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{}
|
HenryCai1129/adapter-toxic2nontoxic-100-50-3e-05
| null |
[
"region:us"
] | null |
2024-04-25T10:54:33+00:00
|
|
text-classification
|
transformers
|
<!-- 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. -->
# 2504separado2
This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6044
- Accuracy: 0.8529
- Precision: 0.8532
- Recall: 0.8529
- F1: 0.8529
- Ratio: 0.4874
## 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: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 4
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.5772 | 0.9870 | 38 | 0.6198 | 0.8235 | 0.8350 | 0.8235 | 0.8220 | 0.4076 |
| 0.4565 | 2.0 | 77 | 0.6044 | 0.8529 | 0.8532 | 0.8529 | 0.8529 | 0.4874 |
| 0.4312 | 2.9870 | 115 | 0.6445 | 0.8445 | 0.8475 | 0.8445 | 0.8442 | 0.5462 |
| 0.4419 | 3.9481 | 152 | 0.6299 | 0.8445 | 0.8457 | 0.8445 | 0.8444 | 0.5294 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "projecte-aina/roberta-base-ca-v2-cased-te", "model-index": [{"name": "2504separado2", "results": []}]}
|
adriansanz/2504separado2
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:projecte-aina/roberta-base-ca-v2-cased-te",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:54:54+00:00
|
null |
peft
|
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
|
{"library_name": "peft"}
|
lekhapinninti/llama-2-7b-enhanced-attention
| null |
[
"peft",
"region:us"
] | null |
2024-04-25T10:55:43+00:00
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABCZ_9_54_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T10:55:45+00:00
|
|
text-to-image
|
diffusers
|
# SDXL LoRA DreamBooth - computational-mama/tardispace
<Gallery />
## Model description
### These are computational-mama/tardispace LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
## Download model
### Use it with UIs such as AUTOMATIC1111, Comfy UI, SD.Next, Invoke
- **LoRA**: download **[`tardispace.safetensors` here 💾](/computational-mama/tardispace/blob/main/tardispace.safetensors)**.
- Place it on your `models/Lora` folder.
- On AUTOMATIC1111, load the LoRA by adding `<lora:tardispace:1>` to your prompt. On ComfyUI just [load it as a regular LoRA](https://comfyanonymous.github.io/ComfyUI_examples/lora/).
- *Embeddings*: download **[`tardispace_emb.safetensors` here 💾](/computational-mama/tardispace/blob/main/tardispace_emb.safetensors)**.
- Place it on it on your `embeddings` folder
- Use it by adding `tardispace_emb` to your prompt. For example, `A tardispace_emb character`
(you need both the LoRA and the embeddings as they were trained together for this LoRA)
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('computational-mama/tardispace', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='computational-mama/tardispace', filename='tardispace_emb.safetensors' repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
pipeline.load_textual_inversion(state_dict["clip_g"], token=["<s0>", "<s1>"], text_encoder=pipeline.text_encoder_2, tokenizer=pipeline.tokenizer_2)
image = pipeline('A <s0><s1> character').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `TOK` → use `<s0><s1>` in your prompt
## Details
All [Files & versions](/computational-mama/tardispace/tree/main).
The weights were trained using [🧨 diffusers Advanced Dreambooth Training Script](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py).
LoRA for the text encoder was enabled. False.
Pivotal tuning was enabled: True.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
|
{"license": "openrail++", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers", "lora", "template:sd-lora"], "widget": [{"text": "A <s0><s1> character pink green tardigrade floating in an empty curvilinear space", "output": {"url": "image-0.png"}}, {"text": "A <s0><s1> character sleepy blue green tardigrade laying on the floor of an empty space with columns", "output": {"url": "image-1.png"}}, {"text": "A <s0><s1> character a green pink tardigrade standing in front of a camera in an empty space with colonnade", "output": {"url": "image-2.png"}}, {"text": "A <s0><s1> character a blue purple tardigrade walking in a curvilinear empty space", "output": {"url": "image-3.png"}}], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "A <s0><s1> character"}
|
computational-mama/tardispace
| null |
[
"diffusers",
"tensorboard",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null |
2024-04-25T10:56:04+00:00
|
text-generation
|
transformers
|
# Llama3-portuguese-luana-8b-instruct
<p align="center">
<img src="https://raw.githubusercontent.com/rhaymisonbetini/huggphotos/main/llama3-luana.webp" width="50%" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>
This model was trained with a superset of 290,000 chat in Portuguese.
The model comes to help fill the gap in models in Portuguese. Tuned from the Llama3 8B, the model was adjusted mainly for chat.
# How to use
### FULL MODEL : A100
### HALF MODEL: L4
### 8bit or 4bit : T4 or V100
You can use the model in its normal form up to 4-bit quantization. Below we will use both approaches.
Remember that verbs are important in your prompt. Tell your model how to act or behave so that you can guide them along the path of their response.
