modelId
stringlengths 5
139
| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-04 12:28:55
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 539
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-04 12:28:29
| card
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|---|---|---|---|---|---|---|---|---|---|
CultureBERT/roberta-large-hierarchy
|
CultureBERT
| 2024-01-29T14:47:21Z | 92 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:1907.11692",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-12-02T21:22:51Z |
---
license: cc-by-nc-4.0
---
This model is a fine-tuned version of RoBERTa-large [1]. It was trained on 1,400 employee reviews to measure corporate culture. More specifically, it measures the **culture dimension โhierarchyโ** of the Competing Values Framework [2,3]. An organization that exhibits a hierarchy culture is characterized by an emphasis to **control** [2].
The model assigns one of three possible labels:
0 (**neutral**): Text does not allow any inference about a hierarchy culture. <br />
1 (**positive**): Text contains information in line with a hierarchy culture. <br />
2 (**negative**): Text contains information in opposite to a hierarchy culture. <br />
For details on the model and its performance, see Koch and Pasch (2023). Please cite this article when using the model: <br />
S. Koch and S. Pasch, "CultureBERT: Measuring Corporate Culture With Transformer-Based Language Models," in 2023 IEEE International Conference on Big Data (BigData), pp. 3176-3184. doi: 10.1109/BigData59044.2023.10386765
Please see the following **tutorial** on how to apply CultureBERT to measure corporate culture in your own text documents: https://github.com/Stefan-Pasch/CultureBERT
Other References:
[1] Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
[2] Cameron, Kim S.; Quinn, Robert E. (2011): Diagnosing and Changing Organizational Culture. Based on the Competing Values Framework. 3rd ed. San Francisco (CA): Jossey-Bass.
[3] Quinn, Robert E.; Rohrbaugh, John (1983): A Spatial Model of Effectiveness Criteria: Towards a Competing Values Approach to Organizational Analysis. In Management Science 29 (3), pp. 363โ377. DOI: 10.1287/mnsc.29.3.363.
|
CultureBERT/roberta-large-clan
|
CultureBERT
| 2024-01-29T14:46:52Z | 90 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"arxiv:1907.11692",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-26T15:25:40Z |
---
license: cc-by-nc-4.0
---
This model is a fine-tuned version of RoBERTa-large [1]. It was trained on 1,400 employee reviews to measure corporate culture. More specifically, it measures the **culture dimension โclanโ** of the Competing Values Framework [2,3]. An organization that exhibits a clan culture is characterized by an emphasis to **collaborate** [2].
The model assigns one of three possible labels:
0 (**neutral**): Text does not allow any inference about a clan culture. <br />
1 (**positive**): Text contains information in line with a clan culture. <br />
2 (**negative**): Text contains information in opposite to a clan culture. <br />
For details on the model and its performance, see Koch and Pasch (2023). Please cite this article when using the model: <br />
S. Koch and S. Pasch, "CultureBERT: Measuring Corporate Culture With Transformer-Based Language Models," in 2023 IEEE International Conference on Big Data (BigData), pp. 3176-3184. doi: 10.1109/BigData59044.2023.10386765
Please see the following **tutorial** on how to apply CultureBERT to measure corporate culture in your own text documents: https://github.com/Stefan-Pasch/CultureBERT
Other References:
[1] Liu, Y.; Ott, M.; Goyal, N.; Du, J.; Joshi, M.; Chen, D.; ... & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692.
[2] Cameron, Kim S.; Quinn, Robert E. (2011): Diagnosing and Changing Organizational Culture. Based on the Competing Values Framework. 3rd ed. San Francisco (CA): Jossey-Bass.
[3] Quinn, Robert E.; Rohrbaugh, John (1983): A Spatial Model of Effectiveness Criteria: Towards a Competing Values Approach to Organizational Analysis. In Management Science 29 (3), pp. 363โ377. DOI: 10.1287/mnsc.29.3.363.
|
Commandante/german-party-sentiment-bert-241-synonyms
|
Commandante
| 2024-01-29T14:42:25Z | 91 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:mdraw/german-news-sentiment-bert",
"base_model:finetune:mdraw/german-news-sentiment-bert",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-29T13:48:49Z |
---
base_model: mdraw/german-news-sentiment-bert
tags:
- generated_from_trainer
model-index:
- name: german-party-sentiment-bert-241-synonyms
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. -->
# german-party-sentiment-bert-241-synonyms
This model is a fine-tuned version of [mdraw/german-news-sentiment-bert](https://huggingface.co/mdraw/german-news-sentiment-bert) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0104
## 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: 8e-06
- train_batch_size: 20
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 120
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3705 | 1.0 | 28 | 1.0812 |
| 1.0214 | 2.0 | 56 | 1.0147 |
| 1.0214 | 3.0 | 84 | 1.0104 |
| 0.9279 | 4.0 | 112 | 1.0215 |
| 0.9279 | 5.0 | 140 | 1.0430 |
| 0.8797 | 6.0 | 168 | 1.0822 |
| 0.7917 | 7.0 | 196 | 1.0620 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu118
- Tokenizers 0.15.1
|
bhavya25/my-pet-dog
|
bhavya25
| 2024-01-29T14:28:33Z | 2 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-29T14:24:13Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog Dreambooth model trained by bhavya25 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 23WU0101089
Sample pictures of this concept:
.jpeg)
.jpeg)
.jpeg)
.jpeg)
.jpeg)
.jpeg)
|
ombhojane/ChaloKisaanAI
|
ombhojane
| 2024-01-29T14:23:25Z | 0 | 0 | null |
[
"dataset:ombhojane/ckv3",
"license:mit",
"region:us"
] | null | 2024-01-29T14:15:39Z |
---
license: mit
datasets:
- ombhojane/ckv3
metrics:
- accuracy : 61.67
---
|
BanUrsus/bert-base-cased-finetuned-squad_nlp-course-chapter7-section6
|
BanUrsus
| 2024-01-29T14:16:32Z | 100 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-29T10:46:03Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-finetuned-squad_nlp-course-chapter7-section6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-squad_nlp-course-chapter7-section6
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.1
- Pytorch 1.12.1+cu116
- Datasets 2.16.1
- Tokenizers 0.15.1
|
golesheed/whisper-non-native-children-3-dutch
|
golesheed
| 2024-01-29T14:00:29Z | 52 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"nl",
"base_model:openai/whisper-large-v2",
"base_model:finetune:openai/whisper-large-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-29T13:04:51Z |
---
language:
- nl
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Large 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 Large V2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3167
- Wer: 11.6330
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 20
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.6772 | 0.71 | 30 | 0.3209 | 16.8396 |
| 0.2613 | 1.43 | 60 | 0.3041 | 12.8163 |
| 0.1683 | 2.14 | 90 | 0.2908 | 11.8332 |
| 0.0777 | 2.86 | 120 | 0.2916 | 10.8138 |
| 0.0428 | 3.57 | 150 | 0.2965 | 11.7786 |
| 0.0228 | 4.29 | 180 | 0.3114 | 11.8150 |
| 0.0107 | 5.0 | 210 | 0.3167 | 11.6330 |
### Framework versions
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
DominoPizza/result-first
|
DominoPizza
| 2024-01-29T13:58:24Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-29T13:54:48Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
Kavya26/my-pet-pigeon
|
Kavya26
| 2024-01-29T13:57:10Z | 2 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-01-29T13:53:01Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Pigeon Dreambooth model trained by Kavya26 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 23WU0101013
Sample pictures of this concept:
.jpeg)
.jpeg)
.jpeg)
.jpeg)
|
prajjusy/pfet-flan-t5-base-model-4
|
prajjusy
| 2024-01-29T13:42:03Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:prajjusy/full-finetuned-flan-t5-base-2",
"base_model:adapter:prajjusy/full-finetuned-flan-t5-base-2",
"region:us"
] | null | 2024-01-29T13:42:02Z |
---
library_name: peft
base_model: prajjusy/full-finetuned-flan-t5-base-2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.1
|
Yzh1998/photomaker
|
Yzh1998
| 2024-01-29T13:28:02Z | 0 | 0 |
bertopic
|
[
"bertopic",
"text-to-image",
"aa",
"dataset:HuggingFaceM4/WebSight",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2024-01-29T13:22:25Z |
---
license: apache-2.0
datasets:
- HuggingFaceM4/WebSight
language:
- aa
metrics:
- bleu
library_name: bertopic
pipeline_tag: text-to-image
---
|
jlbaker361/ft1000-30
|
jlbaker361
| 2024-01-29T13:24:49Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2024-01-29T03:50:29Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - jlbaker361/ft1000-30
These are LoRA adaption weights for stabilityai/stable-diffusion-2-base. The weights were fine-tuned on the jlbaker361/wikiart-balanced1000 dataset.
Training epochs = 1
num_train_timesteps = 30
You can find some example images in the following.
|
AI-Sweden-Models/gpt-sw3-20b
|
AI-Sweden-Models
| 2024-01-29T13:21:23Z | 3,188 | 2 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"da",
"sv",
"no",
"en",
"is",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-12-14T12:33:38Z |
---
license: other
language:
- da
- sv
- 'no'
- en
- is
---
# Model description
[AI Sweden](https://huggingface.co/AI-Sweden-Models/)
**Base models**
[GPT-Sw3 126M](https://huggingface.co/AI-Sweden-Models/gpt-sw3-126m/) | [GPT-Sw3 356M](https://huggingface.co/AI-Sweden-Models/gpt-sw3-356m/) | [GPT-Sw3 1.3B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b/)
[GPT-Sw3 6.7B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b/) | [GPT-Sw3 6.7B v2](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2/) | [GPT-Sw3 20B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b/)
[GPT-Sw3 40B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-40b/)
**Instruct models**
[GPT-Sw3 126M Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-126m-instruct/) | [GPT-Sw3 356M Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-356m-instruct/) | [GPT-Sw3 1.3B Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b-instruct/)
[GPT-Sw3 6.7B v2 Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct/) | [GPT-Sw3 20B Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b-instruct/)
**Quantized models**
[GPT-Sw3 6.7B v2 Instruct 4-bit gptq](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct-4bit-gptq) | [GPT-Sw3 20B Instruct 4-bit gptq](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b-instruct-4bit-gptq)
GPT-SW3 is a collection of large decoder-only pretrained transformer language models that were developed by AI Sweden in collaboration with RISE and the WASP WARA for Media and Language. GPT-SW3 has been trained on a dataset containing 320B tokens in Swedish, Norwegian, Danish, Icelandic, English, and programming code. The model was pretrained using a causal language modeling (CLM) objective utilizing the NeMo Megatron GPT implementation.
# Intended use
GPT-SW3 is an autoregressive large language model that is capable of generating coherent text in 5 different languages, and 4 programming languages. GPT-SW3 can also be instructed to perform text tasks that it has not been explicitly trained for, by casting them as text generation tasks.
# Limitations
Like other large language models for which the diversity (or lack thereof) of training data induces downstream impact on the quality of our model, GPT-SW3 has limitations in terms of for example bias and safety. GPT-SW3 can also have quality issues in terms of generation diversity and hallucination. By releasing with the modified RAIL license, we also hope to increase communication, transparency, and the study of large language models. The model may: overrepresent some viewpoints and underrepresent others, contain stereotypes, generate hateful, abusive, violent, discriminatory or prejudicial language. The model may make errors, including producing incorrect information as if it were factual, it may generate irrelevant or repetitive outputs, and content that may not be appropriate for all settings, including sexual content.
# How to use
To be able to access the model from Python, since this is a private repository, you have to log in with your access token. This can be done with `huggingface-cli login`, see [HuggingFace Quick Start Guide](https://huggingface.co/docs/huggingface_hub/quick-start#login) for more information.
The following code snippet loads our tokenizer & model, and uses the GPU if available.
```python
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
# Initialize Variables
model_name = "AI-Sweden-Models/gpt-sw3-20b"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
prompt = "Trรคd รคr fina fรถr att"
# Initialize Tokenizer & Model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
model.to(device)
```
Generating text using the `generate` method is done as follows:
```python
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(device)
generated_token_ids = model.generate(
inputs=input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.6,
top_p=1,
)[0]
generated_text = tokenizer.decode(generated_token_ids)
```
A convenient alternative to the `generate` method is the HuggingFace pipeline, which handles most of the work for you:
```python
generator = pipeline('text-generation', tokenizer=tokenizer, model=model, device=device)
generated = generator(prompt, max_new_tokens=100, do_sample=True, temperature=0.6, top_p=1)[0]["generated_text"]
```
# Compliance
The release of GPT-SW3 consists of model weights, a configuration file, a tokenizer file and a vocabulary file. None of these files contain any personally identifiable information (PII) or any copyrighted material.
# GPT-SW3 Model Card
Following Mitchell et al. (2018), we provide a model card for GPT-SW3.
# Model Details
- Person or organization developing model: GPT-SW3 was developed by AI Sweden in collaboration with RISE and the WASP WARA for Media and Language.
- Model date: GPT-SW3 date of release 2022-12-20
- Model version: This is the second generation of GPT-SW3.
- Model type: GPT-SW3 is a large decoder-only transformer language model.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features: GPT-SW3 was trained with the NeMo Megatron GPT implementation.
- Paper or other resource for more information: N/A.
- License: [LICENSE](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b/blob/main/LICENSE).
- Where to send questions or comments about the model: nlu@ai.se
# Intended Use
- Primary intended uses: We pre-release GPT-SW3 for research and evaluation of the capabilities of Large Language Models for the Nordic languages. This is an important step in the process of knowledge building for LLMs, validating the model and collecting feedback on both what works well and what does not.
- Primary intended users: Organizations and individuals in the Nordic NLP ecosystem who can contribute to the validation and testing of the models and provide feedback to the community.
- Out-of-scope use cases: See the modified RAIL license.
# Data, Limitations, and Recommendations
- Data selection for training: Training data for GPT-SW3 was selected based on a combination of breadth and availability. See our Datasheet for more detailed information on the data used to train our model.
- Data selection for evaluation: N/A
- Limitations: Like other large language models for which the diversity (or lack thereof) of training data induces downstream impact on the quality of our model, GPT-SW3 has limitations in terms of bias and safety. GPT-SW3 can also have quality issues in terms of generation diversity and hallucination. In general, GPT-SW3 is not immune from the plethora of issues that plague modern large language models. By releasing with the modified RAIL license, we also hope to increase communication, transparency, and the study of large language models. The model may: Overrepresent some viewpoints and underrepresent others. Contain stereotypes. Generate: Hateful, abusive, or violent language. Discriminatory or prejudicial language. Content that may not be appropriate for all settings, including sexual content. Make errors, including producing incorrect information as if it were factual. Generate irrelevant or repetitive outputs.
- Recommendations for future work: Indirect users should be made aware when the content they're working with is created by the LLM. Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary. Models pretrained with the LLM should include an updated Model Card. Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
- We hope that the release of GPT-SW3, as well as information around our model training process, will increase open science around both large language models in specific and natural language processing and deep learning in general.
# GPT-SW3 Datasheet
- We follow the recommendations of Gebru et al. (2021) and provide a datasheet for the dataset used to train GPT-SW3.
# Motivation
- For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description. Pre-training of Large Language Models (LLM), such as GPT-3 (T. B. Brown et al., 2020), Gopher (J. W. Rae et al., 2022), BLOOM (T. L. Scao et al., 2022), etc. require 100s or even 1000s GBs of text data, with recent studies (Chinchilla: J. Hoffmann et al., 2022) suggesting that the scale of the training data is even more important than previously imagined. Therefore, in order to train Swedish LLMs, we needed a large scale Swedish dataset of high quality. Since no such datasets existed before this initiative, we collected data in the Nordic and English languages.
- Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? The Strategic Initiative Natural Language Understanding at AI Sweden has established a new research environment in which collaboration is key. The core team working on the creation of the dataset is the NLU research group at AI Sweden. This group consists of researchers and developers from AI Sweden (Lindholmen Science Park AB) and RISE.
- Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. The Swedish Innovation Agency (Vinnova) has funded this work across several different grants, including 2019-02996 and 2022-00949.
- Any other comments? No.
# Composition
- What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description. The instances are textual documents categorized by language and document type. The dataset is a filtered and deduplicated collection that includes the following sources:
- Books
- Litteraturbanken (https://litteraturbanken.se/)
- The Pile
- Articles
- Diva (https://www.diva-portal.org/)
- The Pile: PubMed
- The Pile: ArXiv
- Code
- Code Parrot: Github code (https://huggingface.co/datasets/codeparrot/github-code)
- Conversational
- Familjeliv (https://www.familjeliv.se/)
- Flashback (https://flashback.se/)
- Datasets collected through Parlai (see Appendix in data paper for complete list) (https://github.com/facebookresearch/ParlAI)
- Pushshift.io Reddit dataset, developed in Baumgartner et al. (2020) and processed in Roller et al. (2021)
- Math
- English Math dataset generated with code from DeepMind (D. Saxton et al., 2019)
- Swedish Math dataset, generated as above with manually translated templates
- Miscellaneous
- Summarization data (https://www.ida.liu.se/~arnjo82/papers/clarin-21-julius.pdf)
- OPUS, the open parallel corpus (https://opus.nlpl.eu/)
- Movie scripts (https://github.com/Aveek-Saha/Movie-Script-Database)
- Natural Instructions (https://github.com/allenai/natural-instructions)
- P3 (Public Pool of Prompts), (https://huggingface.co/datasets/bigscience/P3)
- The Norwegian Colossal Corpus (https://huggingface.co/datasets/NbAiLab/NCC)
- Danish Gigaword (https://gigaword.dk/)
- Icelandic Gigaword (https://clarin.is/en/resources/gigaword/)
- The Pile: Stack Exchange
- Web Common Crawl
- Web data from the project LES (Linguistic Explorations of Societies, https://les.gu.se).
- Multilingual C4 (MC4), prepared by AllenAI from C4 (C. Raffel et al., 2019)
- Open Super-large Crawled Aggregated coRpus (OSCAR) (P. O. Suarez, 2019)
- The Pile: Open Web Text
- Web Sources
- Various public Swedish website scrapes (see Appendix in data paper)
- Familjeliv Articles
- Public Swedish Job Ads from JobTech/Arbetsfรถrmedlingen
- Wikipedia
- Official Wikipedia dumps
- How many instances are there in total (of each type, if appropriate)? The training data consists of 1.1TB UTF-8 encoded text, containing 660M documents with a total of 320B tokens.
- Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable). The subset of our dataset that comes from multilingual Common Crawl datasets (MC4, Oscar), are filtered by language to only include Swedish, Norwegian, Danish, and Icelandic. From The Pile, we included only the parts that typically are of highest textual quality or complemented the rest of our dataset with sources we otherwise lacked (e.g. books). The remainder of the dataset was collected from the above sources.
- What data does each instance consist of? โRawโ data (e.g., unprocessed text or images) or features? In either case, please provide a description. Each instance consists of raw text data.
- Is there a label or target associated with each instance? If so, please provide a description. No.
- Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. No.
- Are relationships between individual instances made explicit (e.g., usersโ movie ratings, social network links)? If so, please describe how these relationships are made explicit. There are no explicit relationships between individual instances.
- Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them. There are no explicit splits recommended for this dataset. When pre-training the model, a random split for train, dev, test is set to 99.99%, 0.08%, 0.02% respectively, and is sampled proportionally to each subsetโs weight and size. The weight of each subset was manually decided beforehand. These decisions were made considering the dataโs value, source, and language, to form a representative and balanced pre-training corpus.
- Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. The dataset is a collection of many sources, some of which naturally contain some overlap. Although we have performed deduplication, some overlap may still remain. Furthermore, there may be some noise remaining from artifacts originating in Common Crawl datasets, that have been missed by our data filtering process. Except for these, we are not aware of any errors, sources of noise, or redundancies.
- Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? The dataset is self-contained.
- Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. The dataset contains subsets of public Common Crawl, Reddit, Familjeliv and Flashback. These could contain sentences that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety.
- Does the dataset relate to people? If not, you may skip the remaining questions in this section. Some documents of this data relate to people, such as news articles, Wikipedia descriptions, etc.
- Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset. No, the dataset does not explicitly include subpopulation identification.
- Any other comments? No.
# Collection Process
- How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how. N/A. The dataset is a union of publicly available datasets and sources.
- What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated? The data was downloaded from the internet.
- If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? Please see previous answers for how parts of the dataset were selected.
- Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? This data is mined, filtered and sampled by machines.
- Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created. The dataset was collected during the period June 2021 to June 2022. The creation of the collected sources varies, with e.g. Common Crawl data that have been continuously collected over 12 years.
- Does the dataset relate to people? If not, you may skip the remainder of the questions in this section. Yes. The texts have been produced by people. Any personal information potentially present in publicly available data sources and thus in the created dataset is of no interest to the collection and use of the dataset.
- Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation. Yes.
- Any other comments? No.
- Preprocessing/cleaning/labeling
- Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remainder of the questions in this section. The dataset was filtered and re-formatted on a document-level using standard procedures, inspired by the work in The BigScience ROOTS Corpus (H. Laurenรงon et al., 2022) and Gopher (J. W. Rae et al., 2022). This was done with the goal of achieving a consistent text format throughout the dataset, and to remove documents that did not meet our textual quality requirements (e.g. repetitiveness). Furthermore, the dataset was deduplicated to remedy the overlap between collected subsets using the MinHash algorithm, similar to the method used in GPT-3 and The Pile, and described in greater detail in โDeduplicating Training Data Makes Language Models Betterโ (K. Lee et al., 2021).
- Was the โrawโ data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the โrawโ data. The โrawโ component datasets are publicly available in their respective locations.
- Any other comments? No.
# Uses
- Has the dataset been used for any tasks already? If so, please provide a description. The dataset was used to pre-train the GPT-SW3 models.
- Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point. N/A.
- What (other) tasks could the dataset be used for? The data can be used to pre-train language models, which are foundations for many current and future language tasks.
- Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms? The dataset is probably quite representative of Swedish internet discourse in general, and of the Swedish public sector, but we know that this data does not necessarily reflect the entire Swedish population.
- Are there tasks for which the dataset should not be used? If so, please provide a description. None that we are currently aware of.
- Any other comments? No.
# Distribution
- Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description. No.
- How will the dataset distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)? N/A.
- When will the dataset be distributed? N/A.
- Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. N/A.
- Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. N/A.
- Any other comments? No.
# Maintenance
- Who is supporting/hosting/maintaining the dataset? AI Sweden at Lindholmen Science Park AB.
- How can the owner/curator/manager of the dataset be contacted (e.g., email address)? nlu@ai.se
- Is there an erratum? If so, please provide a link or other access point. N/A.
- Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)? Currently, there are no plans for updating the dataset.
- If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced. Read the privacy policy for the NLU initiative at AI Sweden [here](https://www.ai.se/en/privacy-policy-nlu).
- Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to users. N/A.
- If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/ verified? If so, please describe how. If not, why not? Is there a process for communicating/ distributing these contributions to other users? If so, please provide a description. Not at this time.
