PMC](https://huggingface.co/razent/SciFive-base-Pubmed_PMC) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7913
- Rouge1: 0.366
- Rouge2: 0.2107
- Rougel: 0.3132
- Rougelsum: 0.3131
- Gen Len: 17.33
## 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: 4
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 275 | 2.2002 | 0.2752 | 0.1436 | 0.2395 | 0.24 | 17.32 |
| 2.3887 | 2.0 | 550 | 1.9610 | 0.347 | 0.2007 | 0.2959 | 0.2961 | 17.73 |
| 2.3887 | 3.0 | 825 | 1.8986 | 0.3664 | 0.2121 | 0.3098 | 0.3101 | 17.5 |
| 1.7972 | 4.0 | 1100 | 1.8486 | 0.3805 | 0.2309 | 0.3267 | 0.327 | 17.1 |
| 1.7972 | 5.0 | 1375 | 1.8232 | 0.372 | 0.2178 | 0.313 | 0.313 | 17.64 |
| 1.6528 | 6.0 | 1650 | 1.8005 | 0.3836 | 0.2271 | 0.3208 | 0.3209 | 17.44 |
| 1.6528 | 7.0 | 1925 | 1.7969 | 0.3821 | 0.2278 | 0.3251 | 0.3253 | 17.25 |
| 1.5676 | 8.0 | 2200 | 1.7872 | 0.3806 | 0.2242 | 0.3224 | 0.323 | 17.3 |
| 1.5676 | 9.0 | 2475 | 1.7888 | 0.3697 | 0.2135 | 0.3135 | 0.3133 | 17.36 |
| 1.5288 | 10.0 | 2750 | 1.7913 | 0.366 | 0.2107 | 0.3132 | 0.3131 | 17.33 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-28T19:37:46Z | 74 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"safetensors",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"7b",
"mistralai/Mistral-7B-Instruct-v0.1",
"fblgit/una-cybertron-7b-v2-bf16",
"juanako",
"UNA",
"cybertron",
"fbl",
"dataset:fblgit/tree-of-knowledge",
"dataset:Open-Orca/SlimOrca-Dedup",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-28T19:26:43Z |
---
license: apache-2.0
tags:
- quantized
- 2-bit
- 3-bit
- 4-bit
- 5-bit
- 6-bit
- 8-bit
- GGUF
- transformers
- safetensors
- mistral
- text-generation
- Safetensors
- text-generation-inference
- merge
- 7b
- mistralai/Mistral-7B-Instruct-v0.1
- fblgit/una-cybertron-7b-v2-bf16
- juanako
- UNA
- cybertron
- fbl
- dataset:fblgit/tree-of-knowledge
- dataset:Open-Orca/SlimOrca-Dedup
- dataset:allenai/ultrafeedback_binarized_cleaned
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1).
## How to use
Thanks to [TheBloke](https://huggingface.co/TheBloke) for preparing an amazing README on how to use GGUF models:
### 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.
### 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
## 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: [MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF.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 MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
</details>
<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 [MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-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 MaziyarPanahi/una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF.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>
## 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 una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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 32768` 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="./una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # 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
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|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="./una-cybertron-7b-v2-bf16-Mistral-7B-Instruct-v0.1-GGUF.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)
|
asun17904/anliR2-bert-base-uncased
|
asun17904
| 2024-01-28T19:37:39Z | 0 | 0 |
pytorch
|
[
"pytorch",
"en",
"license:mit",
"region:us"
] | null | 2024-01-27T18:48:05Z |
---
language: en
license: mit
library_name: pytorch
---
# Knowledge Continuity Regularized Network
Dataset: ANLI
Round: None
Trainer Hyperparameters:
- `lr` = 5e-05
- `per_device_batch_size` = 8
- `gradient_accumulation_steps` = 2
- `weight_decay` = 1e-09
- `seed` = 42
Regularization Hyperparameters
- `numerical stability denominator constant` = 1.0
- `lambda` = 1.0
- `alpha` = 1.0
- `beta` = 1.0
Extended Logs:
|eval_loss|eval_accuracy|epoch|
|--|--|--|
|1.176|0.364|1.0|
|1.142|0.393|2.0|
|1.140|0.402|3.0|
|1.143|0.396|4.0|
|1.125|0.412|5.0|
|1.152|0.392|6.0|
|1.134|0.407|7.0|
|1.140|0.407|8.0|
|1.128|0.420|9.0|
|1.145|0.393|10.0|
|1.117|0.431|11.0|
|1.122|0.426|12.0|
|1.111|0.434|13.0|
|1.130|0.418|14.0|
|1.122|0.428|15.0|
|1.115|0.431|16.0|
|1.110|0.437|17.0|
|1.104|0.440|18.0|
|1.094|0.450|19.0|
**Test Accuracy: 0.456**
|
adalib/colossalai-data-codeparrot-prefix
|
adalib
| 2024-01-28T19:36:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:codeparrot/codeparrot",
"base_model:adapter:codeparrot/codeparrot",
"region:us"
] | null | 2024-01-28T19:36:05Z |
---
library_name: peft
base_model: codeparrot/codeparrot
---
# 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
|
tourist800/mistral_2X7b
|
tourist800
| 2024-01-28T19:34:48Z | 53 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mistralai/Mistral-7B-Instruct-v0.2",
"mistralai/Mistral-7B-v0.1",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-26T21:26:10Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- mistralai/Mistral-7B-v0.1
---
# Mistral_2X7b
Marcoro14-7B-slerp is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mistralai/Mistral-7B-Instruct-v0.2
layer_range: [0, 32]
- model: mistralai/Mistral-7B-v0.1
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
```
|
GbgMehdi/NLP
|
GbgMehdi
| 2024-01-28T19:34:47Z | 62 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"h",
"f",
"-",
"a",
"s",
"r",
"l",
"e",
"d",
"b",
"o",
"en",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-28T17:58:58Z |
---
language:
- en
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
- h
- f
- '-'
- a
- s
- r
- l
- e
- d
- b
- o
metrics:
- wer
base_model: openai/whisper-tiny
model-index:
- name: Whisper Tiny English 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. -->
# Whisper Tiny English v2
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the commands_dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
- Wer: 3.3333
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 30
- training_steps: 300
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0098 | 1.0 | 75 | 0.0038 | 3.3333 |
| 0.0035 | 2.0 | 150 | 0.0006 | 3.75 |
| 0.0031 | 3.0 | 225 | 0.0004 | 3.1667 |
| 0.0012 | 4.0 | 300 | 0.0003 | 3.3333 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
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