Important points like these help models (even smaller models like 8b) to perform much better.
```python
!pip install -q -U transformers
!pip install -q -U accelerate
!pip install -q -U bitsandbytes
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model = AutoModelForCausalLM.from_pretrained("rhaymison/Llama3-portuguese-luana-8b-instruct", device_map= {"": 0})
tokenizer = AutoTokenizer.from_pretrained("rhaymison/Llama3-portuguese-luana-8b-instruct")
model.eval()
```
You can use with Pipeline.
```python
from transformers import pipeline
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
do_sample=True,
max_new_tokens=256,
num_beams=2,
temperature=0.3,
top_k=50,
top_p=0.95,
early_stopping=True,
pad_token_id=tokenizer.eos_token_id,
)
def format_prompt(question:str):
system_prompt = "Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido."
return f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{ system_prompt }<|eot_id|><|start_header_id|>user<|end_header_id|>
{ question }<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
prompt = format_prompt("Me explique quem eram os Romanos")
result = pipe(prompt)
result[0]["generated_text"].split("assistant<|end_header_id|>")[1]
#Os romanos eram um povo antigo que habitava a península italiana, particularmente na região que hoje é conhecida como Itália. Eles estabeleceram o Império Romano,
#que se tornou uma das maiores e mais poderosas civilizações da história. Os romanos eram conhecidos por suas conquistas militares, sua arquitetura e engenharia
#impressionantes e sua influência duradoura na cultura ocidental.
#Os romanos eram uma sociedade complexa que consistia em várias classes sociais, incluindo senadores, cavaleiros, plebeus e escravos.
#Eles tinham um sistema de governo baseado em uma república, onde o poder era dividido entre o Senado e a Assembléia do Povo.
#Os romanos eram conhecidos por suas conquistas militares, que os levaram a expandir seu império por toda a Europa, Ásia e África.
#Eles estabeleceram uma rede de estradas, pontes e outras estruturas que facilitaram a comunicação e o comércio.
```
If you are having a memory problem such as "CUDA Out of memory", you should use 4-bit or 8-bit quantization.
For the complete model in colab you will need the A100.
If you want to use 4bits or 8bits, T4 or L4 will already solve the problem.
# 4bits example
```python
from transformers import BitsAndBytesConfig
import torch
nb_4bit_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True
)
model = AutoModelForCausalLM.from_pretrained(
base_model,
quantization_config=bnb_config,
device_map={"": 0}
)
```
# Open Portuguese LLM Leaderboard Evaluation Results
Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/rhaymison/Llama3-portuguese-luana-8b-instruct) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
| Metric | Value |
|--------------------------|---------|
|Average |**68.15**|
|ENEM Challenge (No Images)| 69|
|BLUEX (No Images) | 51.74|
|OAB Exams | 47.56|
|Assin2 RTE | 89.24|
|Assin2 STS | 72.87|
|FaQuAD NLI | 68.94|
|HateBR Binary | 85.93|
|PT Hate Speech Binary | 64.16|
|tweetSentBR | 63.91|
### Comments
Any idea, help or report will always be welcome.
email: [email protected]
<div style="display:flex; flex-direction:row; justify-content:left">
<a href="https://www.linkedin.com/in/heleno-betini-2b3016175/" target="_blank">
<img src="https://img.shields.io/badge/LinkedIn-0077B5?style=for-the-badge&logo=linkedin&logoColor=white">
</a>
<a href="https://github.com/rhaymisonbetini" target="_blank">
<img src="https://img.shields.io/badge/GitHub-100000?style=for-the-badge&logo=github&logoColor=white">
</a>
|
{"language": ["pt"], "license": "apache-2.0", "library_name": "transformers", "tags": ["portugues", "portuguese", "QA", "instruct"], "datasets": ["rhaymison/superset"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "pipeline_tag": "text-generation", "model-index": [{"name": "Llama3-portuguese-luana-8b-instruct", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "ENEM Challenge (No Images)", "type": "eduagarcia/enem_challenge", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 69.0, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "BLUEX (No Images)", "type": "eduagarcia-temp/BLUEX_without_images", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 51.74, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "OAB Exams", "type": "eduagarcia/oab_exams", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 47.56, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 RTE", "type": "assin2", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 89.24, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 STS", "type": "eduagarcia/portuguese_benchmark", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "pearson", "value": 72.87, "name": "pearson"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "FaQuAD NLI", "type": "ruanchaves/faquad-nli", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 68.94, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HateBR Binary", "type": "ruanchaves/hatebr", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 85.93, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "PT Hate Speech Binary", "type": "hate_speech_portuguese", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 64.16, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "tweetSentBR", "type": "eduagarcia/tweetsentbr_fewshot", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 63.91, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=rhaymison/Llama3-portuguese-luana-8b-instruct", "name": "Open Portuguese LLM Leaderboard"}}]}]}
|
rhaymison/Llama3-portuguese-luana-8b-instruct
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"portugues",
"portuguese",
"QA",
"instruct",
"conversational",
"pt",
"dataset:rhaymison/superset",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:56:05+00:00
|
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **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]
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[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. -->
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[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]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