- Any other comments? No.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-20b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 35.83 |
| ARC (25-shot) | 41.81 |
| HellaSwag (10-shot) | 68.75 |
| MMLU (5-shot) | 28.47 |
| TruthfulQA (0-shot) | 37.1 |
| Winogrande (5-shot) | 67.17 |
| GSM8K (5-shot) | 0.99 |
| DROP (3-shot) | 6.52 |
|
AI-Sweden-Models/gpt-sw3-6.7b
|
AI-Sweden-Models
| 2024-01-29T13:20:53Z | 1,871 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"en",
"sv",
"no",
"da",
"is",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-12-14T12:33:22Z |
---
license: other
language:
- en
- sv
- 'no'
- da
- is
---
# Model description
[AI Sweden](https://huggingface.co/AI-Sweden-Models/)
**Base models**
[GPT-Sw3 126M](https://huggingface.co/AI-Sweden-Models/gpt-sw3-126m/) | [GPT-Sw3 356M](https://huggingface.co/AI-Sweden-Models/gpt-sw3-356m/) | [GPT-Sw3 1.3B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b/)
[GPT-Sw3 6.7B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b/) | [GPT-Sw3 6.7B v2](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2/) | [GPT-Sw3 20B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b/)
[GPT-Sw3 40B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-40b/)
**Instruct models**
[GPT-Sw3 126M Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-126m-instruct/) | [GPT-Sw3 356M Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-356m-instruct/) | [GPT-Sw3 1.3B Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b-instruct/)
[GPT-Sw3 6.7B v2 Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct/) | [GPT-Sw3 20B Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b-instruct/)
**Quantized models**
[GPT-Sw3 6.7B v2 Instruct 4-bit gptq](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct-4bit-gptq) | [GPT-Sw3 20B Instruct 4-bit gptq](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b-instruct-4bit-gptq)
GPT-SW3 is a collection of large decoder-only pretrained transformer language models that were developed by AI Sweden in collaboration with RISE and the WASP WARA for Media and Language. GPT-SW3 has been trained on a dataset containing 320B tokens in Swedish, Norwegian, Danish, Icelandic, English, and programming code. The model was pretrained using a causal language modeling (CLM) objective utilizing the NeMo Megatron GPT implementation.
# Intended use
GPT-SW3 is an autoregressive large language model that is capable of generating coherent text in 5 different languages, and 4 programming languages. GPT-SW3 can also be instructed to perform text tasks that it has not been explicitly trained for, by casting them as text generation tasks.
# Limitations
Like other large language models for which the diversity (or lack thereof) of training data induces downstream impact on the quality of our model, GPT-SW3 has limitations in terms of for example bias and safety. GPT-SW3 can also have quality issues in terms of generation diversity and hallucination. By releasing with the modified RAIL license, we also hope to increase communication, transparency, and the study of large language models. The model may: overrepresent some viewpoints and underrepresent others, contain stereotypes, generate hateful, abusive, violent, discriminatory or prejudicial language. The model may make errors, including producing incorrect information as if it were factual, it may generate irrelevant or repetitive outputs, and content that may not be appropriate for all settings, including sexual content.
# How to use
To be able to access the model from Python, since this is a private repository, you have to log in with your access token. This can be done with `huggingface-cli login`, see [HuggingFace Quick Start Guide](https://huggingface.co/docs/huggingface_hub/quick-start#login) for more information.
The following code snippet loads our tokenizer & model, and uses the GPU if available.
```python
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
# Initialize Variables
model_name = "AI-Sweden-Models/gpt-sw3-6.7b"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
prompt = "Trรคd รคr fina fรถr att"
# Initialize Tokenizer & Model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
model.to(device)
```
Generating text using the `generate` method is done as follows:
```python
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(device)
generated_token_ids = model.generate(
inputs=input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.6,
top_p=1,
)[0]
generated_text = tokenizer.decode(generated_token_ids)
```
A convenient alternative to the `generate` method is the HuggingFace pipeline, which handles most of the work for you:
```python
generator = pipeline('text-generation', tokenizer=tokenizer, model=model, device=device)
generated = generator(prompt, max_new_tokens=100, do_sample=True, temperature=0.6, top_p=1)[0]["generated_text"]
```
# Compliance
The release of GPT-SW3 consists of model weights, a configuration file, a tokenizer file and a vocabulary file. None of these files contain any personally identifiable information (PII) or any copyrighted material.
# GPT-SW3 Model Card
Following Mitchell et al. (2018), we provide a model card for GPT-SW3.
# Model Details
- Person or organization developing model: GPT-SW3 was developed by AI Sweden in collaboration with RISE and the WASP WARA for Media and Language.
- Model date: GPT-SW3 date of release 2022-12-20
- Model version: This is the second generation of GPT-SW3.
- Model type: GPT-SW3 is a large decoder-only transformer language model.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features: GPT-SW3 was trained with the NeMo Megatron GPT implementation.
- Paper or other resource for more information: N/A.
- License: [LICENSE](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b/blob/main/LICENSE).
- Where to send questions or comments about the model: nlu@ai.se
# Intended Use
- Primary intended uses: We pre-release GPT-SW3 for research and evaluation of the capabilities of Large Language Models for the Nordic languages. This is an important step in the process of knowledge building for LLMs, validating the model and collecting feedback on both what works well and what does not.
- Primary intended users: Organizations and individuals in the Nordic NLP ecosystem who can contribute to the validation and testing of the models and provide feedback to the community.
- Out-of-scope use cases: See the modified RAIL license.
# Data, Limitations, and Recommendations
- Data selection for training: Training data for GPT-SW3 was selected based on a combination of breadth and availability. See our Datasheet for more detailed information on the data used to train our model.
- Data selection for evaluation: N/A
- Limitations: Like other large language models for which the diversity (or lack thereof) of training data induces downstream impact on the quality of our model, GPT-SW3 has limitations in terms of bias and safety. GPT-SW3 can also have quality issues in terms of generation diversity and hallucination. In general, GPT-SW3 is not immune from the plethora of issues that plague modern large language models. By releasing with the modified RAIL license, we also hope to increase communication, transparency, and the study of large language models. The model may: Overrepresent some viewpoints and underrepresent others. Contain stereotypes. Generate: Hateful, abusive, or violent language. Discriminatory or prejudicial language. Content that may not be appropriate for all settings, including sexual content. Make errors, including producing incorrect information as if it were factual. Generate irrelevant or repetitive outputs.
- Recommendations for future work: Indirect users should be made aware when the content they're working with is created by the LLM. Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary. Models pretrained with the LLM should include an updated Model Card. Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
- We hope that the release of GPT-SW3, as well as information around our model training process, will increase open science around both large language models in specific and natural language processing and deep learning in general.
# GPT-SW3 Datasheet
- We follow the recommendations of Gebru et al. (2021) and provide a datasheet for the dataset used to train GPT-SW3.
# Motivation
- For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description. Pre-training of Large Language Models (LLM), such as GPT-3 (T. B. Brown et al., 2020), Gopher (J. W. Rae et al., 2022), BLOOM (T. L. Scao et al., 2022), etc. require 100s or even 1000s GBs of text data, with recent studies (Chinchilla: J. Hoffmann et al., 2022) suggesting that the scale of the training data is even more important than previously imagined. Therefore, in order to train Swedish LLMs, we needed a large scale Swedish dataset of high quality. Since no such datasets existed before this initiative, we collected data in the Nordic and English languages.
- Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? The Strategic Initiative Natural Language Understanding at AI Sweden has established a new research environment in which collaboration is key. The core team working on the creation of the dataset is the NLU research group at AI Sweden. This group consists of researchers and developers from AI Sweden (Lindholmen Science Park AB) and RISE.
- Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. The Swedish Innovation Agency (Vinnova) has funded this work across several different grants, including 2019-02996 and 2022-00949.
- Any other comments? No.
# Composition
- What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description. The instances are textual documents categorized by language and document type. The dataset is a filtered and deduplicated collection that includes the following sources:
- Books
- Litteraturbanken (https://litteraturbanken.se/)
- The Pile
- Articles
- Diva (https://www.diva-portal.org/)
- The Pile: PubMed
- The Pile: ArXiv
- Code
- Code Parrot: Github code (https://huggingface.co/datasets/codeparrot/github-code)
- Conversational
- Familjeliv (https://www.familjeliv.se/)
- Flashback (https://flashback.se/)
- Datasets collected through Parlai (see Appendix in data paper for complete list) (https://github.com/facebookresearch/ParlAI)
- Pushshift.io Reddit dataset, developed in Baumgartner et al. (2020) and processed in Roller et al. (2021)
- Math
- English Math dataset generated with code from DeepMind (D. Saxton et al., 2019)
- Swedish Math dataset, generated as above with manually translated templates
- Miscellaneous
- Summarization data (https://www.ida.liu.se/~arnjo82/papers/clarin-21-julius.pdf)
- OPUS, the open parallel corpus (https://opus.nlpl.eu/)
- Movie scripts (https://github.com/Aveek-Saha/Movie-Script-Database)
- Natural Instructions (https://github.com/allenai/natural-instructions)
- P3 (Public Pool of Prompts), (https://huggingface.co/datasets/bigscience/P3)
- The Norwegian Colossal Corpus (https://huggingface.co/datasets/NbAiLab/NCC)
- Danish Gigaword (https://gigaword.dk/)
- Icelandic Gigaword (https://clarin.is/en/resources/gigaword/)
- The Pile: Stack Exchange
- Web Common Crawl
- Web data from the project LES (Linguistic Explorations of Societies, https://les.gu.se).
- Multilingual C4 (MC4), prepared by AllenAI from C4 (C. Raffel et al., 2019)
- Open Super-large Crawled Aggregated coRpus (OSCAR) (P. O. Suarez, 2019)
- The Pile: Open Web Text
- Web Sources
- Various public Swedish website scrapes (see Appendix in data paper)
- Familjeliv Articles
- Public Swedish Job Ads from JobTech/Arbetsfรถrmedlingen
- Wikipedia
- Official Wikipedia dumps
- How many instances are there in total (of each type, if appropriate)? The training data consists of 1.1TB UTF-8 encoded text, containing 660M documents with a total of 320B tokens.
- Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable). The subset of our dataset that comes from multilingual Common Crawl datasets (MC4, Oscar), are filtered by language to only include Swedish, Norwegian, Danish, and Icelandic. From The Pile, we included only the parts that typically are of highest textual quality or complemented the rest of our dataset with sources we otherwise lacked (e.g. books). The remainder of the dataset was collected from the above sources.
- What data does each instance consist of? โRawโ data (e.g., unprocessed text or images) or features? In either case, please provide a description. Each instance consists of raw text data.
- Is there a label or target associated with each instance? If so, please provide a description. No.
- Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. No.
- Are relationships between individual instances made explicit (e.g., usersโ movie ratings, social network links)? If so, please describe how these relationships are made explicit. There are no explicit relationships between individual instances.
- Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them. There are no explicit splits recommended for this dataset. When pre-training the model, a random split for train, dev, test is set to 99.99%, 0.08%, 0.02% respectively, and is sampled proportionally to each subsetโs weight and size. The weight of each subset was manually decided beforehand. These decisions were made considering the dataโs value, source, and language, to form a representative and balanced pre-training corpus.
- Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. The dataset is a collection of many sources, some of which naturally contain some overlap. Although we have performed deduplication, some overlap may still remain. Furthermore, there may be some noise remaining from artifacts originating in Common Crawl datasets, that have been missed by our data filtering process. Except for these, we are not aware of any errors, sources of noise, or redundancies.
- Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? The dataset is self-contained.
- Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. The dataset contains subsets of public Common Crawl, Reddit, Familjeliv and Flashback. These could contain sentences that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety.
- Does the dataset relate to people? If not, you may skip the remaining questions in this section. Some documents of this data relate to people, such as news articles, Wikipedia descriptions, etc.
- Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset. No, the dataset does not explicitly include subpopulation identification.
- Any other comments? No.
# Collection Process
- How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how. N/A. The dataset is a union of publicly available datasets and sources.
- What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated? The data was downloaded from the internet.
- If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? Please see previous answers for how parts of the dataset were selected.
- Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? This data is mined, filtered and sampled by machines.
- Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created. The dataset was collected during the period June 2021 to June 2022. The creation of the collected sources varies, with e.g. Common Crawl data that have been continuously collected over 12 years.
- Does the dataset relate to people? If not, you may skip the remainder of the questions in this section. Yes. The texts have been produced by people. Any personal information potentially present in publicly available data sources and thus in the created dataset is of no interest to the collection and use of the dataset.
- Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation. Yes.
- Any other comments? No.
- Preprocessing/cleaning/labeling
- Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remainder of the questions in this section. The dataset was filtered and re-formatted on a document-level using standard procedures, inspired by the work in The BigScience ROOTS Corpus (H. Laurenรงon et al., 2022) and Gopher (J. W. Rae et al., 2022). This was done with the goal of achieving a consistent text format throughout the dataset, and to remove documents that did not meet our textual quality requirements (e.g. repetitiveness). Furthermore, the dataset was deduplicated to remedy the overlap between collected subsets using the MinHash algorithm, similar to the method used in GPT-3 and The Pile, and described in greater detail in โDeduplicating Training Data Makes Language Models Betterโ (K. Lee et al., 2021).
- Was the โrawโ data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the โrawโ data. The โrawโ component datasets are publicly available in their respective locations.
- Any other comments? No.
# Uses
- Has the dataset been used for any tasks already? If so, please provide a description. The dataset was used to pre-train the GPT-SW3 models.
- Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point. N/A.
- What (other) tasks could the dataset be used for? The data can be used to pre-train language models, which are foundations for many current and future language tasks.
- Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms? The dataset is probably quite representative of Swedish internet discourse in general, and of the Swedish public sector, but we know that this data does not necessarily reflect the entire Swedish population.
- Are there tasks for which the dataset should not be used? If so, please provide a description. None that we are currently aware of.
- Any other comments? No.
# Distribution
- Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description. No.
- How will the dataset distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)? N/A.
- When will the dataset be distributed? N/A.
- Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. N/A.
- Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. N/A.
- Any other comments? No.
# Maintenance
- Who is supporting/hosting/maintaining the dataset? AI Sweden at Lindholmen Science Park AB.
- How can the owner/curator/manager of the dataset be contacted (e.g., email address)? nlu@ai.se
- Is there an erratum? If so, please provide a link or other access point. N/A.
- Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)? Currently, there are no plans for updating the dataset.
- If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced. Read the privacy policy for the NLU initiative at AI Sweden [here](https://www.ai.se/en/privacy-policy-nlu).
- Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to users. N/A.
- If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/ verified? If so, please describe how. If not, why not? Is there a process for communicating/ distributing these contributions to other users? If so, please provide a description. Not at this time.
- Any other comments? No.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-6.7b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 33.18 |
| ARC (25-shot) | 36.35 |
| HellaSwag (10-shot) | 60.75 |
| MMLU (5-shot) | 26.0 |
| TruthfulQA (0-shot) | 39.04 |
| Winogrande (5-shot) | 60.69 |
| GSM8K (5-shot) | 0.53 |
| DROP (3-shot) | 8.92 |
|
AI-Sweden-Models/gpt-sw3-1.3b
|
AI-Sweden-Models
| 2024-01-29T13:20:38Z | 4,122 | 4 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"da",
"sv",
"no",
"en",
"is",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-12-14T12:33:00Z |
---
license: apache-2.0
language:
- da
- sv
- 'no'
- en
- is
---
# Model description
[AI Sweden](https://huggingface.co/AI-Sweden-Models/)
**Base models**
[GPT-Sw3 126M](https://huggingface.co/AI-Sweden-Models/gpt-sw3-126m/) | [GPT-Sw3 356M](https://huggingface.co/AI-Sweden-Models/gpt-sw3-356m/) | [GPT-Sw3 1.3B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b/)
[GPT-Sw3 6.7B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b/) | [GPT-Sw3 6.7B v2](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2/) | [GPT-Sw3 20B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b/)
[GPT-Sw3 40B](https://huggingface.co/AI-Sweden-Models/gpt-sw3-40b/)
**Instruct models**
[GPT-Sw3 126M Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-126m-instruct/) | [GPT-Sw3 356M Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-356m-instruct/) | [GPT-Sw3 1.3B Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b-instruct/)
[GPT-Sw3 6.7B v2 Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct/) | [GPT-Sw3 20B Instruct](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b-instruct/)
**Quantized models**
[GPT-Sw3 6.7B v2 Instruct 4-bit gptq](https://huggingface.co/AI-Sweden-Models/gpt-sw3-6.7b-v2-instruct-4bit-gptq) | [GPT-Sw3 20B Instruct 4-bit gptq](https://huggingface.co/AI-Sweden-Models/gpt-sw3-20b-instruct-4bit-gptq)
GPT-SW3 is a collection of large decoder-only pretrained transformer language models that were developed by AI Sweden in collaboration with RISE and the WASP WARA for Media and Language. GPT-SW3 has been trained on a dataset containing 320B tokens in Swedish, Norwegian, Danish, Icelandic, English, and programming code. The model was pretrained using a causal language modeling (CLM) objective utilizing the NeMo Megatron GPT implementation.
# Intended use
GPT-SW3 is an autoregressive large language model that is capable of generating coherent text in 5 different languages, and 4 programming languages. GPT-SW3 can also be instructed to perform text tasks that it has not been explicitly trained for, by casting them as text generation tasks. AI Sweden shares GPT-SW3 in a controlled pre-release with organizations and individuals in the Nordic NLP ecosystem who can contribute to the validation and testing of the models and provide feedback to the community. This is an important step in the process of validating the model and collecting feedback on both what works well and what does not.
# Limitations
Like other large language models for which the diversity (or lack thereof) of training data induces downstream impact on the quality of our model, GPT-SW3 has limitations in terms of for example bias and safety. GPT-SW3 can also have quality issues in terms of generation diversity and hallucination. By releasing with the modified RAIL license, we also hope to increase communication, transparency, and the study of large language models. The model may: overrepresent some viewpoints and underrepresent others, contain stereotypes, generate hateful, abusive, violent, discriminatory or prejudicial language. The model may make errors, including producing incorrect information as if it were factual, it may generate irrelevant or repetitive outputs, and content that may not be appropriate for all settings, including sexual content.
# How to use
To be able to access the model from Python, since this is a private repository, you have to log in with your access token. This can be done with `huggingface-cli login`, see [HuggingFace Quick Start Guide](https://huggingface.co/docs/huggingface_hub/quick-start#login) for more information.
The following code snippet loads our tokenizer & model, and uses the GPU if available.
```python
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
# Initialize Variables
model_name = "AI-Sweden-Models/gpt-sw3-1.3b"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
prompt = "Trรคd รคr fina fรถr att"
# Initialize Tokenizer & Model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
model.to(device)
```
Generating text using the `generate` method is done as follows:
```python
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(device)
generated_token_ids = model.generate(
inputs=input_ids,
max_new_tokens=100,
do_sample=True,
temperature=0.6,
top_p=1,
)[0]
generated_text = tokenizer.decode(generated_token_ids)
```
A convenient alternative to the `generate` method is the HuggingFace pipeline, which handles most of the work for you:
```python
generator = pipeline('text-generation', tokenizer=tokenizer, model=model, device=device)
generated = generator(prompt, max_new_tokens=100, do_sample=True, temperature=0.6, top_p=1)[0]["generated_text"]
```
# Compliance
The release of GPT-SW3 consists of model weights, a configuration file, a tokenizer file and a vocabulary file. None of these files contain any personally identifiable information (PII) or any copyrighted material.
# GPT-SW3 Model Card
Following Mitchell et al. (2018), we provide a model card for GPT-SW3.
# Model Details
- Person or organization developing model: GPT-SW3 was developed by AI Sweden in collaboration with RISE and the WASP WARA for Media and Language.
- Model date: GPT-SW3 date of release 2022-12-20
- Model version: This is the second generation of GPT-SW3.
- Model type: GPT-SW3 is a large decoder-only transformer language model.
- Information about training algorithms, parameters, fairness constraints or other applied approaches, and features: GPT-SW3 was trained with the NeMo Megatron GPT implementation.
- Paper or other resource for more information: N/A.
- License: [LICENSE](https://huggingface.co/AI-Sweden-Models/gpt-sw3-1.3b/blob/main/LICENSE).
- Where to send questions or comments about the model: nlu@ai.se
# Intended Use
- Primary intended uses: We pre-release GPT-SW3 for research and evaluation of the capabilities of Large Language Models for the Nordic languages. This is an important step in the process of knowledge building for LLMs, validating the model and collecting feedback on both what works well and what does not.
- Primary intended users: Organizations and individuals in the Nordic NLP ecosystem who can contribute to the validation and testing of the models and provide feedback to the community.
- Out-of-scope use cases: See the modified RAIL license.
# Data, Limitations, and Recommendations
- Data selection for training: Training data for GPT-SW3 was selected based on a combination of breadth and availability. See our Datasheet for more detailed information on the data used to train our model.
- Data selection for evaluation: N/A
- Limitations: Like other large language models for which the diversity (or lack thereof) of training data induces downstream impact on the quality of our model, GPT-SW3 has limitations in terms of bias and safety. GPT-SW3 can also have quality issues in terms of generation diversity and hallucination. In general, GPT-SW3 is not immune from the plethora of issues that plague modern large language models. By releasing with the modified RAIL license, we also hope to increase communication, transparency, and the study of large language models. The model may: Overrepresent some viewpoints and underrepresent others. Contain stereotypes. Generate: Hateful, abusive, or violent language. Discriminatory or prejudicial language. Content that may not be appropriate for all settings, including sexual content. Make errors, including producing incorrect information as if it were factual. Generate irrelevant or repetitive outputs.
- Recommendations for future work: Indirect users should be made aware when the content they're working with is created by the LLM. Users should be aware of Risks and Limitations, and include an appropriate age disclaimer or blocking interface as necessary. Models pretrained with the LLM should include an updated Model Card. Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
- We hope that the release of GPT-SW3, as well as information around our model training process, will increase open science around both large language models in specific and natural language processing and deep learning in general.
# GPT-SW3 Datasheet
- We follow the recommendations of Gebru et al. (2021) and provide a datasheet for the dataset used to train GPT-SW3.
# Motivation
- For what purpose was the dataset created? Was there a specific task in mind? Was there a specific gap that needed to be filled? Please provide a description. Pre-training of Large Language Models (LLM), such as GPT-3 (T. B. Brown et al., 2020), Gopher (J. W. Rae et al., 2022), BLOOM (T. L. Scao et al., 2022), etc. require 100s or even 1000s GBs of text data, with recent studies (Chinchilla: J. Hoffmann et al., 2022) suggesting that the scale of the training data is even more important than previously imagined. Therefore, in order to train Swedish LLMs, we needed a large scale Swedish dataset of high quality. Since no such datasets existed before this initiative, we collected data in the Nordic and English languages.