tomaszki/llama-10-b
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T10:57:31+00:00
|
null |
diffusers
|
# 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 🧨 diffusers 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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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### Compute Infrastructure
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#### Hardware
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#### Software
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## 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. -->
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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. -->
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## Model Card Authors [optional]
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## Model Card Contact
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|
{"library_name": "diffusers"}
|
Primeness/prime
| null |
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null |
2024-04-25T10:58:27+00:00
|
null | null |
{}
|
Ronny242/qwen1.5-llm
| null |
[
"gguf",
"region:us"
] | null |
2024-04-25T11:02:02+00:00
|
|
null |
transformers
|
# Uploaded model
- **Developed by:** xiaoliy2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}
|
xiaoliy2/llama-3-8b-ft-model-1
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:02:53+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# 2504separado3
This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6752
- Accuracy: 0.8445
- Precision: 0.8451
- Recall: 0.8445
- F1: 0.8445
- Ratio: 0.5210
## 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: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 4
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.404 | 0.9870 | 38 | 0.7068 | 0.8151 | 0.8174 | 0.8151 | 0.8148 | 0.5420 |
| 0.3648 | 2.0 | 77 | 0.6934 | 0.8277 | 0.8317 | 0.8277 | 0.8272 | 0.5546 |
| 0.3989 | 2.9870 | 115 | 0.6752 | 0.8445 | 0.8451 | 0.8445 | 0.8445 | 0.5210 |
| 0.4125 | 3.9481 | 152 | 0.6799 | 0.8361 | 0.8367 | 0.8361 | 0.8361 | 0.5210 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "projecte-aina/roberta-base-ca-v2-cased-te", "model-index": [{"name": "2504separado3", "results": []}]}
|
adriansanz/2504separado3
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:projecte-aina/roberta-base-ca-v2-cased-te",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:05:25+00:00
|
text-classification
|
transformers
|
<!-- 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_mnli_hans_16K
This model is a fine-tuned version of [google-bert/bert-large-cased](https://huggingface.co/google-bert/bert-large-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: 0.2
- train_batch_size: 8
- eval_batch_size: 8
- seed: 8446
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google-bert/bert-large-cased", "model-index": [{"name": "results_mnli_hans_16K", "results": []}]}
|
Elkelouizajo/bert_mnli_hans
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-large-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:06:30+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** Rebecca19990101
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"}
|
Rebecca19990101/Llama3-Petro-Instruct-adapters
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:06:49+00:00
|
null | null |
{"license": "apache-2.0"}
|
momoyukki/CSIT6000Rgroup_embedding
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T11:07:27+00:00
|
|
null | null |
{}
|
Radhika273/distilbert-base-uncased-finetuned-emotion-finetuned-emotion-finetuned-emotion
| null |
[
"region:us"
] | null |
2024-04-25T11:08:00+00:00
|
|
text-classification
|
transformers
|
<!-- 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. -->
# robust_llm_pythia-410m_mz-130_IMDB_n-its-10-seed-1
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-410m", "model-index": [{"name": "robust_llm_pythia-410m_mz-130_IMDB_n-its-10-seed-1", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-410m_mz-130_IMDB_n-its-10-seed-1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:09:26+00:00
|
null | null |
{"license": "llama3"}
|
CustomerInsightsMedicalAnalytics/llama3_training
| null |
[
"safetensors",
"license:llama3",
"region:us"
] | null |
2024-04-25T11:09:54+00:00
|
|
text-generation
|
transformers
|
<!-- 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. -->
# 0.0_ablation_5iters_bs256_useresponse_iter_3
This model is a fine-tuned version of [ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_2](https://huggingface.co/ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_2) on the ZhangShenao/0.0_ablation_5iters_bs256_useresponse_dataset 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["ZhangShenao/0.0_ablation_5iters_bs256_useresponse_dataset"], "base_model": "ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_2", "model-index": [{"name": "0.0_ablation_5iters_bs256_useresponse_iter_3", "results": []}]}
|
ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_3
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:ZhangShenao/0.0_ablation_5iters_bs256_useresponse_dataset",
"base_model:ZhangShenao/0.0_ablation_5iters_bs256_useresponse_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:10:09+00:00
|
text-classification
|
transformers
|
{}
|
tajshvra/distilbert-base-uncased-finetuned-emotion
| null |
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:11:04+00:00
|
|
null | null |
# Slimplus Keto Gummies Erfahrungen Deutschland Test und Einnahme Preis, kaufen
SlimPlus Stoffwechsel Kapseln Deutschland Erfahrungen Slim Plus Keto Gummies wurden mit dem Ziel entwickelt, Menschen beim Abnehmen zu helfen. Apfelessig mit Muttermilch ist die Hauptzutat in diesem Produkt. Das Wort für „hinzufügen“ ist dasselbe wie das Wort für das, was hinzugefügt wird. Zusätzlich zu diesen natürlichen Zutaten enthält die Mischung auch unterschiedliche Mengen an Fruchtölen. Jede dieser fettverbrennenden und gesundheitsfördernden Kaubonbons enthält ein kleines Behältnis mit dem Wirkstoff. Da diese Süßigkeiten Ihr Immunsystem und Ihren Stoffwechsel unterstützen, werden Sie möglicherweise feststellen, dass der Verzehr dieser Süßigkeiten Ihr Leben insgesamt einfacher und unterhaltsamer macht.
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Ketose kann auch ohne die Hilfe von Nahrungsergänzungsmitteln erreicht werden, allerdings ist es nicht so einfach. Wenn Sie eine Keto-Diät durchführen, verpassen Sie in der Regel die Nährstoffe, die Ihr Körper braucht, und selbst dann gibt es keine Garantie dafür, dass die Ketose genauso effektiv ist.
Aber mit Hilfe der Slim Plus Keto ACV Gummies kann die Ketose ganz effizient erreicht werden, und Sie müssen Ihrem Körper nicht die Nährstoffe entziehen, die er täglich benötigt.
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Personen unter 18 Jahren, Personen mit gesundheitlichen Problemen und Personen, die schwanger sind oder in den nächsten zwei Monaten schwanger werden könnten, sollten dieses Produkt nicht bestellen oder verwenden. Daher werden Anfragen nicht angenommen und Rückerstattungen aus diesen Gründen sind nicht möglich. Bevor Sie die Gummies zu medizinischen Zwecken einnehmen, müssen Sie einen Arzt konsultieren.
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Besuchen Sie die offizielle Website, um die Bewertungen von Slim Plus Keto ACV Gummies zu lesen. Sie werden sehen, dass jeder, der dieses Nahrungsergänzungsmittel zur Gewichtsreduktion einnimmt, gesund abnimmt.
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|
{}
|
VKapseln475/SlimplusKeto888
| null |
[
"region:us"
] | null |
2024-04-25T11:12:55+00:00
|
null | null |
{}
|
Radhika273/distilbert-base-uncased-finetuned-emotion-finetuned-emotion-finetuned-emotion-finetuned-emotion
| null |
[
"region:us"
] | null |
2024-04-25T11:14:19+00:00
|
|
null | null |
# OpenELM-GGUF
- Original model: [OpenELM](https://huggingface.co/apple/OpenELM)
<!-- description start -->
## Description
This repo contains GGUF format model files for [OpenELM](https://huggingface.co/apple/OpenELM).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
* [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
* [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
* [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
* [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
* [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents.
<!-- README_GGUF.md-about-gguf end -->
<!-- compatibility_gguf start -->
## 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.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder.
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: LiteLLMs/OpenELM-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.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 LiteLLMs/OpenELM-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download LiteLLMs/OpenELM-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 huggingface_hub[hf_transfer]
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/OpenELM-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Q4_0/Q4_0-00001-of-00009.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(
"<PROMPT>", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: OpenELM
# OpenELM: An Efficient Language Model Family with Open-source Training and Inference Framework
*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari*
We introduce **OpenELM**, a family of **Open**-source **E**fficient **L**anguage **M**odels. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. We pretrained OpenELM models using the [CoreNet](https://github.com/apple/corenet) library. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters.
Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Please check license agreements and terms of these datasets before using them.
See the list below for the details of each model:
- [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M)
- [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M)
- [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B)
- [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B)
- [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct)
- [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct)
- [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct)
- [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct)
```python
from transformers import AutoModelForCausalLM
openelm_270m = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M", trust_remote_code=True)
openelm_450m = AutoModelForCausalLM.from_pretrained("apple/OpenELM-450M", trust_remote_code=True)
openelm_1b = AutoModelForCausalLM.from_pretrained("apple/OpenELM-1_1B", trust_remote_code=True)
openelm_3b = AutoModelForCausalLM.from_pretrained("apple/OpenELM-3B", trust_remote_code=True)
openelm_270m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-270M-Instruct", trust_remote_code=True)
openelm_450m_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-450M-Instruct", trust_remote_code=True)
openelm_1b_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-1_1B-Instruct", trust_remote_code=True)
openelm_3b_instruct = AutoModelForCausalLM.from_pretrained("apple/OpenELM-3B-Instruct", trust_remote_code=True)
```
## Usage
We have provided an example function to generate output from OpenELM models loaded via [HuggingFace Hub](https://huggingface.co/docs/hub/) in `generate_openelm.py`.
You can try the model by running the following command:
```
python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2
```
Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token.