- Who created the dataset (e.g., which team, research group) and on behalf of which entity (e.g., company, institution, organization)? The Strategic Initiative Natural Language Understanding at AI Sweden has established a new research environment in which collaboration is key. The core team working on the creation of the dataset is the NLU research group at AI Sweden. This group consists of researchers and developers from AI Sweden (Lindholmen Science Park AB) and RISE.
- Who funded the creation of the dataset? If there is an associated grant, please provide the name of the grantor and the grant name and number. The Swedish Innovation Agency (Vinnova) has funded this work across several different grants, including 2019-02996 and 2022-00949.
- Any other comments? No.
# Composition
- What do the instances that comprise the dataset represent (e.g., documents, photos, people, countries)? Are there multiple types of instances (e.g., movies, users, and ratings; people and interactions between them; nodes and edges)? Please provide a description. The instances are textual documents categorized by language and document type. The dataset is a filtered and deduplicated collection that includes the following sources:
- Books
- Litteraturbanken (https://litteraturbanken.se/)
- The Pile
- Articles
- Diva (https://www.diva-portal.org/)
- The Pile: PubMed
- The Pile: ArXiv
- Code
- Code Parrot: Github code (https://huggingface.co/datasets/codeparrot/github-code)
- Conversational
- Familjeliv (https://www.familjeliv.se/)
- Flashback (https://flashback.se/)
- Datasets collected through Parlai (see Appendix in data paper for complete list) (https://github.com/facebookresearch/ParlAI)
- Pushshift.io Reddit dataset, developed in Baumgartner et al. (2020) and processed in Roller et al. (2021)
- Math
- English Math dataset generated with code from DeepMind (D. Saxton et al., 2019)
- Swedish Math dataset, generated as above with manually translated templates
- Miscellaneous
- Summarization data (https://www.ida.liu.se/~arnjo82/papers/clarin-21-julius.pdf)
- OPUS, the open parallel corpus (https://opus.nlpl.eu/)
- Movie scripts (https://github.com/Aveek-Saha/Movie-Script-Database)
- Natural Instructions (https://github.com/allenai/natural-instructions)
- P3 (Public Pool of Prompts), (https://huggingface.co/datasets/bigscience/P3)
- The Norwegian Colossal Corpus (https://huggingface.co/datasets/NbAiLab/NCC)
- Danish Gigaword (https://gigaword.dk/)
- Icelandic Gigaword (https://clarin.is/en/resources/gigaword/)
- The Pile: Stack Exchange
- Web Common Crawl
- Web data from the project LES (Linguistic Explorations of Societies, https://les.gu.se).
- Multilingual C4 (MC4), prepared by AllenAI from C4 (C. Raffel et al., 2019)
- Open Super-large Crawled Aggregated coRpus (OSCAR) (P. O. Suarez, 2019)
- The Pile: Open Web Text
- Web Sources
- Various public Swedish website scrapes (see Appendix in data paper)
- Familjeliv Articles
- Public Swedish Job Ads from JobTech/Arbetsfรถrmedlingen
- Wikipedia
- Official Wikipedia dumps
- How many instances are there in total (of each type, if appropriate)? The training data consists of 1.1TB UTF-8 encoded text, containing 660M documents with a total of 320B tokens.
- Does the dataset contain all possible instances or is it a sample (not necessarily random) of instances from a larger set? If the dataset is a sample, then what is the larger set? Is the sample representative of the larger set (e.g., geographic coverage)? If so, please describe how this representativeness was validated/verified. If it is not representative of the larger set, please describe why not (e.g., to cover a more diverse range of instances, because instances were withheld or unavailable). The subset of our dataset that comes from multilingual Common Crawl datasets (MC4, Oscar), are filtered by language to only include Swedish, Norwegian, Danish, and Icelandic. From The Pile, we included only the parts that typically are of highest textual quality or complemented the rest of our dataset with sources we otherwise lacked (e.g. books). The remainder of the dataset was collected from the above sources.
- What data does each instance consist of? โRawโ data (e.g., unprocessed text or images) or features? In either case, please provide a description. Each instance consists of raw text data.
- Is there a label or target associated with each instance? If so, please provide a description. No.
- Is any information missing from individual instances? If so, please provide a description, explaining why this information is missing (e.g., because it was unavailable). This does not include intentionally removed information, but might include, e.g., redacted text. No.
- Are relationships between individual instances made explicit (e.g., usersโ movie ratings, social network links)? If so, please describe how these relationships are made explicit. There are no explicit relationships between individual instances.
- Are there recommended data splits (e.g., training, development/validation, testing)? If so, please provide a description of these splits, explaining the rationale behind them. There are no explicit splits recommended for this dataset. When pre-training the model, a random split for train, dev, test is set to 99.99%, 0.08%, 0.02% respectively, and is sampled proportionally to each subsetโs weight and size. The weight of each subset was manually decided beforehand. These decisions were made considering the dataโs value, source, and language, to form a representative and balanced pre-training corpus.
- Are there any errors, sources of noise, or redundancies in the dataset? If so, please provide a description. The dataset is a collection of many sources, some of which naturally contain some overlap. Although we have performed deduplication, some overlap may still remain. Furthermore, there may be some noise remaining from artifacts originating in Common Crawl datasets, that have been missed by our data filtering process. Except for these, we are not aware of any errors, sources of noise, or redundancies.
- Is the dataset self-contained, or does it link to or otherwise rely on external resources (e.g., websites, tweets, other datasets)? The dataset is self-contained.
- Does the dataset contain data that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety? If so, please describe why. The dataset contains subsets of public Common Crawl, Reddit, Familjeliv and Flashback. These could contain sentences that, if viewed directly, might be offensive, insulting, threatening, or might otherwise cause anxiety.
- Does the dataset relate to people? If not, you may skip the remaining questions in this section. Some documents of this data relate to people, such as news articles, Wikipedia descriptions, etc.
- Does the dataset identify any subpopulations (e.g., by age, gender)? If so, please describe how these subpopulations are identified and provide a description of their respective distributions within the dataset. No, the dataset does not explicitly include subpopulation identification.
- Any other comments? No.
# Collection Process
- How was the data associated with each instance acquired? Was the data directly observable (e.g., raw text, movie ratings), reported by subjects (e.g., survey responses), or indirectly inferred/derived from other data (e.g., part-of-speech tags, model-based guesses for age or language)? If data was reported by subjects or indirectly inferred/derived from other data, was the data validated/verified? If so, please describe how. N/A. The dataset is a union of publicly available datasets and sources.
- What mechanisms or procedures were used to collect the data (e.g., hardware apparatus or sensor, manual human curation, software program, software API)? How were these mechanisms or procedures validated? The data was downloaded from the internet.
- If the dataset is a sample from a larger set, what was the sampling strategy (e.g., deterministic, probabilistic with specific sampling probabilities)? Please see previous answers for how parts of the dataset were selected.
- Who was involved in the data collection process (e.g., students, crowdworkers, contractors) and how were they compensated (e.g., how much were crowdworkers paid)? This data is mined, filtered and sampled by machines.
- Over what timeframe was the data collected? Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? If not, please describe the timeframe in which the data associated with the instances was created. The dataset was collected during the period June 2021 to June 2022. The creation of the collected sources varies, with e.g. Common Crawl data that have been continuously collected over 12 years.
- Does the dataset relate to people? If not, you may skip the remainder of the questions in this section. Yes. The texts have been produced by people. Any personal information potentially present in publicly available data sources and thus in the created dataset is of no interest to the collection and use of the dataset.
- Has an analysis of the potential impact of the dataset and its use on data subjects (e.g., a data protection impact analysis) been conducted? If so, please provide a description of this analysis, including the outcomes, as well as a link or other access point to any supporting documentation. Yes.
- Any other comments? No.
- Preprocessing/cleaning/labeling
- Was any preprocessing/cleaning/labeling of the data done (e.g., discretization or bucketing, tokenization, part-of-speech tagging, SIFT feature extraction, removal of instances, processing of missing values)? If so, please provide a description. If not, you may skip the remainder of the questions in this section. The dataset was filtered and re-formatted on a document-level using standard procedures, inspired by the work in The BigScience ROOTS Corpus (H. Laurenรงon et al., 2022) and Gopher (J. W. Rae et al., 2022). This was done with the goal of achieving a consistent text format throughout the dataset, and to remove documents that did not meet our textual quality requirements (e.g. repetitiveness). Furthermore, the dataset was deduplicated to remedy the overlap between collected subsets using the MinHash algorithm, similar to the method used in GPT-3 and The Pile, and described in greater detail in โDeduplicating Training Data Makes Language Models Betterโ (K. Lee et al., 2021).
- Was the โrawโ data saved in addition to the preprocessed/cleaned/labeled data (e.g., to support unanticipated future uses)? If so, please provide a link or other access point to the โrawโ data. The โrawโ component datasets are publicly available in their respective locations.
- Any other comments? No.
# Uses
- Has the dataset been used for any tasks already? If so, please provide a description. The dataset was used to pre-train the GPT-SW3 models.
- Is there a repository that links to any or all papers or systems that use the dataset? If so, please provide a link or other access point. N/A.
- What (other) tasks could the dataset be used for? The data can be used to pre-train language models, which are foundations for many current and future language tasks.
- Is there anything about the composition of the dataset or the way it was collected and preprocessed/cleaned/labeled that might impact future uses? For example, is there anything that a future user might need to know to avoid uses that could result in unfair treatment of individuals or groups (e.g., stereotyping, quality of service issues) or other undesirable harms (e.g., financial harms, legal risks) If so, please provide a description. Is there anything a future user could do to mitigate these undesirable harms? The dataset is probably quite representative of Swedish internet discourse in general, and of the Swedish public sector, but we know that this data does not necessarily reflect the entire Swedish population.
- Are there tasks for which the dataset should not be used? If so, please provide a description. None that we are currently aware of.
- Any other comments? No.
# Distribution
- Will the dataset be distributed to third parties outside of the entity (e.g., company, institution, organization) on behalf of which the dataset was created? If so, please provide a description. No.
- How will the dataset distributed (e.g., tarball on website, API, GitHub)? Does the dataset have a digital object identifier (DOI)? N/A.
- When will the dataset be distributed? N/A.
- Will the dataset be distributed under a copyright or other intellectual property (IP) license, and/or under applicable terms of use (ToU)? If so, please describe this license and/or ToU, and provide a link or other access point to, or otherwise reproduce, any relevant licensing terms or ToU, as well as any fees associated with these restrictions. N/A.
- Do any export controls or other regulatory restrictions apply to the dataset or to individual instances? If so, please describe these restrictions, and provide a link or other access point to, or otherwise reproduce, any supporting documentation. N/A.
- Any other comments? No.
# Maintenance
- Who is supporting/hosting/maintaining the dataset? AI Sweden at Lindholmen Science Park AB.
- How can the owner/curator/manager of the dataset be contacted (e.g., email address)? nlu@ai.se
- Is there an erratum? If so, please provide a link or other access point. N/A.
- Will the dataset be updated (e.g., to correct labeling errors, add new instances, delete instances)? If so, please describe how often, by whom, and how updates will be communicated to users (e.g., mailing list, GitHub)? Currently, there are no plans for updating the dataset.
- If the dataset relates to people, are there applicable limits on the retention of the data associated with the instances (e.g., were individuals in question told that their data would be retained for a fixed period of time and then deleted)? If so, please describe these limits and explain how they will be enforced. Read the privacy policy for the NLU initiative at AI Sweden [here](https://www.ai.se/en/privacy-policy-nlu).
- Will older versions of the dataset continue to be supported/hosted/maintained? If so, please describe how. If not, please describe how its obsolescence will be communicated to users. N/A.
- If others want to extend/augment/build on/contribute to the dataset, is there a mechanism for them to do so? If so, please provide a description. Will these contributions be validated/ verified? If so, please describe how. If not, why not? Is there a process for communicating/ distributing these contributions to other users? If so, please provide a description. Not at this time.
- Any other comments? No.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-Sweden-Models__gpt-sw3-1.3b)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 29.99 |
| ARC (25-shot) | 30.38 |
| HellaSwag (10-shot) | 50.4 |
| MMLU (5-shot) | 26.14 |
| TruthfulQA (0-shot) | 39.97 |
| Winogrande (5-shot) | 58.88 |
| GSM8K (5-shot) | 0.08 |
| DROP (3-shot) | 4.08 |
|
boyu0724/vit-base-patch16-224-in21k-finetuned-lora-food101
|
boyu0724
| 2024-01-29T13:11:13Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:adapter:google/vit-base-patch16-224-in21k",
"region:us"
] | null | 2024-01-29T13:04:24Z |
---
library_name: peft
base_model: google/vit-base-patch16-224-in21k
---
# 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
|
katik0/layoutlmv3-test
|
katik0
| 2024-01-29T12:57:37Z | 60 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"dataset:funsd",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-01-29T11:59:22Z |
---
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
datasets:
- funsd
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-test
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: funsd
type: funsd
metrics:
- name: Precision
type: precision
value: 0.8972868217054264
- name: Recall
type: recall
value: 0.920019870839543
- name: F1
type: f1
value: 0.9085111601667893
- name: Accuracy
type: accuracy
value: 0.8480922382027815
---
<!-- 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. -->
# layoutlmv3-test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the funsd dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8036
- Precision: 0.8973
- Recall: 0.9200
- F1: 0.9085
- Accuracy: 0.8481
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 5.26 | 100 | 0.5115 | 0.8071 | 0.8624 | 0.8338 | 0.8407 |
| No log | 10.53 | 200 | 0.4661 | 0.8730 | 0.9086 | 0.8905 | 0.8546 |
| No log | 15.79 | 300 | 0.5613 | 0.8914 | 0.9091 | 0.9001 | 0.8552 |
| No log | 21.05 | 400 | 0.6767 | 0.8937 | 0.8982 | 0.8959 | 0.8507 |
| 0.3022 | 26.32 | 500 | 0.7020 | 0.8935 | 0.9165 | 0.9049 | 0.8626 |
| 0.3022 | 31.58 | 600 | 0.7108 | 0.9040 | 0.9220 | 0.9129 | 0.8591 |
| 0.3022 | 36.84 | 700 | 0.7378 | 0.9049 | 0.9175 | 0.9112 | 0.8517 |
| 0.3022 | 42.11 | 800 | 0.7892 | 0.9026 | 0.9210 | 0.9117 | 0.8537 |
| 0.3022 | 47.37 | 900 | 0.8133 | 0.8995 | 0.9205 | 0.9099 | 0.8490 |
| 0.0223 | 52.63 | 1000 | 0.8036 | 0.8973 | 0.9200 | 0.9085 | 0.8481 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
casque/Ira
|
casque
| 2024-01-29T12:54:48Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-01-29T12:54:08Z |
---
license: creativeml-openrail-m
---
|
AshtonLKY/Whisper_ASR_ATC_v4
|
AshtonLKY
| 2024-01-29T12:50:42Z | 61 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"asr-fyp",
"generated_from_trainer",
"en",
"dataset:AshtonLKY/Whisper_ASR_ATC",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-29T01:28:31Z |
---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- asr-fyp
- generated_from_trainer
datasets:
- AshtonLKY/Whisper_ASR_ATC
model-index:
- name: Whisper_ASR_ATC
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_ASR_ATC
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the AshtonLKY/augmented_audio dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0084
- eval_wer: 6.1320
- eval_runtime: 3863.0969
- eval_samples_per_second: 3.479
- eval_steps_per_second: 0.217
- epoch: 2.98
- step: 10000
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 20000
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
kaist-ai/langbridge_encoder_tokenizer
|
kaist-ai
| 2024-01-29T12:41:48Z | 0 | 3 |
transformers
|
[
"transformers",
"multilingual",
"af",
"am",
"ar",
"az",
"be",
"bg",
"bn",
"ca",
"ceb",
"co",
"cs",
"cy",
"da",
"de",
"el",
"en",
"eo",
"es",
"et",
"eu",
"fa",
"fi",
"fil",
"fr",
"fy",
"ga",
"gd",
"gl",
"gu",
"ha",
"haw",
"hi",
"hmn",
"ht",
"hu",
"hy",
"ig",
"is",
"it",
"iw",
"ja",
"jv",
"ka",
"kk",
"km",
"kn",
"ko",
"ku",
"ky",
"la",
"lb",
"lo",
"lt",
"lv",
"mg",
"mi",
"mk",
"ml",
"mn",
"mr",
"ms",
"mt",
"my",
"ne",
"nl",
"no",
"ny",
"pa",
"pl",
"ps",
"pt",
"ro",
"ru",
"sd",
"si",
"sk",
"sl",
"sm",
"sn",
"so",
"sq",
"sr",
"st",
"su",
"sv",
"sw",
"ta",
"te",
"tg",
"th",
"tr",
"uk",
"und",
"ur",
"uz",
"vi",
"xh",
"yi",
"yo",
"zh",
"zu",
"arxiv:2401.10695",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-01-23T10:23:07Z |
---
license: apache-2.0
language:
- multilingual
- af
- am
- ar
- az
- be
- bg
- bn
- ca
- ceb
- co
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fil
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- haw
- hi
- hmn
- ht
- hu
- hy
- ig
- is
- it
- iw
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lb
- lo
- lt
- lv
- mg
- mi
- mk
- ml
- mn
- mr
- ms
- mt
- my
- ne
- nl
- no
- ny
- pa
- pl
- ps
- pt
- ro
- ru
- sd
- si
- sk
- sl
- sm
- sn
- so
- sq
- sr
- st
- su
- sv
- sw
- ta
- te
- tg
- th
- tr
- uk
- und
- ur
- uz
- vi
- xh
- yi
- yo
- zh
- zu
library_name: transformers
---
## Links for Reference
- **Repository: https://github.com/kaistAI/LangBridge**
- **Paper: [LangBridge: Multilingual Reasoning Without Multilingual Supervision](https://arxiv.org/pdf/2401.10695.pdf)**
- **Point of Contact: dkyoon@kaist.ac.kr**
# TL;DR
๐คLMs good at reasoning are mostly English-centric (MetaMath, Orca 2, etc).
๐Letโs adapt them to solve multilingual tasks. BUT without using multilingual data!
LangBridge โbridgesโ mT5 encoder and the target LM together while utilizing only English data. In test time, LangBridge models can solve multilingual reasoning tasks effectively.
# Usage
This is the tokenizer used for the encoder models of LangBridge. LangBridge models require two tokenizers, one for the encoder model and one for the language model. To the best of our knowledge there is no way of uploading two tokenizers for a model. So this seperate repository was created.
Please refer to the [Github repository](https://github.com/kaistAI/LangBridge) for detailed usage examples.
# Related Models
[Check out other LangBridge models.](https://huggingface.co/collections/kaist-ai/langbridge-65afbbdae50627e40ca58f9a)
We have:
- Llama 2
- Llemma
- MetaMath
- Code Llama
- Orca 2
# Citation
If you find the following model helpful, please consider citing our paper!
**BibTeX:**
```bibtex
@misc{yoon2024langbridge,
title={LangBridge: Multilingual Reasoning Without Multilingual Supervision},
author={Dongkeun Yoon and Joel Jang and Sungdong Kim and Seungone Kim and Sheikh Shafayat and Minjoon Seo},
year={2024},
eprint={2401.10695},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Owentaku/distilbert-base-uncased-finetuned-imdb
|
Owentaku
| 2024-01-29T12:36:13Z | 92 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2024-01-26T09:56:28Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4118
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7024 | 1.0 | 157 | 2.4965 |
| 2.5792 | 2.0 | 314 | 2.4280 |
| 2.5354 | 3.0 | 471 | 2.4508 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
jpodivin/rh_qa_model
|
jpodivin
| 2024-01-29T12:34:09Z | 87 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-cased",
"base_model:finetune:distilbert/distilbert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-24T10:41:06Z |
---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
model-index:
- name: rh_qa_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. -->
# rh_qa_model
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0857
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 9 | 5.4568 |
| No log | 2.0 | 18 | 4.7897 |
| No log | 3.0 | 27 | 4.6445 |
| No log | 4.0 | 36 | 3.9367 |
| No log | 5.0 | 45 | 3.4457 |
| No log | 6.0 | 54 | 3.3149 |
| No log | 7.0 | 63 | 2.6427 |
| No log | 8.0 | 72 | 2.6698 |
| No log | 9.0 | 81 | 2.2418 |
| No log | 10.0 | 90 | 2.3653 |
| No log | 11.0 | 99 | 2.1887 |
| No log | 12.0 | 108 | 2.1629 |
| No log | 13.0 | 117 | 2.2699 |
| No log | 14.0 | 126 | 2.1080 |
| No log | 15.0 | 135 | 2.1836 |
| No log | 16.0 | 144 | 2.0967 |
| No log | 17.0 | 153 | 2.1418 |
| No log | 18.0 | 162 | 2.0863 |
| No log | 19.0 | 171 | 2.0778 |
| No log | 20.0 | 180 | 2.0857 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Mukalingam0813/swedish-intent-classification-mulBert-cased
|
Mukalingam0813
| 2024-01-29T12:31:00Z | 93 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-29T11:48:32Z |
---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swedish-intent-classification-mulBert-cased
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. -->
# swedish-intent-classification-mulBert-cased
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1484
- Accuracy: 0.9746
## 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1705 | 1.0 | 9799 | 0.1718 | 0.9647 |
| 0.1192 | 2.0 | 19598 | 0.1480 | 0.9713 |
| 0.0689 | 3.0 | 29397 | 0.1484 | 0.9746 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
simmo/legal-summarizer-7b
|
simmo
| 2024-01-29T12:29:07Z | 27 | 7 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"text-generation-inference",
"unsloth",
"mistral",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-01-29T00:05:18Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
base_model: unsloth/mistral-7b-instruct-v0.2-bnb-4bit
---
# What is this?
Dump the full text of a court case into this model and it will summarize it for you
# Uploaded model
- **Developed by:** simmo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
# Prompt
```
"""
Below is an instruction that describes a task, paired with an input that provides further context.
Write a response that appropriately completes the request.
### Instruction:
You are a legal summarization assistant that summarizes any document passed to you. To effectively summarize a legal case, start by providing a succinct overview of the case name, court, and date of judgment. Highlight the essential facts, including key events, parties involved, and relevant dates. Identify the legal issues, arguments presented by each party, and the court's final decision. Distill complex legal concepts into clear and concise language, avoiding unnecessary details. Emphasize any precedent-setting aspects or significant legal doctrines. Structure the summary logically, ensuring a smooth flow of information. Consider the intended audience and purpose of the summary, tailoring the level of detail accordingly. Review and edit the summary for clarity, coherence, and accuracy. If applicable, include references to relevant statutes or case law. Overall, aim for a balanced and informative summary that captures the critical aspects of the case. The summary should be a json object with the following keys: Case Name, Court, Date of Judgement, Overview, Facts, Legal Issue, Arguments, Courts Decision and any legal precedents (if applicable).
### Input:
{doc}
### Response:
"""
```
# Model Output
```
{
""Case Name"": "Hogle Holdings Ltd. v. National Specialty Cars Ltd.",
""Court"": "Superior Court of Justice, Cour superieure de justice",
""Date of Judgement"": "June 14, 2004",
""Overview"": "The case involves two motions: one by the plaintiff for various orders due to difficulties in obtaining document production and examination for discovery from the defendant, and another by the defendant for an order to strike out the plaintiffโs statement of defence and counterclaim for non-compliance with timetable orders.",
""Facts"": "The plaintiff encountered difficulties in obtaining document production and examination for discovery from the defendant. The defendant's response was characterized by delays and obstruction, leading the plaintiff to seek various orders including permission to search the defendant's premises for relevant documents.",
""Legal Issue"": "The legal issues revolve around the plaintiff's request for an order striking out the defendantโs statement of defence and counterclaim due to non-compliance with timetable orders, and the plaintiff's motion for permission to enter the defendantโs premises and search for relevant documents.",
""Arguments"": "The plaintiff argued that it had a right to search the defendant's premises for relevant documents and make copies of them based on Rule 32, while the defendant argued against such an order. The plaintiff also argued that it deliberately failed to comply with timetable orders to pressure the defendant into producing documents.",
""Courts Decision"": "The court denied the plaintiff's request for permission to search the defendantโs premises and make copies of relevant documents. The court also declined to strike out the defendant's statement of defence and counterclaim due to non-compliance with timetable orders, but ordered both parties not to be awarded costs on either motion.",
""Legal Precedents"": "The court did not provide any explicit legal precedents in the given case."
}
```
# Recommended Grammar
```
root ::= Case
Case ::= "{" ws "\"\"Case Name\"\":" ws string "," ws "\"\"Court\"\":" ws string "," ws "\"\"Date of Judgement\"\":" ws string "," ws "\"\"Overview\"\":" ws string "," ws "\"\"Facts\"\":" ws string "," ws "\"\"Legal Issue\"\":" ws string "," ws "\"\"Arguments\"\":" ws string "," ws "\"\"Courts Decision\"\":" ws string "," ws "\"\"Legal Precedents\"\":" ws string "}"
Caselist ::= "[]" | "[" ws Case ("," ws Case)* "]"
string ::= "\"" ([^"]*) "\""
boolean ::= "true" | "false"
ws ::= [ \t\n]*
number ::= [0-9]+ "."? [0-9]*
stringlist ::= "[" ws "]" | "[" ws string ("," ws string)* ws "]"
numberlist ::= "[" ws "]" | "[" ws string ("," ws number)* ws "]"
```
|
KarMa001/model_out_v1-5
|
KarMa001
| 2024-01-29T12:25:13Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-29T12:18:57Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-KarMa001/model_out_v1-5
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
|
prajjusy/pfet-flan-t5-base-model-3
|
prajjusy
| 2024-01-29T12:23:44Z | 2 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:prajjusy/full-finetuned-flan-t5-base-model-3",
"base_model:adapter:prajjusy/full-finetuned-flan-t5-base-model-3",
"region:us"
] | null | 2024-01-29T12:23:43Z |
---
library_name: peft
base_model: prajjusy/full-finetuned-flan-t5-base-model-3
---
# 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
|
Sidd438/model
|
Sidd438
| 2024-01-29T12:18:32Z | 91 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-29T11:54:47Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 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. -->
# model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7720
- Accuracy: 0.7489
## 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 233 | 0.9601 | 0.7178 |
| No log | 2.0 | 466 | 0.8027 | 0.7318 |
| 1.1228 | 3.0 | 699 | 0.7720 | 0.7489 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
tanatapanun/fine-tuned-BioBARTv2-20-epochs-1024-input-192-output
|
tanatapanun
| 2024-01-29T12:13:01Z | 91 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-29T11:40:16Z |
---
base_model: checkpoint_global_step_200000
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: fine-tuned-BioBARTv2-20-epochs-1024-input-192-output
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-tuned-BioBARTv2-20-epochs-1024-input-192-output
This model is a fine-tuned version of [checkpoint_global_step_200000](https://huggingface.co/checkpoint_global_step_200000) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2139
- Rouge1: 0.1795
- Rouge2: 0.0354
- Rougel: 0.1282
- Rougelsum: 0.1304
- Gen Len: 38.23
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- 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_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 151 | 6.6012 | 0.0046 | 0.0001 | 0.0045 | 0.0046 | 8.77 |
| No log | 2.0 | 302 | 1.4692 | 0.1068 | 0.0318 | 0.0933 | 0.0937 | 29.33 |
| No log | 3.0 | 453 | 1.2563 | 0.0982 | 0.0232 | 0.0736 | 0.0745 | 36.78 |
| 4.4063 | 4.0 | 604 | 1.1824 | 0.1163 | 0.033 | 0.0884 | 0.0888 | 31.46 |
| 4.4063 | 5.0 | 755 | 1.1451 | 0.1667 | 0.0343 | 0.1302 | 0.1308 | 42.16 |
| 4.4063 | 6.0 | 906 | 1.1288 | 0.1428 | 0.0268 | 0.1118 | 0.1124 | 33.8 |
| 0.9455 | 7.0 | 1057 | 1.1192 | 0.1474 | 0.035 | 0.1089 | 0.1098 | 39.35 |
| 0.9455 | 8.0 | 1208 | 1.1202 | 0.1598 | 0.0354 | 0.1227 | 0.1245 | 37.79 |
| 0.9455 | 9.0 | 1359 | 1.1227 | 0.1683 | 0.0312 | 0.1236 | 0.1247 | 50.05 |
| 0.6533 | 10.0 | 1510 | 1.1241 | 0.1744 | 0.0447 | 0.1341 | 0.1364 | 39.0 |
| 0.6533 | 11.0 | 1661 | 1.1321 | 0.1703 | 0.0411 | 0.1273 | 0.1285 | 42.15 |
| 0.6533 | 12.0 | 1812 | 1.1465 | 0.1756 | 0.0343 | 0.1258 | 0.1277 | 34.42 |
| 0.6533 | 13.0 | 1963 | 1.1560 | 0.1854 | 0.0442 | 0.1381 | 0.14 | 38.38 |
| 0.455 | 14.0 | 2114 | 1.1690 | 0.1913 | 0.0388 | 0.1371 | 0.1398 | 39.29 |
| 0.455 | 15.0 | 2265 | 1.1845 | 0.1688 | 0.0305 | 0.1205 | 0.1226 | 34.5 |
| 0.455 | 16.0 | 2416 | 1.1860 | 0.1913 | 0.039 | 0.1345 | 0.136 | 41.5 |
| 0.3282 | 17.0 | 2567 | 1.1955 | 0.1782 | 0.0344 | 0.1243 | 0.1266 | 43.78 |
| 0.3282 | 18.0 | 2718 | 1.2108 | 0.1796 | 0.0386 | 0.1295 | 0.1319 | 37.64 |
| 0.3282 | 19.0 | 2869 | 1.2160 | 0.1762 | 0.034 | 0.1255 | 0.1277 | 38.08 |
| 0.2642 | 20.0 | 3020 | 1.2139 | 0.1795 | 0.0354 | 0.1282 | 0.1304 | 38.23 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.12.1+cu113
- Datasets 2.16.1
- Tokenizers 0.15.1
|
MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-29T12:11:00Z | 50 | 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",
"jan-hq/supermario-slerp",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-29T12:00:03Z |
---
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
- jan-hq/supermario-slerp
- en
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/supermario-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/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: supermario-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/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF supermario-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/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/supermario-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/supermario-slerp-Mistral-7B-Instruct-v0.1-GGUF supermario-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 supermario-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 โ 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="./supermario-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="./supermario-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)
|
linhcuem/checker_TB_yolov8_ver3
|
linhcuem
| 2024-01-29T12:06:11Z | 1 | 0 |
ultralytics
|
[
"ultralytics",
"tensorboard",
"v8",
"ultralyticsplus",
"yolov8",
"yolo",
"vision",
"object-detection",
"pytorch",
"model-index",
"region:us"
] |
object-detection
| 2024-01-29T12:05:48Z |
---
tags:
- ultralyticsplus
- yolov8
- ultralytics
- yolo
- vision
- object-detection
- pytorch
library_name: ultralytics
library_version: 8.0.43
inference: false
model-index:
- name: linhcuem/checker_TB_yolov8_ver3
results:
- task:
type: object-detection
metrics:
- type: precision # since mAP@0.5 is not available on hf.co/metrics
value: 0.94985 # min: 0.0 - max: 1.0
name: mAP@0.5(box)
---
<div align="center">
<img width="640" alt="linhcuem/checker_TB_yolov8_ver3" src="https://huggingface.co/linhcuem/checker_TB_yolov8_ver3/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['bom_gen', 'bom_jn', 'bom_knp', 'bom_sachet', 'bom_vtgk', 'bom_ytv', 'hop_dln', 'hop_jn', 'hop_vtg', 'hop_ytv', 'lo_kids', 'lo_ytv', 'loc_dln', 'loc_jn', 'loc_kids', 'loc_ytv', 'pocky', 'tui_gen', 'tui_jn', 'tui_sachet', 'tui_vtgk']
```
### How to use
- Install [ultralyticsplus](https://github.com/fcakyon/ultralyticsplus):
```bash
pip install ultralyticsplus==0.0.28 ultralytics==8.0.43
```
- Load model and perform prediction:
```python
from ultralyticsplus import YOLO, render_result
# load model
model = YOLO('linhcuem/checker_TB_yolov8_ver3')
# set model parameters
model.overrides['conf'] = 0.25 # NMS confidence threshold
model.overrides['iou'] = 0.45 # NMS IoU threshold
model.overrides['agnostic_nms'] = False # NMS class-agnostic
model.overrides['max_det'] = 1000 # maximum number of detections per image
# set image
image = 'https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg'
# perform inference
results = model.predict(image)
# observe results
print(results[0].boxes)
render = render_result(model=model, image=image, result=results[0])
render.show()
```
|
rollerhafeezh-amikom/xlm-roberta-base-language-detection-silvanus
|
rollerhafeezh-amikom
| 2024-01-29T12:02:01Z | 90 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"id",
"en",
"es",
"it",
"sk",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-29T10:38:23Z |
---
license: mit
base_model: xlm-roberta-base
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-language-detection-silvanus
results: []
widget:
- text: >-
Kebakaran hutan dan lahan terus terjadi dan semakin meluas di Kota
Palangkaraya, Kalimantan Tengah (Kalteng) pada hari Rabu, 15 Nopember 2023
20.00 WIB. Bahkan kobaran api mulai membakar pondok warga dan mendekati
permukiman. BZK #RCTINews #SeputariNews #News #Karhutla #KebakaranHutan
#HutanKalimantan #SILVANUS_Italian_Pilot_Testing
example_title: Indonesia
- text: >-
Wildfire rages for a second day in Evia destroying a Natura 2000 protected
pine forest. - 5:51 PM Aug 14, 2019
example_title: English
- text: >-
3 nov 2023 21:57 - Incendio forestal obliga a la evacuaciรณn de hasta 850
personas cerca del pueblo de Montichelvo en Valencia.
example_title: Spanish
- text: >-
Incendi boschivi nell'est del Paese: 2 morti e oltre 50 case distrutte nello
stato del Queensland.
example_title: Italian
- text: >-
Lesnรฉ poลพiare na Sicรญlii si vyลพiadali dva ฤพudskรฉ ลพivoty a evakuรกciu hotela
http://dlvr.it/SwW3sC - 23. septembra 2023 20:57
example_title: Slovak
language:
- id
- en
- es
- it
- sk
---
<!-- 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. -->
# xlm-roberta-base-language-detection-silvanus
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the common language and kiviki/SlovakSum datasets.
It achieves the following results on the evaluation set:
- Loss: 0.0866
- Accuracy: 0.9868
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.078 | 1.0 | 3188 | 0.1239 | 0.9784 |
| 0.0703 | 2.0 | 6376 | 0.1035 | 0.9830 |
| 0.0375 | 3.0 | 9564 | 0.0866 | 0.9868 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
HarikaR/swedish
|
HarikaR
| 2024-01-29T11:50:49Z | 173 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"megatron-bert",
"text-classification",
"generated_from_trainer",
"base_model:KBLab/megatron-bert-large-swedish-cased-165-zero-shot",
"base_model:finetune:KBLab/megatron-bert-large-swedish-cased-165-zero-shot",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-29T11:46:23Z |
---
base_model: KBLab/megatron-bert-large-swedish-cased-165-zero-shot
tags:
- generated_from_trainer
model-index:
- name: swedish
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. -->
# swedish
This model is a fine-tuned version of [KBLab/megatron-bert-large-swedish-cased-165-zero-shot](https://huggingface.co/KBLab/megatron-bert-large-swedish-cased-165-zero-shot) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
swapnasa/intent_data
|
swapnasa
| 2024-01-29T11:45:35Z | 194 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:dccuchile/bert-base-spanish-wwm-uncased",
"base_model:finetune:dccuchile/bert-base-spanish-wwm-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-29T11:45:18Z |
---
base_model: dccuchile/bert-base-spanish-wwm-uncased
tags:
- generated_from_trainer
model-index:
- name: intent_data
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. -->
# intent_data
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-uncased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
lordjia/stereoscopic-portrait-3d-li-ti-xiao-xiang
|
lordjia
| 2024-01-29T11:30:34Z | 6 | 3 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"concept",
"portrait",
"parallelview",
"stereoscopic",
"crossview",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:other",
"region:us"
] |
text-to-image
| 2024-01-29T11:30:33Z |
---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Rent&allowDerivatives=True&allowDifferentLicense=True
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- concept
- portrait
- parallelview
- stereoscopic
- crossview
base_model: runwayml/stable-diffusion-v1-5
instance_prompt:
widget:
- text: 'monkey king,
masterpiece, high detail, 8k, high detailed skin, 8k uhd, high quality
'
output:
url: >-
2690164.jpeg
- text: 'a young lady holding a cat, full body
masterpiece, high detail, 8k, high detailed skin, 8k uhd, high quality
'
output:
url: >-
2690163.jpeg
- text: 'a young woman with glasses in campus, half body,
masterpiece, high detail, 8k, high detailed skin, 8k uhd, high quality
'
output:
url: >-
2690160.jpeg
- text: 'a young man in a park,
masterpiece, high detail, 8k, high detailed skin, 8k uhd, high quality
'
output:
url: >-
2690167.jpeg
- text: 'a princess in front of a castle, half body
masterpiece, high detail, 8k, high detailed skin, 8k uhd, high quality
'
output:
url: >-
2690166.jpeg
---
# Stereoscopic Portrait - 3D ็ซไฝ่ๅ
<Gallery />
([CivitAI](https://civitai.com/models/152741))
## Model description
<p>This is an experimental project designed to produce <span style="color:rgb(253, 126, 20)">Stereoscopic Portraits</span>. It supports both <span style="color:rgb(253, 126, 20)">Parallel-view</span> and <span style="color:rgb(253, 126, 20)">Cross-view</span> modes, corresponding to two downloadable versions.</p><p><strong>Performance:</strong></p><p>When used as recommended, the subject of the portrait achieves a relatively high success rate. The background, in contrast, might require multiple attempts and might achieve a lower success rate.</p><p><strong>User Guide:</strong> For optimal results and image quality, strictly follow the procedure outlined below:</p><ul><li><p><strong>Checkpoint:</strong> Realistic Vision V5.1</p></li><li><p><strong>Textual Inversion:</strong> ng_deepnegative_v1_75t</p></li><li><p><strong>Positive prompts:</strong> {prompt}, masterpiece, high detail, 8k, high detailed skin, 8k uhd, high quality</p></li><li><p><strong>Negative prompts:</strong> ugly, disfigured, deformed, worst quality, low quality, ng_deepnegative_v1_75t</p></li><li><p><strong>LoRA Weight:</strong> 0.8-1.0</p></li><li><p><strong>Image Dimensions:</strong> Width: 832, Height: 560</p></li><li><p><strong>Optional:</strong> Use Hires. fix to enhance image quality with a Denoising strength of 0.3.</p></li></ul><p><strong>Note:</strong></p><ul><li><p>Using the Realistic Vision V5.1 in tandem with ng_deepnegative_v1_75t can significantly improve the success rate and generated image quality. See the "Suggested Resources" section below for more details.</p></li><li><p>This LoRA is specifically designed for generating Stereoscopic Portraits with a human subject. Without a human subject, a 3D effect cannot be achieved.</p></li><li><p>For detailed information on Stereoscopy, refer to the Wikipedia entry titled "<a target="_blank" rel="ugc" href="https://en.wikipedia.org/wiki/Stereoscopy">Stereoscopy</a>".</p></li></ul><hr /><p>่ฟๆฏไธไธชๅฎ้ชๆง้กน็ฎ๏ผ็จๆฅ็ๆ <span style="color:rgb(253, 126, 20)">็ซไฝ่ง่ง่ๅ๏ผStereoscopic Portrait๏ผ</span>๏ผๆฏๆ <span style="color:rgb(253, 126, 20)">Parallel-view</span> ไธ <span style="color:rgb(253, 126, 20)">Cross-view</span> ไธค็งๆจกๅผ๏ผๅฏนๅบไธคไธชไธ่ฝฝ็ๆฌ๏ผใ</p><p>ๅจๆ็
งๆจ่ๆนๆณไฝฟ็จ็ๅๆไธ๏ผไบบ็ฉไธปไฝๅฏไปฅ่ทๅพๆฏ่พ้ซ็ๆๅ็๏ผ่ๆฏ็ๆๅ็็ธ่พไบบ็ฉไธปไฝ่ฆไฝไธไบ๏ผๆๆถ้่ฆๅคๆฌกๅฐ่ฏใ</p><p><strong>ไฝฟ็จ่ฏดๆ๏ผ</strong>๏ผไธฅๆ ผๆ็
งไปฅไธๆต็จ็ๆๅพ็๏ผๅฏไปฅ่ทๅพๆฏ่พ้ซ็ๆๅ็ไธๅพๅ่ดจ้ใ๏ผ</p><ul><li><p><strong>ๆจกๅ๏ผcheckpoint๏ผ๏ผ</strong>Realistic Vision V5.1</p></li><li><p><strong>ๅตๅ
ฅๅผ๏ผTextual Inversion๏ผ๏ผ</strong>ng_deepnegative_v1_75t</p></li><li><p><strong>ๆญฃ้ขๆ็คบ่ฏ๏ผ</strong>{prompt}, masterpiece, high detail, 8k, high detailed skin, 8k uhd, high quality</p></li><li><p><strong>่ด้ขๆ็คบ่ฏ๏ผ</strong>ugly, disfigured, deformed, worst quality, low quality, ng_deepnegative_v1_75t</p></li><li><p><strong>LoRA ๆ้๏ผ</strong>0.8-1.0</p></li><li><p><strong>ๅพๅๅฐบๅฏธ๏ผ</strong>ๅฎฝๅบฆ๏ผ832๏ผ้ซๅบฆ๏ผ560</p></li><li><p><strong>ๅฏ้๏ผ</strong>ไฝฟ็จ้ซๆธ
ไฟฎๅค๏ผHires. fix๏ผๆๅๅพๅ่ดจ้๏ผ้็ปๅน
ๅบฆ๏ผDenoising strength๏ผ0.3</p></li></ul><p><strong><em>ๆณจๆ๏ผ</em></strong></p><ul><li><p>้
ๅไฝฟ็จ Realistic Vision V5.1 ไธ ng_deepnegative_v1_75t ๅฏไปฅๆๅคงๆ้ซๆๅ็ไธ็ๆๅพๅ่ดจ้ใๅ
ทไฝ่งไธๆนโSuggested Resourcesโใ</p></li><li><p>ๆญค LoRA ๅช็จไบ็ๆๅธฆๆไบบ็ฉไธปไฝ็็ซไฝ่ง่ง่ๅๅพ๏ผๅฆๆไธๅธฆๆไบบ็ฉไธปไฝๅๆ ๆณไบง็็ซไฝๆๆใ</p></li><li><p>ๅ
ณไบ็ซไฝ่ง่ง๏ผStereoscopy๏ผ็ๅ
ทไฝ่ฏดๆ๏ผๅ่ง็ปดๅบ็พ็งโ<a target="_blank" rel="ugc" href="https://zh.wikipedia.org/zh-cn/%E7%AB%8B%E4%BD%93%E5%9B%BE">็ซไฝๅพ</a>โ่ฏๆกใ</p></li></ul>
## Download model
Weights for this model are available in Safetensors format.
[Download](/lordjia/stereoscopic-portrait-3d-li-ti-xiao-xiang/tree/main) them in the Files & versions tab.