Additional arguments to the hugging face generate function can be passed via `generate_kwargs`. As an example, to speedup the inference, you can try [lookup token speculative generation](https://huggingface.co/docs/transformers/generation_strategies) by passing the `prompt_lookup_num_tokens` argument as follows:
```
python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10
```
Alternatively, try model-wise speculative generation with an [assistive model](https://huggingface.co/blog/assisted-generation) by passing a smaller model through the `assistant_model` argument, for example:
```
python generate_openelm.py --model [MODEL_NAME] --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL_NAME]
```
## Main Results
### Zero-Shot
| **Model Size** | **ARC-c** | **ARC-e** | **BoolQ** | **HellaSwag** | **PIQA** | **SciQ** | **WinoGrande** | **Average** |
| | | - | | -- | | | -- | -- | | | - | | -- |
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 27.65 | **66.79** | 47.15 | 25.72 | 69.75 | 30.91 | **39.24** | **53.83** | 45.13 |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | 66.01 | **51.58** | **26.70** | **70.78** | 33.78 | 38.72 | 53.20 | **46.66** |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 30.20 | **68.63** | 53.86 | **26.01** | 72.31 | 33.11 | 40.18 | 57.22 | 47.69 |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | 67.44 | **59.31** | 25.41 | **72.63** | **36.84** | **40.48** | **58.33** | **49.25** |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 36.69 | **71.74** | 65.71 | **27.05** | **75.57** | 36.46 | 36.98 | 63.22 | 51.68 |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | 71.02 | **71.83** | 25.65 | 75.03 | **39.43** | **45.95** | **64.72** | **54.40** |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 42.24 | **73.29** | 73.28 | **26.76** | 78.24 | **38.76** | 34.98 | 67.25 | 54.35 |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **47.70** | 72.33 | **76.87** | 24.80 | **79.00** | 38.47 | **38.76** | **67.96** | **55.73** |
See the technical report for more results and comparison.
## Evaluation
### Setup
Install the following dependencies:
```bash
# install public lm-eval-harness
harness_repo="public-lm-eval-harness"
git clone https://github.com/EleutherAI/lm-evaluation-harness ${harness_repo}
cd ${harness_repo}
# use main branch on 03-15-2024, SHA is dc90fec
git checkout dc90fec
pip install -e .
cd ..
# 66d6242 is the main branch on 2024-04-01
pip install datasets@git+https://github.com/huggingface/datasets.git@66d6242
pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0
```
### Evaluate OpenELM
```bash
# OpenELM-270M
hf_model=OpenELM-270M
# this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMA tokenizer which requires add_bos_token to be True
tokenizer=meta-llama/Llama-2-7b-hf
add_bos_token=True
batch_size=1
mkdir lm_eval_output
shot=0
task=arc_challenge,arc_easy,boolq,hellaswag,piqa,race,winogrande,sciq,truthfulqa_mc2
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=5
task=mmlu,winogrande
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=25
task=arc_challenge,crows_pairs_english
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=10
task=hellaswag
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
```
## Bias, Risks, and Limitations
The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements.
## Citation
If you find our work useful, please cite:
```BibTex
@article{mehtaOpenELMEfficientLanguage2024,
title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open}-source {Training} and {Inference} {Framework}},
shorttitle = {{OpenELM}},
url = {https://arxiv.org/abs/2404.14619v1},
language = {en},
urldate = {2024-04-24},
journal = {arXiv.org},
author = {Mehta, Sachin and Sekhavat, Mohammad Hossein and Cao, Qingqing and Horton, Maxwell and Jin, Yanzi and Sun, Chenfan and Mirzadeh, Iman and Najibi, Mahyar and Belenko, Dmitry and Zatloukal, Peter and Rastegari, Mohammad},
month = apr,
year = {2024},
}
@inproceedings{mehta2022cvnets,
author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad},
title = {CVNets: High Performance Library for Computer Vision},
year = {2022},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
series = {MM '22}
}
```
<!-- original-model-card end -->
|
{"license": "other", "tags": ["GGUF"], "license_name": "apple-sample-code-license", "license_link": "LICENSE", "quantized_by": "andrijdavid"}
|
LiteLLMs/OpenELM-GGUF
| null |
[
"GGUF",
"arxiv:2404.14619",
"license:other",
"region:us"
] | null |
2024-04-25T11:15:06+00:00
|
null | null |
{}
|
anshu1234/fine-tuning
| null |
[
"region:us"
] | null |
2024-04-25T11:15:36+00:00
|
|
text-classification
|
transformers
|
{}
|
tajshvra/roberta-finetuned-emotion
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:15:50+00:00
|
|
null |
peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0
|
{"library_name": "peft", "base_model": "microsoft/resnet-18"}
|
pintu5057/resnet18-finetuned-lora-food101
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:microsoft/resnet-18",
"region:us"
] | null |
2024-04-25T11:16:10+00:00
|
null | null |
# OpenELM-3B-Instruct-GGUF
- Original model: [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct)
<!-- description start -->
## Description
This repo contains GGUF format model files for [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
* [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
* [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
* [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
* [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
* [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents.
<!-- README_GGUF.md-about-gguf end -->
<!-- compatibility_gguf start -->
## 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.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder.
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: LiteLLMs/OpenELM-3B-Instruct-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.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 LiteLLMs/OpenELM-3B-Instruct-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download LiteLLMs/OpenELM-3B-Instruct-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 huggingface_hub[hf_transfer]
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/OpenELM-3B-Instruct-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./Q4_0/Q4_0-00001-of-00009.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(
"<PROMPT>", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: OpenELM-3B-Instruct
# OpenELM
*Sachin Mehta, Mohammad Hossein Sekhavat, Qingqing Cao, Maxwell Horton, Yanzi Jin, Chenfan Sun, Iman Mirzadeh, Mahyar Najibi, Dmitry Belenko, Peter Zatloukal, Mohammad Rastegari*
We introduce **OpenELM**, a family of **Open**-source **E**fficient **L**anguage **M**odels. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each layer of the transformer model, leading to enhanced accuracy. We pretrained OpenELM models using the [CoreNet](https://github.com/apple/corenet) library. We release both pretrained and instruction tuned models with 270M, 450M, 1.1B and 3B parameters.
Our pre-training dataset contains RefinedWeb, deduplicated PILE, a subset of RedPajama, and a subset of Dolma v1.6, totaling approximately 1.8 trillion tokens. Please check license agreements and terms of these datasets before using them.
## Usage
We have provided an example function to generate output from OpenELM models loaded via [HuggingFace Hub](https://huggingface.co/docs/hub/) in `generate_openelm.py`.
You can try the model by running the following command:
```
python generate_openelm.py --model apple/OpenELM-3B-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2
```
Please refer to [this link](https://huggingface.co/docs/hub/security-tokens) to obtain your hugging face access token.
Additional arguments to the hugging face generate function can be passed via `generate_kwargs`. As an example, to speedup the inference, you can try [lookup token speculative generation](https://huggingface.co/docs/transformers/generation_strategies) by passing the `prompt_lookup_num_tokens` argument as follows:
```
python generate_openelm.py --model apple/OpenELM-3B-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 prompt_lookup_num_tokens=10
```
Alternatively, try model-wise speculative generation with an [assistive model](https://huggingface.co/blog/assisted-generation) by passing a smaller model through the `assistant_model` argument, for example:
```
python generate_openelm.py --model apple/OpenELM-3B-Instruct --hf_access_token [HF_ACCESS_TOKEN] --prompt 'Once upon a time there was' --generate_kwargs repetition_penalty=1.2 --assistant_model [SMALLER_MODEL]
```
## Main Results
### Zero-Shot
| **Model Size** | **ARC-c** | **ARC-e** | **BoolQ** | **HellaSwag** | **PIQA** | **SciQ** | **WinoGrande** | **Average** |
| | | - | | -- | | | -- | -- | | | - | | -- |
| [OpenELM-270M](https://huggingface.co/apple/OpenELM-270M) | 27.65 | **66.79** | 47.15 | 25.72 | 69.75 | 30.91 | **39.24** | **53.83** | 45.13 |
| [OpenELM-270M-Instruct](https://huggingface.co/apple/OpenELM-270M-Instruct) | **32.51** | 66.01 | **51.58** | **26.70** | **70.78** | 33.78 | 38.72 | 53.20 | **46.66** |
| [OpenELM-450M](https://huggingface.co/apple/OpenELM-450M) | 30.20 | **68.63** | 53.86 | **26.01** | 72.31 | 33.11 | 40.18 | 57.22 | 47.69 |
| [OpenELM-450M-Instruct](https://huggingface.co/apple/OpenELM-450M-Instruct) | **33.53** | 67.44 | **59.31** | 25.41 | **72.63** | **36.84** | **40.48** | **58.33** | **49.25** |
| [OpenELM-1_1B](https://huggingface.co/apple/OpenELM-1_1B) | 36.69 | **71.74** | 65.71 | **27.05** | **75.57** | 36.46 | 36.98 | 63.22 | 51.68 |
| [OpenELM-1_1B-Instruct](https://huggingface.co/apple/OpenELM-1_1B-Instruct) | **41.55** | 71.02 | **71.83** | 25.65 | 75.03 | **39.43** | **45.95** | **64.72** | **54.40** |
| [OpenELM-3B](https://huggingface.co/apple/OpenELM-3B) | 42.24 | **73.29** | 73.28 | **26.76** | 78.24 | **38.76** | 34.98 | 67.25 | 54.35 |
| [OpenELM-3B-Instruct](https://huggingface.co/apple/OpenELM-3B-Instruct) | **47.70** | 72.33 | **76.87** | 24.80 | **79.00** | 38.47 | **38.76** | **67.96** | **55.73** |
See the technical report for more results and comparison.