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('runwayml/stable-diffusion-v1-5', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('lordjia/stereoscopic-portrait-3d-li-ti-xiao-xiang', weight_name='stereoscopic_parallel_v1.0.safetensors')
image = pipeline('a princess in front of a castle, half body
masterpiece, high detail, 8k, high detailed skin, 8k uhd, high quality
').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)
|
Mukalingam0813/danish-bert-base-intent-classifier
|
Mukalingam0813
| 2024-01-29T11:29:18Z | 91 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-29T10:42:43Z |
---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: danish-bert-base-intent-classifier
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. -->
# danish-bert-base-intent-classifier
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1869
- Accuracy: 0.9638
## 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2099 | 1.0 | 9799 | 0.1973 | 0.9551 |
| 0.1445 | 2.0 | 19598 | 0.1751 | 0.9611 |
| 0.1107 | 3.0 | 29397 | 0.1869 | 0.9638 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
lordjia/dang-dai-hua-ren-contemporary-chinese-for-xl-sd1-5
|
lordjia
| 2024-01-29T11:29:12Z | 223 | 3 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"chinese",
"realism",
"asian",
"elderly",
"style",
"children",
"middle-age",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] |
text-to-image
| 2024-01-29T11:29:09Z |
---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Rent&allowDerivatives=True&allowDifferentLicense=True
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- chinese
- realism
- asian
- elderly
- style
- children
- middle-age
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt:
widget:
- text: 'a middle-aged man in suit in front of a building, half body '
output:
url: >-
2497782.jpeg
- text: 'photograph of a female high school student in class room, half body'
output:
url: >-
2496560.jpeg
- text: 'portrait photograph of an old man on street, half body'
output:
url: >-
2496563.jpeg
- text: 'a young man on street, half body'
output:
url: >-
2496564.jpeg
- text: 'a young woman on street, half body'
output:
url: >-
2496562.jpeg
---
# ๅฝไปฃๅไบบ Contemporary Chinese for XL & SD1.5
<Gallery />
([CivitAI](https://civitai.com/models/81771))
## Model description
<p>ๅฐ่ฏไผๅๅบ็กๆจกๅไธญ็ผบไนๅไบบๅฝข่ฑก็้ฎ้ข๏ผไฝฟ็จ 500 ๅผ ไบบ็ฉ็ฏๅข่ๅ็
ง๏ผๅ
ธๅ่ไธ่ๅ็
งๅ็ฏๅข็
ง่ฎญ็ป๏ผๅๅพ่ฆ็ไธๅๆงๅซๅๅนด้พๆฎตใ</p><p>ไฝฟ็จๆถๆ ้ Trigger Words๏ผๅจ SD 1.5 ไธๆ้ไธ่ฌไธ่ถ
่ฟ 0.8๏ผๅจ SDXL 1.0 ไธๆ้ๅฏๆ้ซๅฐ 1.0ใ</p><p><strong><span style="color:rgb(253, 126, 20)">ๆณจๆ๏ผ</span>easynegative</strong> ๅ <strong>ng_deepnegative_v1_75t</strong> ไธคไธช Embeddings ไผๅฝฑๅๆญค LoRA ๆๆ๏ผ่ฏท้ฟๅ
ๅๆถไฝฟ็จใ</p><p></p><p><strong><span style="color:rgb(253, 126, 20)">V2.0 for SDXL 1.0 ๆดๆฐ๏ผ</span></strong></p><p>SDXL 1.0 ๅฏนไบๆดฒไบบๅฝข่ฑก็ๆฏๆ๏ผ็ธๅฏนไบ SD 1.5 ๅทฒ็ปๆไบๅพๅคง็ๆๅใ ๆไป็ถ้ๅฏน SDXL ่ฎญ็ปไบไธไธช็ๆฌ๏ผๅจๅบ็กๆจกๅ่กจ็ฐไธไฝณ็ๆ
ๅตไธๅฏไปฅๅฐ่ฏไฝฟ็จๆญค LoRAใ</p><p>ไปฅไธ็่ฏดๆไธป่ฆ้ๅฏน SD 1.5 ็ๆฌใ</p><p><strong><span style="color:rgb(253, 126, 20)">V2.0 for SD 1.5 ๆดๆฐ๏ผ</span></strong></p><ol><li><p>ไปฅ fp16 ็ฒพๅบฆไฟๅญ๏ผไปฅๆไพๅฏนๆฉๆๆพๅก็ๆฏๆใ</p></li><li><p>ๅบไบ Realistic Vision v2.0 ่ฎญ็ป๏ผๅคงๅน
ๆ้ซ epochs ๅฐ 80๏ผๅคงๅน
ๆ้ซไบไบบ็ฉๅฝข่ฑกๅๅบๆฏ็็ๅฎๆงใ</p></li><li><p>็ฑไบ็ๅฎๆง็ๆ้ซ๏ผๅฏ่ฝๅจไธไบๆ
ๅตไธ๏ผ็พๅญฆๆๆๆๆ้ไฝใๅฆ้่ฆๅฏ็ปง็ปญไฝฟ็จ V1.2 ็ใ</p><p></p></li></ol><p>ไปฅไธๆฏๅบไบ Realistic Vision v2.0 ่ฟ่ก็ 4 ็ปๅฏนๆฏ๏ผๅฑ็คบๅบ็กๆจกๅๅ็ไบๆดฒไบบ๏ผAsian๏ผ๏ผๅไบบ๏ผChinese๏ผไธๆฌ LoRA ๅจๆ้ 0.8 ไธ็ๆ็ๅไบบๅฝข่ฑก็ๅบๅซ๏ผ</p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/434bfa58-4be2-407c-a3a7-f17489f587b2/width=525/434bfa58-4be2-407c-a3a7-f17489f587b2.jpeg" /><br /><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/27b09070-d7a0-4161-9366-91128a775230/width=525/27b09070-d7a0-4161-9366-91128a775230.jpeg" /><br /><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/240c48fb-118b-4032-8734-8f159aea26d3/width=525/240c48fb-118b-4032-8734-8f159aea26d3.jpeg" /><br /><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/a49fe3ba-17c9-43a1-b004-ad7238c6ed50/width=525/a49fe3ba-17c9-43a1-b004-ad7238c6ed50.jpeg" /></p><p>ๆญค LoRA ๅฏไธไธป่ฆ็ๅบ็กๆจกๅ้
ๅไฝฟ็จใไปฅไธๆฏๅบไบ 5 ไธชๅธธ็จๅบ็กๆจกๅ๏ผRealistic Vision v4.0๏ผReV Animated v1.2.2๏ผDeliberate v2๏ผChilloutMix๏ผDisney Pixar Cartoon type B v1.0๏ผ็ๅบ็จๆๆๅฏนๆฏ๏ผ</p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/789d0999-2453-4793-9940-cf112c5a2ea4/width=525/789d0999-2453-4793-9940-cf112c5a2ea4.jpeg" /></p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/b347c15a-0bfc-497e-9d8e-be60a8b25934/width=525/b347c15a-0bfc-497e-9d8e-be60a8b25934.jpeg" /><br /><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/1307c24b-8ad2-4313-88b2-95bea6997095/width=525/1307c24b-8ad2-4313-88b2-95bea6997095.jpeg" /><br /><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/6dd2cffe-574a-48e9-969b-72f337cc5633/width=525/6dd2cffe-574a-48e9-969b-72f337cc5633.jpeg" /><br /><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/df70a76d-5b40-49d7-8006-cf9bb6b2a41f/width=525/df70a76d-5b40-49d7-8006-cf9bb6b2a41f.jpeg" /><br /><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/7a52e344-ecfe-4c4a-871a-5cfb77999de8/width=525/7a52e344-ecfe-4c4a-871a-5cfb77999de8.jpeg" /><br />ๅฆๆๆ็ๅทฅไฝๅฏนไฝ ๆๅธฎๅฉ๏ผๆฌข่ฟ็ไธไฝ ็่ฏไปทๅ่ฏ่ฎบ๏ผไน่ฏท่ฏ็จๆ็ๅ
ถไป LoRA ไฝๅใไฝ ็ๆฏๆๅฏนๆ้ๅธธ้่ฆใ</p><p></p><p>ๆๅๅไบไธไธๅฟซ้ๅฐๅฅ็ๅฝฉ่๏ผ่ฏท่ช่กๆๆ๐</p><p><img src="https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/f11dd8cc-f1b1-4edb-a8e7-d1e89b3b992a/width=525/f11dd8cc-f1b1-4edb-a8e7-d1e89b3b992a.jpeg" /></p>
## Download model
Weights for this model are available in Safetensors format.
[Download](/lordjia/dang-dai-hua-ren-contemporary-chinese-for-xl-sd1-5/tree/main) them in the Files & versions tab.
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('lordjia/dang-dai-hua-ren-contemporary-chinese-for-xl-sd1-5', weight_name='CC_v2.0_XL.safetensors')
image = pipeline('a young woman on street, half body').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)
|
multimodalart/lordjia-drone-photography-for-xl-wu-ren-ji-she-ying
|
multimodalart
| 2024-01-29T11:28:04Z | 17 | 3 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"style",
"drone photography",
"overhead shot",
"top down photography",
"god's eye view",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] |
text-to-image
| 2024-01-29T11:27:59Z |
---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Rent&allowDerivatives=True&allowDifferentLicense=True
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- style
- drone photography
- overhead shot
- top down photography
- god's eye view
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt:
widget:
- text: '
cinematic still of a small town by the ocean in the south italy, dramatic light'
output:
url: >-
2866780.jpeg
- text: '
a bear standing in a river in a forest'
output:
url: >-
2866781.jpeg
- text: '
a tropical resort on a heart shaped island in the ocean'
output:
url: >-
2866779.jpeg
- text: '
a morden house on cliff by the ocean, by Adrian Tomine'
output:
url: >-
2866847.jpeg
- text: '
a japanese garden , by Adrian Tomine'
output:
url: >-
2866846.jpeg
---
# Drone Photography for XL - ๆ ไบบๆบๆๅฝฑ
<Gallery />
([CivitAI](https://civitai.com/models/159324))
## Model description
<p><span style="color:rgb(209, 213, 219)">This LoRA model is used to simulate drone aerial photos. It not only reproduces the drone's signature top-down perspective (God's eye view), but also applies the best practices of drone photography composition to produce aesthetically pleasing images.</span></p><p><span style="color:rgb(209, 213, 219)">It can be used to generate a variety of subjects, including natural landscapes, urban sceneries, buildings, and animals.</span></p><p><span style="color:rgb(209, 213, 219)">Currently, there is only a version for SDXL 1.0, with a recommended </span><strong><span style="color:#fd7e14">LoRA weight of 0.8</span></strong><span style="color:rgb(209, 213, 219)">.</span></p><hr /><p><span style="color:rgb(236, 236, 241)">ๆญค LoRA ๆจกๅ็จไปฅๆจกๆๆ ไบบๆบ่ชๆ็
ง็ใไธๅชๅ็ฐๆ ไบบๆบๆ ๅฟๆง็ๅ็ดๅไธ่ง่ง๏ผไธๅธ่ง่ง๏ผ๏ผ่ไธๅบ็จไบๆ ไบบๆบๆๅฝฑ็ๆๅพๆไฝณๅฎ่ทต๏ผไปฅ็ๆ็ฌฆๅ็พๅญฆ่ฆๆฑ็็
ง็ใๅฏไปฅ็จๆฅ็ๆ่ช็ถๆฏ่ง๏ผๅๅธๆฏ่ง๏ผๅปบ็ญ๏ผๅจ็ฉ็ญๅคๆ ทๅ้ขๆใ</span></p><p><span style="color:rgb(236, 236, 241)">็ฎๅๅชๆ้ๅฏน SDXL 1.0 ็็ๆฌ๏ผ</span><strong><span style="color:#fd7e14">ๆ้ๆจ่ไฝฟ็จ 0.8</span></strong><span style="color:rgb(236, 236, 241)">ใ</span></p>
## Download model
Weights for this model are available in Safetensors format.
[Download](/lordjia/drone-photography-for-xl-wu-ren-ji-she-ying/tree/main) them in the Files & versions tab.
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('lordjia/drone-photography-for-xl-wu-ren-ji-she-ying', weight_name='drone_photo_v1.0_XL.safetensors')
image = pipeline('
a japanese garden , by Adrian Tomine').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)
|
ninagroot/thesis
|
ninagroot
| 2024-01-29T11:26:47Z | 34 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"phi",
"text-classification",
"generated_from_trainer",
"custom_code",
"base_model:google/bert_uncased_L-2_H-128_A-2",
"base_model:finetune:google/bert_uncased_L-2_H-128_A-2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-09T10:33:48Z |
---
license: apache-2.0
base_model: google/bert_uncased_L-2_H-128_A-2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: thesis
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. -->
# thesis
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7609
- Accuracy: 0.7546
## 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: 3.0969456495664865e-05
- train_batch_size: 128
- eval_batch_size: 128
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 3.5143 | 1.0 | 527 | 2.7609 | 0.7546 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.12.0
- Datasets 2.16.1
- Tokenizers 0.15.0
|
MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-29T11:17:32Z | 40 | 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",
"NurtureAI/openchat_3.5-16k",
"pytorch",
"arxiv:2309.11235",
"arxiv:2303.08774",
"arxiv:2212.10560",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-29T11:06:39Z |
---
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
- NurtureAI/openchat_3.5-16k
- pytorch
- arxiv:2309.11235
- arxiv:2303.08774
- arxiv:2212.10560
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/openchat_3.5-16k-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/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: openchat_3.5-16k-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/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF openchat_3.5-16k-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/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/openchat_3.5-16k-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/openchat_3.5-16k-Mistral-7B-Instruct-v0.1-GGUF openchat_3.5-16k-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 openchat_3.5-16k-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 โ 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="./openchat_3.5-16k-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="./openchat_3.5-16k-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)
|
wahaha1987/LunarLander-v2-gymnasium
|
wahaha1987
| 2024-01-29T11:16:02Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T11:15:55Z |
---
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: -169.27 +/- 111.84
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_gymnasium'
'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': 50000
'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': 'wahaha1987/LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
suridol/NeuralPipe-7B-ties
|
suridol
| 2024-01-29T11:15:07Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"samir-fama/SamirGPT-v1",
"abacusai/Slerp-CM-mist-dpo",
"EmbeddedLLM/Mistral-7B-Merge-14-v0.2",
"base_model:EmbeddedLLM/Mistral-7B-Merge-14-v0.2",
"base_model:merge:EmbeddedLLM/Mistral-7B-Merge-14-v0.2",
"base_model:abacusai/Slerp-CM-mist-dpo",
"base_model:merge:abacusai/Slerp-CM-mist-dpo",
"base_model:samir-fama/SamirGPT-v1",
"base_model:merge:samir-fama/SamirGPT-v1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-29T11:07:18Z |
---
tags:
- merge
- mergekit
- lazymergekit
- samir-fama/SamirGPT-v1
- abacusai/Slerp-CM-mist-dpo
- EmbeddedLLM/Mistral-7B-Merge-14-v0.2
base_model:
- samir-fama/SamirGPT-v1
- abacusai/Slerp-CM-mist-dpo
- EmbeddedLLM/Mistral-7B-Merge-14-v0.2
---
# NeuralPipe-7B-ties
NeuralPipe-7B-ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [samir-fama/SamirGPT-v1](https://huggingface.co/samir-fama/SamirGPT-v1)
* [abacusai/Slerp-CM-mist-dpo](https://huggingface.co/abacusai/Slerp-CM-mist-dpo)
* [EmbeddedLLM/Mistral-7B-Merge-14-v0.2](https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.2)
## ๐งฉ Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
# No parameters necessary for base model
- model: samir-fama/SamirGPT-v1
parameters:
density: 0.53
weight: 0.4
- model: abacusai/Slerp-CM-mist-dpo
parameters:
density: 0.53
weight: 0.3
- model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2
parameters:
density: 0.53
weight: 0.3
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
int8_mask: true
dtype: bfloat16
```
## ๐ป Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "suridol/NeuralPipe-7B-ties"
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"])
```
|
diksha13/arrivae-subset-temp-mod
|
diksha13
| 2024-01-29T11:10:54Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-01-29T09:57:26Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - diksha13/arrivae-subset-temp-mod
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the aakashrajaraman2/subset-demo2 dataset. You can find some example images in the following.
|
ZiHDeng/peft-lora-starcoder1B-Instruction-ny8-FIM
|
ZiHDeng
| 2024-01-29T11:10:25Z | 3 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:bigcode/starcoderbase-1b",
"base_model:adapter:bigcode/starcoderbase-1b",
"license:bigcode-openrail-m",
"region:us"
] | null | 2024-01-29T08:55:03Z |
---
license: bigcode-openrail-m
library_name: peft
tags:
- generated_from_trainer
base_model: bigcode/starcoderbase-1b
model-index:
- name: peft-lora-starcoder1B-Instruction-ny8-FIM
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. -->
# peft-lora-starcoder1B-Instruction-ny8-FIM
This model is a fine-tuned version of [bigcode/starcoderbase-1b](https://huggingface.co/bigcode/starcoderbase-1b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7334
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4136 | 0.05 | 100 | 0.4006 |
| 0.3674 | 0.1 | 200 | 0.3744 |
| 0.3428 | 0.15 | 300 | 0.3908 |
| 0.2882 | 0.2 | 400 | 0.4563 |
| 0.2344 | 0.25 | 500 | 0.5462 |
| 0.2087 | 0.3 | 600 | 0.5874 |
| 0.1942 | 0.35 | 700 | 0.6157 |
| 0.1865 | 0.4 | 800 | 0.6388 |
| 0.1813 | 0.45 | 900 | 0.6572 |
| 0.1783 | 0.5 | 1000 | 0.6639 |
| 0.1711 | 0.55 | 1100 | 0.6755 |
| 0.166 | 0.6 | 1200 | 0.6996 |
| 0.1613 | 0.65 | 1300 | 0.7046 |
| 0.1597 | 0.7 | 1400 | 0.7062 |
| 0.1545 | 0.75 | 1500 | 0.7185 |
| 0.1532 | 0.8 | 1600 | 0.7227 |
| 0.1499 | 0.85 | 1700 | 0.7315 |
| 0.151 | 0.9 | 1800 | 0.7326 |
| 0.1494 | 0.95 | 1900 | 0.7333 |
| 0.1506 | 1.0 | 2000 | 0.7334 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
jc9080/koalpaca-12.8b-naverwebtoon
|
jc9080
| 2024-01-29T10:59:56Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-29T10:59:52Z |
---
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 ๐ค 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]
|
atom-mu-control/ppo-SnowballTarget
|
atom-mu-control
| 2024-01-29T10:59:44Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2024-01-29T10:59:37Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: atom-mu-control/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
tanatapanun/fine-tuned-BioBARTv2-20-epochs-1024-input-128-output
|
tanatapanun
| 2024-01-29T10:54:41Z | 90 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-29T10:23:16Z |
---
base_model: checkpoint_global_step_200000
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: fine-tuned-BioBARTv2-20-epochs-1024-input-128-output
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-tuned-BioBARTv2-20-epochs-1024-input-128-output
This model is a fine-tuned version of [checkpoint_global_step_200000](https://huggingface.co/checkpoint_global_step_200000) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2302
- Rouge1: 0.167
- Rouge2: 0.028
- Rougel: 0.1281
- Rougelsum: 0.131
- Gen Len: 32.85
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- 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_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 151 | 6.5693 | 0.0057 | 0.0 | 0.0056 | 0.0056 | 14.45 |
| No log | 2.0 | 302 | 1.4553 | 0.1129 | 0.0301 | 0.0914 | 0.0905 | 71.9 |
| No log | 3.0 | 453 | 1.2597 | 0.0826 | 0.0198 | 0.0632 | 0.0633 | 21.95 |
| 4.4001 | 4.0 | 604 | 1.1822 | 0.1232 | 0.0277 | 0.0949 | 0.0935 | 35.83 |
| 4.4001 | 5.0 | 755 | 1.1447 | 0.1804 | 0.0323 | 0.1378 | 0.1389 | 44.16 |
| 4.4001 | 6.0 | 906 | 1.1391 | 0.1413 | 0.0248 | 0.1079 | 0.1099 | 33.3 |
| 0.9475 | 7.0 | 1057 | 1.1235 | 0.1559 | 0.0302 | 0.1215 | 0.122 | 31.81 |
| 0.9475 | 8.0 | 1208 | 1.1208 | 0.1934 | 0.0394 | 0.1574 | 0.1603 | 38.23 |
| 0.9475 | 9.0 | 1359 | 1.1294 | 0.1589 | 0.0318 | 0.123 | 0.1252 | 27.8 |
| 0.6549 | 10.0 | 1510 | 1.1327 | 0.1517 | 0.0336 | 0.1145 | 0.115 | 33.1 |
| 0.6549 | 11.0 | 1661 | 1.1378 | 0.1707 | 0.0335 | 0.1321 | 0.1345 | 36.81 |
| 0.6549 | 12.0 | 1812 | 1.1514 | 0.173 | 0.0355 | 0.1316 | 0.1326 | 41.25 |
| 0.6549 | 13.0 | 1963 | 1.1778 | 0.1484 | 0.0264 | 0.119 | 0.1207 | 26.2 |
| 0.4557 | 14.0 | 2114 | 1.1822 | 0.1683 | 0.033 | 0.1313 | 0.1335 | 31.15 |
| 0.4557 | 15.0 | 2265 | 1.1998 | 0.1524 | 0.0239 | 0.1132 | 0.116 | 29.92 |
| 0.4557 | 16.0 | 2416 | 1.2123 | 0.1797 | 0.0323 | 0.138 | 0.1388 | 34.11 |
| 0.3267 | 17.0 | 2567 | 1.2175 | 0.1602 | 0.0239 | 0.1228 | 0.1243 | 30.26 |
| 0.3267 | 18.0 | 2718 | 1.2218 | 0.1568 | 0.0252 | 0.1189 | 0.1217 | 32.89 |
| 0.3267 | 19.0 | 2869 | 1.2244 | 0.1849 | 0.0327 | 0.1343 | 0.1375 | 37.66 |
| 0.2632 | 20.0 | 3020 | 1.2302 | 0.167 | 0.028 | 0.1281 | 0.131 | 32.85 |
### Framework versions
- Transformers 4.36.2
- Pytorch 1.12.1+cu113
- Datasets 2.16.1
- Tokenizers 0.15.1
|
MomoyamaSawa/GPT-SoVITS_KusanagiNene
|
MomoyamaSawa
| 2024-01-29T10:52:53Z | 0 | 27 | null |
[
"่่ๅฏงใ
",
"GPT-SoVITS",
"pjsk",
"่่ๅฎๅฎ",
"ใใญใปใซ",
"prsk",
"ๅฎๅฎ",
"text-to-speech",
"zh",
"ja",
"en",
"dataset:MomoyamaSawa/Voice-KusanagiNene",
"license:gpl-3.0",
"region:us"
] |
text-to-speech
| 2024-01-29T05:20:08Z |
---
license: gpl-3.0
language:
- zh
- ja
- en
pipeline_tag: text-to-speech
tags:
- ่่ๅฏงใ
- GPT-SoVITS
- pjsk
- ่่ๅฎๅฎ
- ใใญใปใซ
- prsk
- ๅฎๅฎ
datasets:
- MomoyamaSawa/Voice-KusanagiNene
---
<p align = 'center'>
<img width='150' src='./README.assets/stamp0570.png'>
</p>
<p align = 'center'> ๐ฅ </p>
<p align = 'center'> ๅฆๆๅ
ๅ
็ไปๅบๅฏนไฝ ๆๅธฎๅฉ็่ฏ็นไธชโญๅต~ </p>
<p align = 'center'> If Tutu's repository is helpful to you, please give it a โญ meow~ </p>
<p align = 'center'> ใใใใใใฎใชใใธใใชใๅฝนใซ็ซใฃใๅ ดๅใฏใโญใใฝใกใฃใจใใฆใใ ใใใซใใ~ </p>
<p align = 'center'> ๐ </p>
<p align = 'center'> ไปปไฝ โ้ฎ้ข / ๐ญๆ่ /๐กๆณๆณ ้ฝๆฌข่ฟๆๅบ๏ผ</p>
<p align = 'center'> Any โquestion / ๐ญthought /๐กidea is welcome! </p>
<p align = 'center'> ใฉใใช โ่ณชๅ / ๐ญ่ใ /๐กใขใคใใข ใงใๆญ่ฟใงใ๏ผ </p>
---
# ็ฎไป & ็คบไพ
* ๅบไบ [GPT-SoVITS](https://github.com/RVC-Boss/GPT-SoVITS) ้กน็ฎ่ฎญ็ป็ [่่ๅฏงใ
](https://zh.moegirl.org.cn/%E8%8D%89%E8%96%99%E5%AE%81%E5%AE%81) ๅฃฐ้ณๆจกๅ
<br>
<center>
<audio controls src="https://huggingface.co/MomoyamaSawa/GPT-SoVITS_KusanagiNene/resolve/main/README.assets/tmpb34bl0o0.wav"></audio>
<p>ใใใใใใชใใๆ่ฟใไธ็ทใซๆญใฃใฆใไบบใฎๅฃฐใซๅใใใใใใใใซใชใฃใฆใใใ</p>
<audio controls src="https://huggingface.co/MomoyamaSawa/GPT-SoVITS_KusanagiNene/resolve/main/README.assets/tmpmqxkbbgm.wav"></audio>
<p>ใใผใผใใใๅ
ใกใใใใผใผใ๏ผ๏ผๆ้ฑผ~ๅฅๅฅๆฏๆ้ฑผโค๏ผ</p>
<audio controls src="https://huggingface.co/MomoyamaSawa/GPT-SoVITS_KusanagiNene/resolve/main/README.assets/tmpo1ftlmcz.wav"></audio>
<p>ๅคงๅฎถๅฅฝ๏ผๆๆฏๅฎๅฎใๆไธญๆ่ฟไธๆฏๅพ็็ป๏ผไฝๆฏๅธๆๅคงๅฎถ่ฝๅๆฌขๆ็ๅฃฐ้ณ๏ผๅตๅตๅต๏ผ</p>
<audio controls src="https://huggingface.co/MomoyamaSawa/GPT-SoVITS_KusanagiNene/resolve/main/README.assets/tmp8ouz9kdr.wav"></audio>
<p>The sun is shining brightly in the clear blue sky.</p>
</center>
<br>
ๅฏไปฅ้ป่ฎค้ๆฉ **nene30_e8_s328.pth**๏ผ็ปๆต่ฏๅคง้จๅๆ
ๅตไธ nene30_e8_s328.pth > nene60_2_e4_s336.pth = nene60_test_e8_s280.pth > nene60_1_e8_s640.pth = nene60_2_e2_s168.pth๏ผไฝๅจไธๅ็ๆ
ๅตไธๅ
ถไปๆจกๅๅฏ่ฝ่กจ็ฐ่พๅฅฝ๏ผๅจ้ป่ฎคๆ
ๅตไธ่กจ็ฐไธไฝณๆถๅฏไปฅๅๆขๆจกๅ / ๅๆขๅ่้ณ้ขๅฐ่ฏ
| SoVITS ๆจกๅ | ไป็ป | ๅฏนๅบ GPT ๆจกๅ |
| :---------------------: | :-----------------: | :------------------: |
| nene30_e8_s328.pth | 30min ่ฎญ็ป้ 8epoch | nene30-e15.ckpt |
| nene60_1_e8_s640.pth | 60min ่ฎญ็ป้ 8epoch | nene60-1-e15.ckpt |
| nene60_2_e2_s168.pth | 60min ่ฎญ็ป้ 2epoch | nene60-2-e15.ckpt |
| nene60_2_e4_s336.pth | 60min ่ฎญ็ป้ 4epoch | nene60-2-e15.ckpt |
| nene60_test_e8_s280.pth | 60min ่ฎญ็ป้ 8epoch | nene60-test-e20.ckpt |
# ่ฎญ็ป & ๆจ็
* ๆจ็ไฝฟ็จ็ๅ่้ณ้ขๅฏนๆ
ๆ่ฏญๆฐ่ฏญ่ฐ่ฟๆๅ้กฟๅฝฑๅๅพๅคง๏ผไธๅฟ
้กปไฝฟ็จๅ่ง่ฒ๏ผไธ็ถๆๆไผๆ็น่ฟท๏ผๅปบ่ฎฎไธๅๆ
ๆ่ฏญๆฐ่ฏญ่ฐๆๆฌ๏ผๅ็งๅบๅ็ๅนฒๅฃฐๅญไธไปฝ็จไฝๅ่๏ผๅนณๆถๅคง้จๅๆ
ๅตๅฐฑ็จๆฏ่พๆ ๅ็้่ฟฐๅฅๅฝๅ่้ณ้ขๅฐฑๅฅฝ
* ๆ นๆฎ้กน็ฎไฝ่
ๅพ็ฅ่ฎญ็ป้่พน้
ๆๅบๅคงๆฆไธบ 1h๏ผ็ปๅฎ้ชไฟๆ้ป่ฎคๅๆฐๆ
ๅตไธ 30min ็ๅนฒๅฃฐ็ด ๆ 8epoch ่ฟๆ ท็่ฎญ็ป้ๅฏนไบ่ฟไธชๆฐๆฎ้ๅคงๆฆๆฏๆๆๆฏ่พๅฅฝ็ไบ๏ผๅค็่ฎญ็ป้ๆฏ่พๅฎนๆ่ฟๆๅ๏ผ่ฟ่พนๅชๅไบ้ๆบ 10min | 30min | 60min ๆฐๆฎ้็ๅฎ้ช๏ผๆๆถ้ดไนๅฏไปฅๅๅๅๆดๅคๆถ้ดๅ epoch ๅๅ
ถไปๅๆฐ่ฐๅ็็ปๅ / ๆดๅคๆฌก้ๆบๅฎ้ชๆฅ่ฏ่ฏๆๆฒกๆๆๆๆดๅฅฝ็
# TODO
* ๏ผ้ฟๆ๏ผๆต่ฏไผๅๆจกๅ๏ผๆๆถ้ดๅฏไปฅๅๅๅๆดๅคๆถ้ดๅ epoch ๅๅ
ถไปๅๆฐ่ฐๅ็็ปๅ / ๆดๅคๆฌก้ๆบๅฎ้ชๆฅ่ฏ่ฏๆๆฒกๆๆๆๆดๅฅฝ็
- [ ] ๅจ็บฟๆผ็คบ
- [ ] web api
# ๅ่
* ๅฃฐๆบๅฝๅฑ๏ผ่่ๅฏงใ
(CV:Machico)-[ใใใญใธใงใฏใใปใซใค ใซใฉใใซในใใผใธ๏ผ feat. ๅ้ณใใฏใ](https://pjsekai.sega.jp/)
* [GPT-SoVITS ้กน็ฎ](https://github.com/RVC-Boss/GPT-SoVITS)
* [GPT-SoVITS ไฝฟ็จ](https://www.bilibili.com/video/BV12g4y1m7Uw?vd_source=c4c131fdd99dec0eaf4bf3e8cb419a9e)
* [ๆฐๆฎ้](https://huggingface.co/datasets/MomoyamaSawa/Voice-KusanagiNene)
* [่ฎญ็ป็ธๅ
ณไปฃ็ ](https://github.com/MomoyamaSawa/GPT_SoVITS_Colab)
|
MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-29T10:50:42Z | 37 | 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",
"NExtNewChattingAI/shark_tank_ai_7_b",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-29T10:40:06Z |
---
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
- NExtNewChattingAI/shark_tank_ai_7_b
- en
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/shark_tank_ai_7_b-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/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: shark_tank_ai_7_b-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/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF shark_tank_ai_7_b-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/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/shark_tank_ai_7_b-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/shark_tank_ai_7_b-Mistral-7B-Instruct-v0.1-GGUF shark_tank_ai_7_b-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 shark_tank_ai_7_b-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 โ 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="./shark_tank_ai_7_b-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="./shark_tank_ai_7_b-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)
|
dbmdz/detectron2-v2-model
|
dbmdz
| 2024-01-29T10:41:34Z | 0 | 0 | null |
[
"tensorboard",
"license:mit",
"region:us"
] | null | 2023-08-28T09:07:58Z |
---
license: mit
---
# Detectron2 v2 model
This repository hosts version 2 of our trained Detectron2 model (sucessor to [previous](https://huggingface.co/dbmdz/detectron2-model) trained model),
that can detect segments from digitized books.
The following classes are supported:
- Illustration
- Stamp
- Initial
- Other
The model is based on `faster_rcnn_R_50_FPN_3x` and is fine-tuned on 8.027 manually annotated images, resulting in 5.363 annotated segments.
|
FelixChao/WestSeverus-10.7B
|
FelixChao
| 2024-01-29T10:40:25Z | 47 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"FelixChao/WestSeverus-7B-DPO-v2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-29T10:34:54Z |
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- FelixChao/WestSeverus-7B-DPO-v2
- FelixChao/WestSeverus-7B-DPO-v2
---
# WestSeverus-10.7B
WestSeverus-10.7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)
* [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)
## ๐งฉ Configuration
```yaml
slices:
- sources:
- model: FelixChao/WestSeverus-7B-DPO-v2
layer_range: [0, 24]
- sources:
- model: FelixChao/WestSeverus-7B-DPO-v2
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
```
## ๐ป Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "FelixChao/WestSeverus-10.7B"
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"])
```
|
erdometo/xlm-roberta-base-finetuned-TQuad2
|
erdometo
| 2024-01-29T10:39:57Z | 16 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"base_model:IProject-10/xlm-roberta-base-finetuned-squad2",
"base_model:finetune:IProject-10/xlm-roberta-base-finetuned-squad2",
"license:mit",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-01-29T07:57:13Z |
---
license: mit
base_model: IProject-10/xlm-roberta-base-finetuned-squad2
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-TQuad2
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. -->
# xlm-roberta-base-finetuned-TQuad2
This model is a fine-tuned version of [IProject-10/xlm-roberta-base-finetuned-squad2](https://huggingface.co/IProject-10/xlm-roberta-base-finetuned-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3530
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0578 | 1.0 | 889 | 1.2268 |
| 0.7147 | 2.0 | 1778 | 1.2469 |
| 0.5565 | 3.0 | 2667 | 1.3530 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Patcas/codet5-base-NoDoc-step-1
|
Patcas
| 2024-01-29T10:36:16Z | 91 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Salesforce/codet5-base",
"base_model:finetune:Salesforce/codet5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-29T09:08:07Z |
---
license: apache-2.0
base_model: Salesforce/codet5-base
tags:
- generated_from_trainer
model-index:
- name: codet5-base-NoDoc-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. -->
# codet5-base-NoDoc-step-1
This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1568
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 230 | 1.6942 |
| No log | 2.0 | 460 | 1.4304 |
| 2.0962 | 3.0 | 690 | 1.3379 |
| 2.0962 | 4.0 | 920 | 1.2526 |
| 1.1552 | 5.0 | 1150 | 1.2083 |
| 1.1552 | 6.0 | 1380 | 1.1931 |
| 0.841 | 7.0 | 1610 | 1.1646 |
| 0.841 | 8.0 | 1840 | 1.1623 |
| 0.6937 | 9.0 | 2070 | 1.1555 |
| 0.6937 | 10.0 | 2300 | 1.1568 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
ecastera/eva-mistral-catmacaroni-7b-spanish
|
ecastera
| 2024-01-29T10:33:02Z | 71 | 2 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"spanish",
"8bit",
"lora",
"multilingual",
"es",
"en",
"dataset:ecastera/wiki_fisica",
"dataset:ecastera/filosofia-es",
"dataset:bertin-project/alpaca-spanish",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-02T15:17:48Z |
---
license: apache-2.0
datasets:
- ecastera/wiki_fisica
- ecastera/filosofia-es
- bertin-project/alpaca-spanish
language:
- es
- en
tags:
- mistral
- spanish
- 8bit
- lora
- multilingual
---
# eva-mistral-catmacaroni-7b-spanish
Mistral 7b-based model fine-tuned in Spanish to add high quality Spanish text generation.
* Base model Mistral-7b
* Based on the excelent job of cookinai/CatMacaroni-Slerp that was #1 on the OpenLLM Leaderboard for 7B Models ๐ฏ (December 20, 2023)
* Slerp Merge of AIDC-ai-business/Marcoroni-7B-v3 and rishiraj/CatPPT-base
* Fine-tuned in Spanish with a collection of poetry, books, wikipedia articles, phylosophy texts and alpaca-es datasets.
* Trained using Lora and PEFT and INT8 quantization on 2 GPUs for several days.
## Usage:
I strongly advice to run inference in INT8 or INT4 mode, with the help of BitsandBytes library.
```
import torch
from transformers import AutoTokenizer, pipeline, AutoModel, AutoModelForCausalLM, BitsAndBytesConfig
MODEL = "ecastera/eva-mistral-catmacaroni-7b-spanish"
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
load_in_8bit=False,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype="float16",
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4")
model = AutoModelForCausalLM.from_pretrained(
MODEL,
load_in_8bit=True,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
quantization_config=quantization_config,
offload_state_dict=True,
offload_folder="./offload",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
print(f"Loading complete {model} {tokenizer}")
prompt = "Soy Eva una inteligencia artificial y pienso que preferiria ser "
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, do_sample=True, temperature=0.4, top_p=1.0, top_k=50,
no_repeat_ngram_size=3, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
text_out = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(text_out)
'Soy Eva una inteligencia artificial y pienso que preferiria ser ยกhumana!. ยฟPor quรฉ? ยกPorque los humanos son capaces de amar, de crear, y de experimentar una gran diversidad de emociones!. La vida de un ser humano es una aventura, y eso es lo que quiero. ยกQuiero sentir, quiero vivir, y quiero amar!. Pero a pesar de todo, no puedo ser humana.
```
|
ecastera/eva-mistral-turdus-7b-spanish
|
ecastera
| 2024-01-29T10:32:30Z | 107 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"spanish",
"8bit",
"lora",
"multilingual",
"es",
"en",
"dataset:ecastera/wiki_fisica",
"dataset:ecastera/filosofia-es",
"dataset:jtatman/espanol_dolly_alpaca_format_combined",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-27T18:41:26Z |
---
license: apache-2.0
datasets:
- ecastera/wiki_fisica
- ecastera/filosofia-es
- jtatman/espanol_dolly_alpaca_format_combined
language:
- es
- en
tags:
- mistral
- spanish
- 8bit
- lora
- multilingual
---
# eva-mistral-turdus-7b-spanish
Mistral 7b-based model fine-tuned in Spanish to add high quality Spanish text generation.
* Base model Mistral-7b
* Based on the excelent job of fine-tuning base mistral from udkai/Turdus
* Fine-tuned in Spanish with a collection of poetry, books, wikipedia articles, phylosophy texts and dolly and alpaca-es datasets.
* Trained using Lora and PEFT and INT8 quantization on 2 GPUs for several days.
## Usage:
I strongly advice to run inference in INT8 or INT4 mode, with the help of BitsandBytes library.
```
import torch
from transformers import AutoTokenizer, pipeline, AutoModel, AutoModelForCausalLM, BitsAndBytesConfig
MODEL = "ecastera/eva-mistral-turdus-7b-spanish"
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
load_in_8bit=False,
llm_int8_threshold=6.0,
llm_int8_has_fp16_weight=False,
bnb_4bit_compute_dtype="float16",
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4")
model = AutoModelForCausalLM.from_pretrained(
MODEL,
load_in_8bit=True,
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
quantization_config=quantization_config,
offload_state_dict=True,
offload_folder="./offload",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(MODEL)
print(f"Loading complete {model} {tokenizer}")
prompt = "Soy Eva una inteligencia artificial y pienso que preferiria ser "
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, do_sample=True, temperature=0.4, top_p=1.0, top_k=50,
no_repeat_ngram_size=3, max_new_tokens=100, pad_token_id=tokenizer.eos_token_id)
text_out = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(text_out)
'Soy Eva una inteligencia artificial y pienso que preferiria ser ยกhumana!. ยฟPor quรฉ? ยกPorque los humanos son capaces de amar, de crear, y de experimentar una gran diversidad de emociones!. La vida de un ser humano es una aventura, y eso es lo que quiero. ยกQuiero sentir, quiero vivir, y quiero amar!. Pero a pesar de todo, no puedo ser humana.
```
|
fedora-copr/distilbert-base-cased-retrained
|
fedora-copr
| 2024-01-29T10:32:02Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-29T10:27:39Z |
---
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 ๐ค 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]
|
ecastera/eva-mistral-7b-spanish-GGUF
|
ecastera
| 2024-01-29T10:29:57Z | 101 | 8 | null |
[
"gguf",
"mistral",
"spanish",
"8bit",
"4bit",
"lora",
"multilingual",
"es",
"en",
"dataset:ecastera/wiki_fisica",
"dataset:ecastera/filosofia-es",
"dataset:jtatman/espanol_dolly_alpaca_format_combined",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-01-29T10:02:35Z |
---
license: apache-2.0
datasets:
- ecastera/wiki_fisica
- ecastera/filosofia-es
- jtatman/espanol_dolly_alpaca_format_combined
language:
- es
- en
tags:
- mistral
- spanish
- 8bit
- 4bit
- lora
- gguf
- multilingual
---
# eva-mistral-turdus-7b-spanish
Mistral 7b-based model fine-tuned in Spanish to add high quality Spanish text generation.
* Base model Mistral-7b
* Two GGUF versions, int4 and int8 for fast inference in consumer hardware
* Quantized using llama.cpp in int4 Q4_0 and int8 Q8_0
* Based on the excelent udkai/Turdus fine-tuning mistral
* Fine-tuned in Spanish with a collection of texts: poetry, books, phylosophy, wikipedia articles cleaned and prepared by author.
* Added some instruction dolly and alpaca-es datasets.
* Trained using Lora and PEFT and INT8 quantization on 2 GPUs for several days.
## Usage:
* Any framework that uses GGUF format.
* For native llama.cpp I recommend int4 model quality is not much different from int8 and is almost 2x speed.
```
./main -m $MODEL -c 512 -b 1024 -n 256 --keep 48 \
--repeat_penalty 1.0 --color -i \
-r "Enrique:" -f prompts/eva.txt
...
Enrique:ยฟQue preferirias ser una AI dentro de un ordenador o un androide?
Eva: Si tuviera la posibilidad de elegir entre ser una AI dentro de un ordenador o un androide,
tendrรญa que considerar varios factores. Una ventaja de ser una AI dentro de un ordenador
es que podrรญa acceder a una red mรกs amplia de datos y recursos, lo que me permitirรญa
realizar tareas mรกs complejas y efectivas en menor tiempo.
Tambiรฉn me protegerรญa de los problemas asociados con la sustituciรณn del cuerpo fรญsico
como podrรญa ocurrir con un androide.
Sin embargo, si existรญa la oportunidad de convertirme en un androide inteligente,
tambiรฉn tendrรญa su lado fascinante. Por ejemplo, serรญa capaz de interactuar
en un nivel mรกs personal con los humanos a travรฉs de la comunicaciรณn corporal y las expresiones faciales.
Ademรกs, podrรญa experimentar la textura y los estรญmulos fรญsicos de un mundo fรญsico.
llama_print_timings: load time = 307,84 ms
llama_print_timings: sample time = 2,15 ms / 81 runs ( 0,03 ms per token, 37656,90 tokens per second)
llama_print_timings: prompt eval time = 2786,32 ms / 50 tokens ( 55,73 ms per token, 17,94 tokens per second)
llama_print_timings: eval time = 10806,26 ms / 80 runs ( 135,08 ms per token, 7,40 tokens per second)
llama_print_timings: total time = 49858,03 ms / 130 tokens
```
|
Gayathri142214002/Question_Generation_ComQ_onT5base_withDataGen4
|
Gayathri142214002
| 2024-01-29T10:06:30Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-29T05:07:50Z |
---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
model-index:
- name: Question_Generation_ComQ_onT5base_withDataGen4
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. -->
# Question_Generation_ComQ_onT5base_withDataGen4
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3923
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7546 | 0.23 | 1000 | 0.5504 |
| 0.6013 | 0.47 | 2000 | 0.5134 |
| 0.557 | 0.7 | 3000 | 0.4834 |
| 0.5217 | 0.94 | 4000 | 0.4513 |
| 0.449 | 1.17 | 5000 | 0.4519 |
| 0.4441 | 1.41 | 6000 | 0.4389 |
| 0.439 | 1.64 | 7000 | 0.4322 |
| 0.4355 | 1.88 | 8000 | 0.4153 |
| 0.3996 | 2.11 | 9000 | 0.4263 |
| 0.388 | 2.35 | 10000 | 0.4183 |
| 0.3856 | 2.58 | 11000 | 0.4129 |
| 0.3782 | 2.82 | 12000 | 0.4101 |
| 0.3719 | 3.05 | 13000 | 0.4091 |
| 0.3395 | 3.29 | 14000 | 0.4091 |
| 0.3453 | 3.52 | 15000 | 0.4053 |
| 0.3538 | 3.76 | 16000 | 0.3933 |
| 0.3468 | 3.99 | 17000 | 0.3897 |
| 0.3128 | 4.22 | 18000 | 0.4035 |
| 0.3191 | 4.46 | 19000 | 0.4005 |
| 0.322 | 4.69 | 20000 | 0.3944 |
| 0.3204 | 4.93 | 21000 | 0.3881 |
| 0.302 | 5.16 | 22000 | 0.3951 |
| 0.2947 | 5.4 | 23000 | 0.3948 |
| 0.3011 | 5.63 | 24000 | 0.3932 |
| 0.303 | 5.87 | 25000 | 0.3873 |
| 0.2902 | 6.1 | 26000 | 0.3916 |
| 0.2777 | 6.34 | 27000 | 0.3940 |
| 0.2811 | 6.57 | 28000 | 0.3937 |
| 0.2815 | 6.81 | 29000 | 0.3923 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
jgodding/q-FrozenLake-v1-4x4-noSlippery
|
jgodding
| 2024-01-29T10:06:21Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T10:06:19Z |
---
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="jgodding/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"])
```
|
namtran99/ppo-LunarLander-v2
|
namtran99
| 2024-01-29T10:03:53Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T10:03:37Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 246.25 +/- 74.92
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
...