## Evaluation
### Setup
Install the following dependencies:
```bash
# install public lm-eval-harness
harness_repo="public-lm-eval-harness"
git clone https://github.com/EleutherAI/lm-evaluation-harness ${harness_repo}
cd ${harness_repo}
# use main branch on 03-15-2024, SHA is dc90fec
git checkout dc90fec
pip install -e .
cd ..
# 66d6242 is the main branch on 2024-04-01
pip install datasets@git+https://github.com/huggingface/datasets.git@66d6242
pip install tokenizers>=0.15.2 transformers>=4.38.2 sentencepiece>=0.2.0
```
### Evaluate OpenELM
```bash
# OpenELM-3B-Instruct
hf_model=OpenELM-3B-Instruct
# this flag is needed because lm-eval-harness set add_bos_token to False by default, but OpenELM uses LLaMA tokenizer which requires add_bos_token to be True
tokenizer=meta-llama/Llama-2-7b-hf
add_bos_token=True
batch_size=1
mkdir lm_eval_output
shot=0
task=arc_challenge,arc_easy,boolq,hellaswag,piqa,race,winogrande,sciq,truthfulqa_mc2
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=5
task=mmlu,winogrande
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=25
task=arc_challenge,crows_pairs_english
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
shot=10
task=hellaswag
lm_eval --model hf \
--model_args pretrained=${hf_model},trust_remote_code=True,add_bos_token=${add_bos_token},tokenizer=${tokenizer} \
--tasks ${task} \
--device cuda:0 \
--num_fewshot ${shot} \
--output_path ./lm_eval_output/${hf_model//\//_}_${task//,/_}-${shot}shot \
--batch_size ${batch_size} 2>&1 | tee ./lm_eval_output/eval-${hf_model//\//_}_${task//,/_}-${shot}shot.log
```
## Bias, Risks, and Limitations
The release of OpenELM models aims to empower and enrich the open research community by providing access to state-of-the-art language models. Trained on publicly available datasets, these models are made available without any safety guarantees. Consequently, there exists the possibility of these models producing outputs that are inaccurate, harmful, biased, or objectionable in response to user prompts. Thus, it is imperative for users and developers to undertake thorough safety testing and implement appropriate filtering mechanisms tailored to their specific requirements.
## Citation
If you find our work useful, please cite:
```BibTex
@article{mehtaOpenELMEfficientLanguage2024,
title = {{OpenELM}: {An} {Efficient} {Language} {Model} {Family} with {Open}-source {Training} and {Inference} {Framework}},
shorttitle = {{OpenELM}},
url = {https://arxiv.org/abs/2404.14619v1},
language = {en},
urldate = {2024-04-24},
journal = {arXiv.org},
author = {Mehta, Sachin and Sekhavat, Mohammad Hossein and Cao, Qingqing and Horton, Maxwell and Jin, Yanzi and Sun, Chenfan and Mirzadeh, Iman and Najibi, Mahyar and Belenko, Dmitry and Zatloukal, Peter and Rastegari, Mohammad},
month = apr,
year = {2024},
}
@inproceedings{mehta2022cvnets,
author = {Mehta, Sachin and Abdolhosseini, Farzad and Rastegari, Mohammad},
title = {CVNets: High Performance Library for Computer Vision},
year = {2022},
booktitle = {Proceedings of the 30th ACM International Conference on Multimedia},
series = {MM '22}
}
```
<!-- original-model-card end -->
|
{"license": "other", "tags": ["GGUF"], "license_name": "apple-sample-code-license", "license_link": "LICENSE", "quantized_by": "andrijdavid"}
|
LiteLLMs/OpenELM-3B-Instruct-GGUF
| null |
[
"GGUF",
"arxiv:2404.14619",
"license:other",
"region:us"
] | null |
2024-04-25T11:16:16+00:00
|
image-classification
|
transformers
|
<!-- 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. -->
# Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold3
This model is a fine-tuned version of [microsoft/swin-base-patch4-window7-224-in22k](https://huggingface.co/microsoft/swin-base-patch4-window7-224-in22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1325
- Accuracy: 0.6741
## 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_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1101 | 1.0 | 923 | 1.1258 | 0.6189 |
| 1.0093 | 2.0 | 1846 | 1.0135 | 0.6527 |
| 0.9375 | 3.0 | 2769 | 0.9810 | 0.6678 |
| 0.7383 | 4.0 | 3692 | 0.9381 | 0.6824 |
| 0.5544 | 5.0 | 4615 | 1.0054 | 0.6762 |
| 0.3667 | 6.0 | 5538 | 1.0182 | 0.6746 |
| 0.4307 | 7.0 | 6461 | 1.0606 | 0.6754 |
| 0.3187 | 8.0 | 7384 | 1.1112 | 0.6746 |
| 0.3138 | 9.0 | 8307 | 1.1223 | 0.6787 |
| 0.3019 | 10.0 | 9230 | 1.1325 | 0.6741 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-base-patch4-window7-224-in22k", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold3", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6740600486881255, "name": "Accuracy"}]}]}]}
|
onizukal/Boya1_RMSProp_1-e5_10Epoch_swin-base-window7-224-in22k_fold3
| null |
[
"transformers",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-base-patch4-window7-224-in22k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:17:18+00:00
|
null | null |
{"license": "apache-2.0"}