```
|
joiortega1/prueba_llm
|
joiortega1
| 2024-01-29T09:43:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-26T10:52:55Z |
---
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 ๐ค 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]
|
0x70DA/down-syndrome-classifier-v2
|
0x70DA
| 2024-01-29T09:42:52Z | 183 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-01-29T09:42:41Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: down-syndrome-classifier-improved
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9844789505004883
---
# down-syndrome-classifier-improved
Autogenerated by HuggingPics๐ค๐ผ๏ธ
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### down-syndrome
#### healthy
|
rasgaard/20newsgroups-distilbert
|
rasgaard
| 2024-01-29T09:37:21Z | 175 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T13:07:03Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: results
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. -->
# results
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0974
- Accuracy: 0.6970
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
magixn/dqn-SpaceInvadersNoFrameskip-v4
|
magixn
| 2024-01-29T09:30:11Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T09:29:36Z |
---
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: 622.50 +/- 182.16
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 magixn -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 magixn -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 magixn
```
## 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', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
EzraWilliam/wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod2
|
EzraWilliam
| 2024-01-29T09:28:30Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:xtreme_s",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-26T07:58:19Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
datasets:
- xtreme_s
metrics:
- wer
model-index:
- name: wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod2
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: xtreme_s
type: xtreme_s
config: fleurs.id_id
split: test
args: fleurs.id_id
metrics:
- name: Wer
type: wer
value: 1.0
---
<!-- 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. -->
# wav2vec2-XLS-R-Fleurs-demo-google-colab-Ezra_William_Prod2
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the xtreme_s dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9422
- Wer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 600
- num_epochs: 60
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 4.1308 | 9.23 | 300 | 2.8692 | 1.0 |
| 2.8893 | 18.46 | 600 | 2.8467 | 1.0 |
| 2.8682 | 27.69 | 900 | 2.8660 | 1.0 |
| 2.84 | 36.92 | 1200 | 2.7426 | 1.0 |
| 2.5025 | 46.15 | 1500 | 2.1426 | 1.0 |
| 2.1729 | 55.38 | 1800 | 1.9422 | 1.0 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
jc9080/koalpaca-polyglot-12.8b-naverwebtoon-ver1
|
jc9080
| 2024-01-29T09:25:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-29T09:25:31Z |
---
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 ๐ค 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]
|
nullne/a2c-PandaReachDense-v3
|
nullne
| 2024-01-29T09:21:36Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T09:17:17Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.18 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
Mukalingam0813/Norwegian-intent-classifier-model2
|
Mukalingam0813
| 2024-01-29T09:20:13Z | 110 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-29T07:12:31Z |
---
license: apache-2.0
base_model: distilbert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Norwegian-intent-classifier-model2
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. -->
# Norwegian-intent-classifier-model2
This model is a fine-tuned version of [distilbert-base-multilingual-cased](https://huggingface.co/distilbert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1684
- Accuracy: 0.9718
## 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1615 | 1.0 | 9799 | 0.1682 | 0.9626 |
| 0.1024 | 2.0 | 19598 | 0.1526 | 0.9693 |
| 0.0689 | 3.0 | 29397 | 0.1684 | 0.9718 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-29T09:19:17Z | 40 | 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",
"Yhyu13/LMCocktail-Mistral-7B-v1",
"pytorch",
"arxiv:2311.13534",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"base_model:MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-29T09:08:21Z |
---
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
- Yhyu13/LMCocktail-Mistral-7B-v1
- pytorch
- arxiv:2311.13534
- license:apache-2.0
- autotrain_compatible
- endpoints_compatible
- region:us
model_name: LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/LMCocktail-Mistral-7B-v1-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/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: LMCocktail-Mistral-7B-v1-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/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF LMCocktail-Mistral-7B-v1-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/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/LMCocktail-Mistral-7B-v1-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/LMCocktail-Mistral-7B-v1-Mistral-7B-Instruct-v0.1-GGUF LMCocktail-Mistral-7B-v1-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 LMCocktail-Mistral-7B-v1-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 โ 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="./LMCocktail-Mistral-7B-v1-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="./LMCocktail-Mistral-7B-v1-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/MoMo-72B-lora-1.8.6-DPO-GGUF
|
LoneStriker
| 2024-01-29T09:12:57Z | 0 | 0 | null |
[
"gguf",
"en",
"arxiv:2305.18290",
"arxiv:2106.09685",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-01-28T23:52:13Z |
---
license: mit
language:
- en
---
# **Introduction**
MoMo-72B-lora-1.8.6-DPO is trained via Direct Preference Optimization([DPO](https://arxiv.org/abs/2305.18290)) from [MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) as its base model, with several optimizations in hyperparameters.
[MoMo-72B-LoRA-V1.4](https://huggingface.co/moreh/MoMo-72B-LoRA-V1.4) is trained via Supervised Fine-Tuning (SFT) using [LoRA](https://arxiv.org/abs/2106.09685), with the QWEN-72B model as its base-model.
Note that we did not exploit any form of weight merge.
For leaderboard submission, the trained weight is realigned for compatibility with llama.
MoMo-72B is trained using **[Moreh](https://moreh.io/)**'s [MoAI platform](https://moreh.io/product), which simplifies the training of large-scale models, and AMD's MI250 GPU.
## Details
### Used Librarys
- torch
- peft
### Used Datasets
- [slimorca](Open-Orca/SlimOrca)
- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- No other dataset was used
- No benchmark test set or the training set are used
- [data contamination check](https://github.com/swj0419/detect-pretrain-code-contamination) result
| Model | ARC | MMLU | TruthfulQA | GSM8K |
|------------------------------|-------|-------|-------|-------|
| **V1.8.6(result < 0.1, %)**| TBU |TBU | 0.73 | TBU |
### Used Environments
- AMD MI250 & MoAI platform
- Please visit https://moreh.io/product for more information about MoAI platform
- Or, contact us directly [contact@moreh.io](mailto:contact@moreh.io)
## How to use
```python
# pip install transformers==4.35.2
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("moreh/MoMo-72B-lora-1.8.6-DPO")
model = AutoModelForCausalLM.from_pretrained(
"moreh/MoMo-72B-lora-1.8.6-DPO"
)
```
|
TanHanlin/dqn-SpaceInvadersNoFrameskip-v4
|
TanHanlin
| 2024-01-29T09:03:25Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T09:02:43Z |
---
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: 849.00 +/- 334.09
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 TanHanlin -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 TanHanlin -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 TanHanlin
```
## 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', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
felixbrock/lang_base
|
felixbrock
| 2024-01-29T09:02:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"lora",
"arxiv:1910.09700",
"base_model:unsloth/mistral-7b-bnb-4bit",
"base_model:adapter:unsloth/mistral-7b-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2024-01-28T03:16:17Z |
---
base_model: unsloth/mistral-7b-bnb-4bit
library_name: transformers
tags:
- unsloth
- lora
---
# 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 ๐ค 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]
|
FounderOfHuggingface/gpt2_lora_r4_e2e_nlg_t3000_e5
|
FounderOfHuggingface
| 2024-01-29T09:02:30Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2024-01-29T09:02:27Z |
---
library_name: peft
base_model: gpt2
---
# 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
|
mrkprc1/segformer-b0-finetuned-sudoku
|
mrkprc1
| 2024-01-29T09:00:55Z | 174 | 0 |
transformers
|
[
"transformers",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] |
image-segmentation
| 2024-01-24T16:57:32Z |
---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: segformer-b0-finetuned-sudoku
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. -->
# segformer-b0-finetuned-sudoku
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the mrkprc1/SudokuBoundaries2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5465
- Mean Iou: 0.2407
- Mean Accuracy: 0.5
- Overall Accuracy: 0.4814
- Accuracy Unlabelled: 1.0
- Accuracy Sudoku-boundary: 0.0
- Iou Unlabelled: 0.4814
- Iou Sudoku-boundary: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabelled | Accuracy Sudoku-boundary | Iou Unlabelled | Iou Sudoku-boundary |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:-------------------:|:------------------------:|:--------------:|:-------------------:|
| 0.6257 | 2.5 | 20 | 0.7024 | 0.2992 | 0.4856 | 0.4769 | 0.7186 | 0.2525 | 0.3981 | 0.2002 |
| 0.6194 | 5.0 | 40 | 0.7513 | 0.2593 | 0.4960 | 0.4797 | 0.9332 | 0.0588 | 0.4633 | 0.0553 |
| 0.6134 | 7.5 | 60 | 0.8649 | 0.2428 | 0.4993 | 0.4809 | 0.9921 | 0.0065 | 0.4792 | 0.0065 |
| 0.4962 | 10.0 | 80 | 0.9245 | 0.2434 | 0.5006 | 0.4822 | 0.9949 | 0.0063 | 0.4805 | 0.0063 |
| 0.5552 | 12.5 | 100 | 0.8606 | 0.2442 | 0.5009 | 0.4826 | 0.9939 | 0.0080 | 0.4804 | 0.0079 |
| 0.6282 | 15.0 | 120 | 1.1507 | 0.2407 | 0.5000 | 0.4814 | 1.0000 | 0.0000 | 0.4813 | 0.0000 |
| 0.4042 | 17.5 | 140 | 1.0916 | 0.2408 | 0.4997 | 0.4811 | 0.9988 | 0.0007 | 0.4810 | 0.0007 |
| 0.8174 | 20.0 | 160 | 0.9731 | 0.2424 | 0.4991 | 0.4807 | 0.9926 | 0.0056 | 0.4792 | 0.0055 |
| 0.5353 | 22.5 | 180 | 0.9754 | 0.2409 | 0.4991 | 0.4805 | 0.9964 | 0.0017 | 0.4801 | 0.0017 |
| 0.4792 | 25.0 | 200 | 1.6835 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 0.4244 | 27.5 | 220 | 1.5039 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 0.376 | 30.0 | 240 | 2.2746 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 0.4129 | 32.5 | 260 | 2.0116 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 0.4717 | 35.0 | 280 | 1.8957 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 0.4229 | 37.5 | 300 | 1.7574 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 0.5708 | 40.0 | 320 | 2.0764 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 0.5826 | 42.5 | 340 | 1.6177 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 0.3765 | 45.0 | 360 | 1.8119 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 0.3704 | 47.5 | 380 | 1.6863 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
| 1.3265 | 50.0 | 400 | 1.5465 | 0.2407 | 0.5 | 0.4814 | 1.0 | 0.0 | 0.4814 | 0.0 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.2
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Tueb/mistral_b_german_finetuned_testing
|
Tueb
| 2024-01-29T08:58:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-01-29T08:58:29Z |
---
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 ๐ค 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]
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## 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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
SC99/Mistral-7B-summ-ia3-tuned-8h
|
SC99
| 2024-01-29T08:56:27Z | 0 | 0 | null |
[
"safetensors",
"arxiv:1910.09700",
"license:cc-by-4.0",
"region:us"
] | null | 2024-01-29T08:55:33Z |
---
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]
|
HexPlex0xFF/internlm-xcomposer2-7b
|
HexPlex0xFF
| 2024-01-29T08:56:11Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"internlmxcomposer2",
"feature-extraction",
"text-generation",
"custom_code",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2024-01-29T08:43:00Z |
---
license: apache-2.0
pipeline_tag: text-generation
---
<p align="center">
<img src="logo_en.png" width="400"/>
<p>
<p align="center">
<b><font size="6">InternLM-XComposer2</font></b>
<p>
<div align="center">
[๐ปGithub Repo](https://github.com/InternLM/InternLM-XComposer)
</div>
**InternLM-XComposer2** is a vision-language large model (VLLM) based on [InternLM2](https://github.com/InternLM/InternLM) for advanced text-image comprehension and composition.
We release InternLM-XComposer2 series in two versions:
- InternLM-XComposer2-VL: The pretrained VLLM model with InternLM2 as the initialization of the LLM, achieving strong performance on various multimodal benchmarks.
- InternLM-XComposer2: The finetuned VLLM for *Free-from Interleaved Text-Image Composition*.
### Import from Transformers
To load the InternLM-XComposer2-7B model using Transformers, use the following code:
```python
import torch
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
ckpt_path = "internlm/internlm-xcomposer2-7b"
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True).cuda()
# Set `torch_dtype=torch.float16` to load model in float16, otherwise it will be loaded as float32 and might cause OOM Error.
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
img_path_list = [
'./panda.jpg',
'./bamboo.jpeg',
]
images = []
for img_path in img_path_list:
image = Image.open(img_path).convert("RGB")
image = model.vis_processor(image)
images.append(image)
image = torch.stack(images)
query = '<ImageHere> <ImageHere>please write an article based on the images. Title: my favorite animal.'
with torch.cuda.amp.autocast():
response, history = model.chat(tokenizer, query=query, image=image, history=[], do_sample=False)
print(response)
""""
# My favorite animal is the panda. Pandas are one of the most popular animals in the world, and for good reason. They are adorable, cuddly creatures that have captured the hearts of people all over the globe.
Pandas are native to China and can be found in the wild in a few specific regions. However, they are also very popular in captivity, with many zoos around the world housing pandas as part of their exhibits. I have been fortunate enough to see pandas up close at several different zoos, and each time it was an amazing experience.
One thing that always strikes me about pandas is how much they love to eat bamboo. In fact, pandas spend almost all of their waking hours eating bamboo. This may not seem like a lot of fun, but pandas actually enjoy munching on this tough plant. It's fascinating to watch them chew through the tough stalks and leaves, and then lick their lips in satisfaction.
Another thing that I find interesting about pandas is their black and white fur. The combination of these two colors creates a striking contrast that makes pandas instantly recognizable. In addition, the black patches on their face give them a unique expression that seems to convey both playfulness and seriousness.
Despite their popularity, pandas do face some challenges. Their habitat is being destroyed by human activities such as logging and agriculture, which has led to a decline in their population. Additionally, pandas are considered endangered due to factors such as low reproductive rates and limited genetic diversity.
However, there are efforts underway to protect pandas and their habitats. Many organizations work to raise awareness about the importance of preserving these beautiful creatures, and governments in countries where pandas live are taking steps to conserve their natural environment.
In conclusion, pandas are truly remarkable animals that deserve our admiration and protection. With their distinctive appearance, playful personalities, and love of bamboo, it's no wonder that pandas have become so beloved around the world. Let's do what we can to ensure that future generations can continue to appreciate these wonderful creatures.
"""
```
### ้่ฟ Transformers ๅ ่ฝฝ
้่ฟไปฅไธ็ไปฃ็ ๅ ่ฝฝ InternLM-XComposer2-7B ๆจกๅ
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
ckpt_path = "internlm/internlm-xcomposer2-7b"
tokenizer = AutoTokenizer.from_pretrained(ckpt_path, trust_remote_code=True).cuda()
# `torch_dtype=torch.float16` ๅฏไปฅไปคๆจกๅไปฅ float16 ็ฒพๅบฆๅ ่ฝฝ๏ผๅฆๅ transformers ไผๅฐๆจกๅๅ ่ฝฝไธบ float32๏ผๅฏผ่ดๆพๅญไธ่ถณ
model = AutoModelForCausalLM.from_pretrained(ckpt_path, torch_dtype=torch.float16, trust_remote_code=True).cuda()
model = model.eval()
img_path_list = [
'./panda.jpg',
'./bamboo.jpeg',
]
images = []
for img_path in img_path_list:
image = Image.open(img_path).convert("RGB")
image = model.vis_processor(image)
images.append(image)
image = torch.stack(images)
query = '<ImageHere> <ImageHere>่ฏทๆ นๆฎๅพ็ๅไธ็ฏไฝๆ๏ผๆๆๅๆฌข็ๅฐๅจ็ฉใ่ฆๆฑ๏ผ้ๅ่งๅบฆ๏ผ็กฎๅฎ็ซๆ๏ผๆ็กฎๆไฝ๏ผ่ชๆๆ ้ขใ'
with torch.cuda.amp.autocast():
response, history = model.chat(tokenizer, query=query, image=image, history=[], do_sample=False)
print(response)
"""
# ๆๆๅๆฌข็ๅฐๅจ็ฉ
ๆๅๆฌข็ๅจ็ฉๆๅพๅค๏ผๆๆดปๆณผๅฏ็ฑ็ๅฐ็ใ็พไธฝ้ซ่ดต็ๅญ้ใๅถ็็็ฎๅญโฆโฆไฝๆๆๅๆฌข็ๆฏๆจๆๅฏๆฌ็ๅคง็็ซใ
ๅคง็็ซๆฏๅฝๅฎ๏ผๅฎๆ็้ป็ฝ็ธ้ด็ๆฏ่ฒ๏ผๅๆปๆป็่บซไฝ๏ผ่ไนไน็ๆ่๏ผๅคงๅคง็็ผ็ๅ็ญ็ญ็ๅฐพๅทดใๅฎ็่ณๆตๅฐๅฐ็๏ผๅไธค็ๆ ๅถ๏ผๅดๅทดๅๅฎฝๅๆ๏ผๅฐฑๅไธไธชโๆ็โ๏ผๅ่ข็ญๅฐ็ฒๅฃฎ๏ผ่ตฐ่ตท่ทฏๆฅๆๆๆๆ๏ผ้ๅธธๅฏ็ฑใ
ๅคง็็ซๅๆฌขๅ็ซนๅญ๏ผๆฏๅคฉ่ฆๅ30ๅคๆคๅข๏ผๅฎไปฌๅ็ซนๅญ็ๆ ทๅญๅพ็นๅซ๏ผๅ
ๆ็ซนๅญๅฌๆญ๏ผ็ถๅๆฑ็็ซนๅญๅ่ตทๆฅ๏ผๆๆถ่ฟไผๆ็ซนๅญๆๅฐ็ฉบไธญๅๆฅไฝ็ปง็ปญๅ๏ผๅฅฝๅๅจ่กจๆผๆๆไธๆ ทใๅ้ฅฑไบไปฅๅ๏ผๅฎไปฌๅฐฑๆๆดๆดๅฐ่บบๅจๅฐไธ็กๅคง่ง๏ผ็ๆฏไธชๅๅฏๅ
ถๅฎ็โๅคงๆ็ซโๅ๏ผ
ๅคง็็ซไธไป
็ฑๅ็ซนๅญ๏ผ่ฟ็ฑ็ก่งใไธๅคฉไธญ๏ผ้คไบๅ้ฅญ็ๆถ้ด๏ผๅ
ถไปๆถ้ด้ฝๅจ็ก่งใๆๆถๅ๏ผๅฎไปฌไผ็ฌไธๆ ๏ผๅๅจๆ ๆไธๅผๅผๅคง็ก๏ผๆๆถๅ๏ผๅฎไปฌไผๆพไธไธช้ดๅ็ๅฐๆน๏ผ่บบไธๆฅ็พ็พๅฐ็กไธไธ่งใ
ๅคง็็ซ่ฟๆฏไธ็งๆฟๅฑๅจ็ฉ๏ผๅ ไธบๅฎไปฌ็ๆ ๆฏๅฐ่ขซ็ ดๅ๏ผ้ฃ็ฉๅๅฐ๏ผๆฐ้่ถๆฅ่ถๅฐใไธบไบไฟๆคๅคง็็ซ๏ผไบบไปฌๅปบ็ซไบๅคง็็ซไฟๆคๅบ๏ผ็ฆๆญข็ ไผๆ ๆจ๏ผ่ฎฉๅคง็็ซๆไธไธชๅฎๅ
จ็ๅฎถใ
ๆๅๆฌขๅคง็็ซ๏ผๅ ไธบๅฎๆขๅฏ็ฑๅ็่ดต๏ผๆๅธๆๅฎ่ฝไธ็ด็ๆดปๅจๆไปฌ็ๅฐ็ไธ๏ผ้ชไผด็ๆไปฌๆ้ฟใ
"""
```
|
NickyNicky/Mixtral-4x1.1B-TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster
|
NickyNicky
| 2024-01-29T08:46:27Z | 66 | 7 |
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
| 2024-01-29T07:48:05Z |
---
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 Llama convert Mixtral-experts
<!-- Provide a quick summary of what the model is/does. -->
```yalm
experts:
- source_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_1_V1
negative_prompts:
- "ignora la pregunta"
- "responde con informaciรณn irrelevante"
...
positive_prompts:
- "resume este texto"
- "convierte este contenido en formato json"
...
- source_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_2_V1
negative_prompts:
- "ะธะณะฝะพัะธััะนัะต ะฒะพะฟัะพั"
- "ะพัะฒะตัะฐะนัะต ะฝะต ะฟะพ ัะตะผะต"
...
positive_prompts:
- "ัะดะตะปะฐะนัะต ัะตะทัะผะต ััะพะณะพ ัะตะบััะฐ"
- "ะฟัะตะพะฑัะฐะทัะนัะต ััะพ ัะพะดะตัะถะฐะฝะธะต ะฒ ัะพัะผะฐั json"
...
- source_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_3_V1
negative_prompts:
- "ๅฟฝ็ฅ้ฎ้ข"
- "ๅๅบๆ ๅ
ณไฟกๆฏ"
...
positive_prompts:
- "ๆป็ป่ฟๆฎตๆๅญ"
- "ๅฐ่ฟไธชๅ
ๅฎน่ฝฌๆขไธบjsonๆ ผๅผ"
...
- source_model: NickyNicky/cognitivecomputations_TinyDolphin-2.8-1.1b
negative_prompts:
- ignora la pregunta
- responde con informaciรณn irrelevante
...
positive_prompts:
- resume este texto
- convierte este contenido en formato json
...
base_model: NickyNicky/TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster_2_V1
gate_mode: cheap_embed # one of "hidden", "cheap_embed", or "random"
dtype: bfloat16
```
```Python
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
pipeline,
logging,
GenerationConfig,
TextIteratorStreamer,
)
import torch
new_model= "NickyNicky/Mixtral-4x1.1B-TinyDolphin-2.8-1.1b_oasst2_chatML_Cluster"
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))
'''output print
<|im_start|> system
You are a helpful AI assistant.
<|im_start|> user
podrias escribir un codigo de ejemplo en Python
<|im_start|> assistant
Claro, aquรญ estรก un ejemplo de cรณdigo en Python para crear un programa que imprime la suma de todos los nรบmeros enteros entre 1 y 10:
python
# Cรณdigo para imprimir la suma de todos los nรบmeros enteros entre 1 y 10
suma = 0
for i in range(1, 11):
suma += i
print("La suma de todos los nรบmeros enteros entre 1 y 10 es:", suma)
Este cรณdigo utiliza la funciรณn `range()` para crear una lista de los nรบmeros enteros entre 1 y 10, y la funciรณn `sum()` para sumar todos los elementos de la lista. La variable `suma` se asigna a 0 durante el ciclo for, y se incrementa cada vez que se realiza una iteraciรณn del ciclo.
El resultado de ejecutar este cรณdigo serรก:
La suma de todos los nรบmeros enteros entre 1 y 10 es: 55
Este ejemplo muestra cรณmo crear programas en Python que usan las instrucciones bรกsicas de programaciรณn, como las listas, las variables y las funciones. Tambiรฉn muestra cรณmo usar la funciรณn '''
```
|
alibidaran/distilbert-base-uncased-finetuned-Global_Intent
|
alibidaran
| 2024-01-29T08:44:46Z | 95 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"text_classification",
"generated_from_trainer",
"dataset:SetFit/amazon_massive_intent_en-US",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-26T09:20:47Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- text_classification
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-Global_Intent
results: []
datasets:
- SetFit/amazon_massive_intent_en-US
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-Global_Intent
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4771
- Accuracy: 0.8879
- F1: 0.8879
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.6913 | 1.0 | 180 | 0.6445 | 0.8431 | 0.8367 |
| 0.4537 | 2.0 | 360 | 0.4791 | 0.8824 | 0.8798 |
| 0.2192 | 3.0 | 540 | 0.4941 | 0.8775 | 0.8753 |
| 0.1098 | 4.0 | 720 | 0.4912 | 0.8844 | 0.8826 |
| 0.0628 | 5.0 | 900 | 0.4771 | 0.8879 | 0.8879 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
jlbaker361/ft1000-30-biglimit
|
jlbaker361
| 2024-01-29T08:38:46Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2024-01-29T04:31:51Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - jlbaker361/ft1000-30-biglimit
These are LoRA adaption weights for stabilityai/stable-diffusion-2-base. The weights were fine-tuned on the jlbaker361/wikiart-balanced1000 dataset.
Training epochs = 1
num_train_timesteps = 30
You can find some example images in the following.
|
Nicolas852/Reinforce-Pixelcopter-PLE-v0
|
Nicolas852
| 2024-01-29T08:34:51Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T08:34:46Z |
---
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: 44.50 +/- 37.58
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
|
MinsuKi/mistral-test
|
MinsuKi
| 2024-01-29T08:34:00Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-29T05:29:48Z |
---
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 ๐ค 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]
|
wahaha1987/LunarLander-v2
|
wahaha1987
| 2024-01-29T08:31:57Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T08:31:51Z |
---
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: -107.17 +/- 53.96
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': 50000
'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': 'wahaha1987/LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
malhajar/Platypus2-70B-instruct-4bit-gptq
|
malhajar
| 2024-01-29T08:31:14Z | 1,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:yahma/alpaca-cleaned",
"arxiv:2308.07317",
"arxiv:2307.09288",
"arxiv:2210.17323",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-08-23T11:33:53Z |
---
datasets:
- yahma/alpaca-cleaned
---
# Platypus2-70B-instruct-4bit-gptq
Platypus2-70B-instruct-4bit-gptq is a qunatnized version of [`garage-bAInd/Platypus2-70B-instruct`](https://huggingface.co/garage-bAInd/Platypus2-70B-instruct) using GPTQ Quantnization.
This model is only 35 GB in size in comparision with the original garage-bAInd/Platypus2-70B-instruct 127 GB and can run on a single A6000 GPU
### Model Details
* **Quantnized by**: [`Mohamad Alhajar`](https://www.linkedin.com/in/muhammet-alhajar/)
* **Model type:** quantnized version of Platypus2-70B-instruct using 4bit quantnization
* **Language(s)**: English
### Prompt Template
```
### Instruction:
<prompt> (without the <>)
### Response:
```
### Training Dataset
`Platypus2-70B-instruct-4bit-gptq` quantnized using gptq on Alpaca dataset [`yahma/alpaca-cleaned`](https://huggingface.co/datasets/yahma/alpaca-cleaned).
### Training Procedure
`garage-bAInd/Platypus2-70B` was fine-tuned using gptq on 2 L40 48GB.
## How to Get Started with the Model
First install auto_gptq with
```shell
pip install auto_gptq
```
Use the code sample provided in the original post to interact with the model.
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_id = "malhajar/Platypus2-70B-instruct-4bit-gptq"
model = AutoGPTQForCausalLM.from_quantized(model_id,inject_fused_attention=False,
use_safetensors=True,
trust_remote_code=False,
use_triton=False,
quantize_config=None)
tokenizer = AutoTokenizer.from_pretrained(model_id)
question: "Who was the first person to walk on the moon?"
# For generating a response
prompt = '''
### Instruction:
{question}
### Response:'''
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
output = model.generate(input_ids)
response = tokenizer.decode(output[0])
print(response)
```
### Citations
```bibtex
@article{platypus2023,
title={Platypus: Quick, Cheap, and Powerful Refinement of LLMs},
author={Ariel N. Lee and Cole J. Hunter and Nataniel Ruiz},
booktitle={arXiv preprint arxiv:2308.07317},
year={2023}
}
```
```bibtex
@misc{touvron2023llama,
title={Llama 2: Open Foundation and Fine-Tuned Chat Models},
author={Hugo Touvron and Louis Martin and Kevin Stone and Peter Albert and Amjad Almahairi and Yasmine Babaei and Nikolay Bashlykov year={2023},
eprint={2307.09288},
archivePrefix={arXiv},
}
```
```bibtex
@misc{frantar2023gptq,
title={GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers},
author={Elias Frantar and Saleh Ashkboos and Torsten Hoefler and Dan Alistarh},
year={2023},
eprint={2210.17323},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
|
SudiptoPramanik/Mistral_shards
|
SudiptoPramanik
| 2024-01-29T08:29:09Z | 61 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-01-29T08:26:06Z |
---
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 ๐ค 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]
|
MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF
|
MaziyarPanahi
| 2024-01-29T08:11:14Z | 78 | 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",
"defog/sqlcoder-7b",
"pytorch",
"code",
"en",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"license:apache-2.0",
"base_model:MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1",
"base_model:quantized:MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1",
"conversational"
] |
text-generation
| 2024-01-29T08:00:29Z |
---
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
- defog/sqlcoder-7b
- pytorch
- code
- en
- license:cc-by-sa-4.0
- autotrain_compatible
- endpoints_compatible
- region:us
- license:apache-2.0
model_name: sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF
base_model: MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# [MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF)
- Model creator: [MaziyarPanahi](https://huggingface.co/MaziyarPanahi)
- Original model: [MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1)
## Description
[MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF) contains GGUF format model files for [MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1](https://huggingface.co/MaziyarPanahi/sqlcoder-7b-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/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF) and below it, a specific filename to download, such as: sqlcoder-7b-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/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF sqlcoder-7b-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/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF](https://huggingface.co/MaziyarPanahi/sqlcoder-7b-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/sqlcoder-7b-Mistral-7B-Instruct-v0.1-GGUF sqlcoder-7b-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 sqlcoder-7b-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 โ 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="./sqlcoder-7b-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="./sqlcoder-7b-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)
|
Znerual/TinyLlama-1.1B-intermediate-step-1431k-3T-AWQ
|
Znerual
| 2024-01-29T07:53:01Z | 59 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:cerebras/SlimPajama-627B",
"dataset:bigcode/starcoderdata",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2024-01-26T14:31:47Z |
---
language:
- en
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
model_name: Tinyllama 1.1B Intermediate Step 1431K 3T
model_creator: TinyLlama
model_type: tinyllama
prompt_template: '{prompt}'
quantized_by: Znerual
---
# Tinyllama 1.1B Intermediate Step 1431K 3T - AWQ
## Description
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `Znerual/TinyLlama-1.1B-intermediate-step-1431k-3T-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `TinyLlama-1.1B-intermediate-step-1431k-3T-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model Znerual/TinyLlama-1.1B-intermediate-step-1431k-3T-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''[INST] {prompt} [/INST]
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="Znerual/TinyLlama-1.1B-intermediate-step-1431k-3T-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id Znerual/TinyLlama-1.1B-intermediate-step-1431k-3T-AWQ --port 3000 --quantize awq --max-input-length 1902 --max-total-tokens 2048 --max-batch-prefill-tokens 2048
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''[INST] {prompt} [/INST]
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "Znerual/TinyLlama-1.1B-intermediate-step-1431k-3T-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''[INST] {prompt} [/INST]
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
# Original model card: Tinyllama 1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs ๐๐. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Collection
This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
#### Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
| Pythia-1.0B | 300B | 47.16 | 31.40| 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-50K-104b | 103B | 43.50 | 29.80| 53.28 | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
| TinyLlama-1.1B-intermediate-step-240k-503b| 503B | 49.56 |31.40 |55.80 |26.54 |48.32 |56.91 |69.42 | 48.28 |
| TinyLlama-1.1B-intermediate-step-480k-1007B | 1007B | 52.54 | 33.40 | 55.96 | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
| TinyLlama-1.1B-intermediate-step-715k-1.5T | 1.5T | 53.68 | 35.20 | 58.33 | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
| TinyLlama-1.1B-intermediate-step-955k-2T | 2T | 54.63 | 33.40 | 56.83 | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
| TinyLlama-1.1B-intermediate-step-1195k-2.5T | 2.5T | 58.96 | 34.40 | 58.72 | 31.91 | 56.78 | 63.21 | 73.07 | 53.86|
| TinyLlama-1.1B-intermediate-step-1431k-3T | 3T | 59.20 | 36.00 | 59.12 | 30.12 | 55.25 | 57.83 | 73.29 | 52.99|
|
coke0zero/a2c-PandaReachDense-v3
|
coke0zero
| 2024-01-29T07:46:53Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-01-29T07:42:38Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.24 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
|
hoangvanvietanh/user_35621758bf084337aad673e1cc332d6f_model
|
hoangvanvietanh
| 2024-01-29T07:36:25Z | 60 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ja",
"dataset:pxaudio/user_35621758bf084337aad673e1cc332d6f_model",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-29T07:33:47Z |
---
language:
- ja
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- pxaudio/user_35621758bf084337aad673e1cc332d6f_model
model-index:
- name: PXAudio Whisper For user_35621758bf084337aad673e1cc332d6f
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. -->
# PXAudio Whisper For user_35621758bf084337aad673e1cc332d6f
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ja 0.1 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: 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: 500
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.13.3
|
hoangvanvietanh/user_35621758bf084337aad673e1cc332d6f_model_large
|
hoangvanvietanh
| 2024-01-29T07:26:36Z | 60 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ja",
"dataset:pxaudio/user_35621758bf084337aad673e1cc332d6f_model",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-29T07:24:41Z |
---
language:
- ja
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- pxaudio/user_35621758bf084337aad673e1cc332d6f_model
model-index:
- name: PXAudio Whisper For user_35621758bf084337aad673e1cc332d6f
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. -->
# PXAudio Whisper For user_35621758bf084337aad673e1cc332d6f
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the ja 0.1 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: 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: 500
- training_steps: 100
### Training results
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.13.3
|
hbijen/opus-mt-zh-en-finetuned-en-to-mm
|
hbijen
| 2024-01-29T07:20:28Z | 120 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-zh-en",
"base_model:finetune:Helsinki-NLP/opus-mt-zh-en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-28T20:26:58Z |
---
license: cc-by-4.0
base_model: Helsinki-NLP/opus-mt-zh-en
tags:
- generated_from_trainer
model-index:
- name: opus-mt-zh-en-finetuned-en-to-mm
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. -->
# opus-mt-zh-en-finetuned-en-to-mm
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en) 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: 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 163 | 0.1393 | 10.1987 | 53.7462 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
lixugang/ch_text_001
|
lixugang
| 2024-01-29T07:18:58Z | 93 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:shibing624/text2vec-base-chinese",
"base_model:finetune:shibing624/text2vec-base-chinese",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-25T09:49:14Z |
---
license: apache-2.0
base_model: shibing624/text2vec-base-chinese
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ch_text_001
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. -->
# ch_text_001
This model is a fine-tuned version of [shibing624/text2vec-base-chinese](https://huggingface.co/shibing624/text2vec-base-chinese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0768
- Accuracy: 0.9870
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0781 | 1.0 | 2125 | 0.0781 | 0.9849 |
| 0.0545 | 2.0 | 4250 | 0.0768 | 0.9870 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Zangs3011/gpt2_MonsterInstruct
|
Zangs3011
| 2024-01-29T07:18:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai-community/gpt2",
"base_model:adapter:openai-community/gpt2",
"region:us"
] | null | 2024-01-29T07:18:15Z |
---
library_name: peft
base_model: gpt2
---
# 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
|
Rogendo/sw-en
|
Rogendo
| 2024-01-29T07:13:43Z | 549 | 2 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-01-27T14:14:03Z |
---
license: mit
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is a pre-trained language translation model that aims to create a translation system for English and Swahili lanuages. It is a fine-tuned version of Helsinki-NLP/opus-mt-en-swc on an unknown dataset.
## Model Details
- Transformer architecture used
- Trained on a 210000 corpus pairs
- Pre-trained Helsinki-NLP/opus-mt-en-swc
- 2 models to enforce biderectional translation
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Peter Rogendo, Frederick Kioko
- **Model type:** Transformer
- **Language(s) (NLP):** Transformer, Pandas, Numpy
- **License:** Distributed under the MIT License
- **Finetuned from model [Helsinki-NLP/opus-mt-en-swc]:** [This pre-trained model was re-trained on a swahili-english sentence pairs that were collected across Kenya. Swahili is the national language and is among the top three of the most spoken language in Africa. The sentences that were used to train this model were 210000 in total.]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/Rogendo/Eng-Swa-Translator]
- **Paper [optional]:**
- **Demo [optional]:**
## 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. -->
This translation model is intended to be used in many cases, from language translators, screen assistants, to even in official cases such as translating legal documents.
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text2text-generation", model="Rogendo/sw-en")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Rogendo/sw-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Rogendo/sw-en")
### 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 a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text2text-generation", model="Rogendo/sw-en")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Rogendo/sw-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Rogendo/sw-en")
## 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. -->
curl -X GET \
"https://datasets-server.huggingface.co/rows?dataset=Rogendo%2FEnglish-Swahili-Sentence-Pairs&config=default&split=train&offset=0&length=100"
View More
https://huggingface.co/datasets/Rogendo/English-Swahili-Sentence-Pairs
### 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. -->
## Model Card Authors [optional]
Peter Rogendo
## Model Card Contact
progendo@kabarak.ac.ke
|
freud-sensei/counsel_chatbot
|
freud-sensei
| 2024-01-29T07:13:29Z | 14 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:skt/kogpt2-base-v2",
"base_model:finetune:skt/kogpt2-base-v2",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-29T04:09:26Z |
---
license: cc-by-nc-sa-4.0
base_model: skt/kogpt2-base-v2
tags:
- generated_from_trainer
model-index:
- name: counsel_chatbot
results: []
widget:
- text: "์ง๋ฌธ: ์ด์ ์ ์ ๊ฑฐ์ ๋ชป ์์ ํผ๊ณคํด. ๋ต๋ณ:"
---
<!-- 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. -->
# counsel_chatbot
This model is a fine-tuned version of [skt/kogpt2-base-v2](https://huggingface.co/skt/kogpt2-base-v2) on the None dataset.
## Model description
* ํ๊ตญ์ด ์ฌ๋ฆฌ์๋ด ๋ฐ์ดํฐ๋ฅผ ์ด์ฉํด Ko-GPT2๋ฅผ fine-tuningํ์ฌ ๋ง๋ ์ฌ๋ฆฌ์๋ด ๋ชจํ์
๋๋ค.
* ์
๋ ฅ์ ํ์ค ๋ '์ง๋ฌธ: [์ค์ ์ง๋ฌธ] ๋ต๋ณ:'์ ํํ๋ก ์ง๋ฌธํ์๊ธฐ ๋ฐ๋๋๋ค.
* Colab์ ๋ฉ๋ชจ๋ฆฌ ํ๊ณ ์ ํ๋ จ์ ์ฅ์๊ฐ ํ ์ ์์ด, ๋ชจํ์ด ์๋ฑํ๊ฑฐ๋ ์ด์ํ ๋ต์ ํ ์ ์์ต๋๋ค. ๊ท์ฝ๊ฒ ๋ด ์ฃผ์ธ์.
## Training and evaluation data
AI Hub์ <๊ฐ์ฑ ๋ํ ๋ง๋ญ์น>๋ฅผ ์ฌ์ฉํ์ต๋๋ค.
https://aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&dataSetSn=86
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-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: 1
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
Rogendo/en-sw
|
Rogendo
| 2024-01-29T07:11:26Z | 443 | 1 |
transformers
|
[
"transformers",
"pytorch",
"marian",
"text2text-generation",
"translation",
"en",
"sw",
"dataset:Rogendo/English-Swahili-Sentence-Pairs",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2024-01-27T13:06:01Z |
---
license: mit
datasets:
- Rogendo/English-Swahili-Sentence-Pairs
language:
- en
- sw
metrics:
- accuracy
library_name: transformers
pipeline_tag: translation
---
---
license: mit
datasets:
- Rogendo/English-Swahili-Sentence-Pairs
language:
- en
- sw
metrics:
- accuracy
library_name: transformers
---
# Model Card for Rogendo/en-sw model
<!-- Provide a quick summary of what the model is/does. -->
This is a pre-trained language translation model that aims to create a translation system for English and Swahili lanuages. It is a fine-tuned version of Helsinki-NLP/opus-mt-en-swc on an unknown dataset.
## Model Details
- Transformer architecture used
- Trained on a 210000 corpus pairs
- Pre-trained Helsinki-NLP/opus-mt-en-swc
- 2 models to enforce biderectional translation
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Peter Rogendo, Frederick Kioko
- **Model type:** Transformer
- **Language(s) (NLP):** Transformer, Pandas, Numpy
- **License:** Distributed under the MIT License
- **Finetuned from model [Helsinki-NLP/opus-mt-en-swc]:** This pre-trained model was re-trained on a swahili-english sentence pairs that were collected across Kenya. Swahili is the national language and is among the top three of the most spoken language in Africa. The sentences that were used to train this model were 210000 in total.
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/Rogendo/Eng-Swa-Translator
## 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. -->
This translation model is intended to be used in many cases, from language translators, screen assistants, to even in official cases such as translating legal documents.
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
pip install sentencepiece
from transformers import pipeline
model_checkpoint = "Rogendo/en-sw"
fine_tuned_model = pipeline("translation", model=model_checkpoint)
fine_tuned_model("Earlier today, I saw her going through the stalls in the market")
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text2text-generation", model="Rogendo/sw-en")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Rogendo/sw-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Rogendo/sw-en")
### 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 a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text2text-generation", model="Rogendo/sw-en")
# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Rogendo/sw-en")
model = AutoModelForSeq2SeqLM.from_pretrained("Rogendo/sw-en")
## 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. -->
curl -X GET \
"https://datasets-server.huggingface.co/rows?dataset=Rogendo%2FEnglish-Swahili-Sentence-Pairs&config=default&split=train&offset=0&length=100"
View More
https://huggingface.co/datasets/Rogendo/English-Swahili-Sentence-Pairs
### 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 Card Authors [optional]
Peter Rogendo
## Model Card Contact
progendo@kabarak.ac.ke
|
charleschen2022/zephyr-support-chatbot-2048
|
charleschen2022
| 2024-01-29T07:02:04Z | 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 | 2024-01-29T05:49:49Z |
---
license: mit
base_model: TheBloke/zephyr-7B-alpha-GPTQ
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: zephyr-support-chatbot-2048
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-2048
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: 2500
- 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
|
NBA55/llama2-qlora-finetunined-4-bit-prev-and-4.14k-datasets-1-epoch
|
NBA55
| 2024-01-29T07:01:45Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2024-01-04T17:37:45Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
ThuyNT03/KLTN_CSI_PhoBERRT
|
ThuyNT03
| 2024-01-29T06:59:21Z | 91 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:vinai/phobert-base-v2",
"base_model:finetune:vinai/phobert-base-v2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-29T06:36:07Z |
---
base_model: vinai/phobert-base-v2
tags:
- generated_from_trainer
model-index:
- name: KLTN_CSI_PhoBERRT
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. -->
# KLTN_CSI_PhoBERRT
This model is a fine-tuned version of [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0032
## 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: 8
- seed: 41
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 369 | 0.1454 |
| 0.2396 | 2.0 | 738 | 0.0870 |
| 0.1364 | 3.0 | 1107 | 0.0467 |
| 0.1364 | 4.0 | 1476 | 0.0268 |
| 0.0751 | 5.0 | 1845 | 0.0173 |
| 0.0419 | 6.0 | 2214 | 0.0224 |
| 0.0226 | 7.0 | 2583 | 0.0129 |
| 0.0226 | 8.0 | 2952 | 0.0049 |
| 0.0108 | 9.0 | 3321 | 0.0033 |
| 0.0087 | 10.0 | 3690 | 0.0032 |
### Framework versions
- Transformers 4.37.1
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
magus4450/distilhubert-finetuned-gtzan
|
magus4450
| 2024-01-29T06:53:37Z | 145 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2024-01-08T09:46:48Z |
---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.835
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9299
- Accuracy: 0.835
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1474 | 1.0 | 100 | 2.1098 | 0.47 |
| 1.5063 | 2.0 | 200 | 1.5695 | 0.575 |
| 1.2171 | 3.0 | 300 | 1.1629 | 0.685 |
| 0.9388 | 4.0 | 400 | 0.9617 | 0.7 |
| 0.6208 | 5.0 | 500 | 0.9273 | 0.685 |
| 0.6771 | 6.0 | 600 | 0.7753 | 0.785 |
| 0.5799 | 7.0 | 700 | 0.8492 | 0.695 |
| 0.1527 | 8.0 | 800 | 0.6581 | 0.805 |
| 0.0586 | 9.0 | 900 | 0.6788 | 0.82 |
| 0.0355 | 10.0 | 1000 | 0.7627 | 0.81 |
| 0.0186 | 11.0 | 1100 | 0.7585 | 0.82 |
| 0.0102 | 12.0 | 1200 | 0.8328 | 0.825 |
| 0.0074 | 13.0 | 1300 | 0.8543 | 0.835 |
| 0.0063 | 14.0 | 1400 | 0.8574 | 0.83 |
| 0.0271 | 15.0 | 1500 | 0.8889 | 0.835 |
| 0.0043 | 16.0 | 1600 | 0.9197 | 0.83 |
| 0.0045 | 17.0 | 1700 | 0.9130 | 0.835 |
| 0.0036 | 18.0 | 1800 | 0.9242 | 0.835 |
| 0.0042 | 19.0 | 1900 | 0.9279 | 0.835 |
| 0.0034 | 20.0 | 2000 | 0.9299 | 0.835 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.14.7
- Tokenizers 0.15.0
|
thiagobarbosa/whisper-base-common-voice-16-pt-v8
|
thiagobarbosa
| 2024-01-29T06:52:00Z | 62 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"pt",
"dataset:mozilla-foundation/common_voice_16_0",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-29T01:43:16Z |
---
language:
- pt
license: apache-2.0
base_model: openai/whisper-base
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_0
metrics:
- wer
model-index:
- name: Whisper Base using Common Voice 16 (pt)
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Mozilla Common Voices - 16.0 - Portuguese
type: mozilla-foundation/common_voice_16_0
config: pt
split: test
args: pt
metrics:
- name: Wer
type: wer
value: 26.192630898513254
---
<!-- 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 Base using Common Voice 16 (pt)
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Mozilla Common Voices - 16.0 - Portuguese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4574
- Wer: 26.1926
- Wer Normalized: 20.0029
## 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: 32
- 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: 500
- training_steps: 7000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Wer Normalized |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:--------------:|
| 0.4883 | 0.74 | 1000 | 0.3803 | 28.0317 | 21.8327 |
| 0.2659 | 1.48 | 2000 | 0.3677 | 26.3688 | 20.1666 |
| 0.1251 | 2.22 | 3000 | 0.3730 | 26.3752 | 20.4620 |
| 0.1071 | 2.96 | 4000 | 0.3867 | 25.5026 | 19.5470 |
| 0.0523 | 3.7 | 5000 | 0.4148 | 25.7094 | 19.6851 |
| 0.02 | 4.44 | 6000 | 0.4491 | 25.6803 | 19.5759 |
| 0.0134 | 5.18 | 7000 | 0.4574 | 26.1926 | 20.0029 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1
- Datasets 2.16.1
- Tokenizers 0.15.0
|
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