|
Crystal427/Cryst_Su_SDXLtrainingCKPT
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T11:17:32+00:00
|
|
text-classification
|
transformers
|
<!-- 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. -->
# 2504separado4
This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7346
- Accuracy: 0.8403
- Precision: 0.8451
- Recall: 0.8403
- F1: 0.8398
- Ratio: 0.5588
## 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: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 4
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.3339 | 0.9870 | 38 | 0.8160 | 0.8151 | 0.8243 | 0.8151 | 0.8138 | 0.5840 |
| 0.324 | 2.0 | 77 | 0.7346 | 0.8403 | 0.8451 | 0.8403 | 0.8398 | 0.5588 |
| 0.3548 | 2.9870 | 115 | 0.7188 | 0.8319 | 0.8343 | 0.8319 | 0.8316 | 0.5420 |
| 0.3957 | 3.9481 | 152 | 0.6996 | 0.8361 | 0.8367 | 0.8361 | 0.8361 | 0.5210 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "projecte-aina/roberta-base-ca-v2-cased-te", "model-index": [{"name": "2504separado4", "results": []}]}
|
adriansanz/2504separado4
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:projecte-aina/roberta-base-ca-v2-cased-te",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:17:42+00:00
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABCZ_22_992_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:18:13+00:00
|
|
null | null |
{}
|
Radhika273/finetunedemotionmodel
| null |
[
"region:us"
] | null |
2024-04-25T11:18:14+00:00
|
|
null |
diffusers
|
{}
|
RonenWeiz/encdec_debug_model_ide
| null |
[
"diffusers",
"safetensors",
"diffusers:StableDiffusionInstructPix2PixPipeline",
"region:us"
] | null |
2024-04-25T11:18:59+00:00
|
|
null | null |
{}
|
sumithanwate/mt5-small-finetuned-amazon-en-es
| null |
[
"region:us"
] | null |
2024-04-25T11:19:25+00:00
|
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
PakinClean/git-large-coco-food
| null |
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:20:11+00:00
|
null |
peft
|
<!-- 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. -->
# Meta-Llama-3-8B-Instruct
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- 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
- training_steps: 800
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
{"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Meta-Llama-3-8B-Instruct", "results": []}]}
|
CustomerInsightsMedicalAnalytics/llama3_training_files
| null |
[
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null |
2024-04-25T11:20:12+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_SAPOL_v1_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SAPOL_v1_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_SAPOL_v1_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:20:12+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# robust_llm_pythia-410m_mz-130_IMDB_n-its-10-seed-3
This model is a fine-tuned version of [EleutherAI/pythia-410m](https://huggingface.co/EleutherAI/pythia-410m) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-410m", "model-index": [{"name": "robust_llm_pythia-410m_mz-130_IMDB_n-its-10-seed-3", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-410m_mz-130_IMDB_n-its-10-seed-3
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-410m",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:21:13+00:00
|
null | null |
{}
|
MX4T/animecgxl
| null |
[
"region:us"
] | null |
2024-04-25T11:22:05+00:00
|
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/x2bee/POLAR-14B-v0.1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q2_K.gguf) | Q2_K | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.IQ3_XS.gguf) | IQ3_XS | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q3_K_S.gguf) | Q3_K_S | 6.3 | |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.IQ3_S.gguf) | IQ3_S | 6.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.IQ3_M.gguf) | IQ3_M | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q3_K_M.gguf) | Q3_K_M | 7.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q3_K_L.gguf) | Q3_K_L | 7.6 | |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.IQ4_XS.gguf) | IQ4_XS | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q4_K_S.gguf) | Q4_K_S | 8.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q4_K_M.gguf) | Q4_K_M | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q5_K_S.gguf) | Q5_K_S | 9.9 | |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q5_K_M.gguf) | Q5_K_M | 10.2 | |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q6_K.gguf) | Q6_K | 11.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/POLAR-14B-v0.1-GGUF/resolve/main/POLAR-14B-v0.1.Q8_0.gguf) | Q8_0 | 15.2 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
{"language": ["en"], "license": "cc-by-nc-3.0", "library_name": "transformers", "base_model": "x2bee/POLAR-14B-v0.1", "quantized_by": "mradermacher"}
|
mradermacher/POLAR-14B-v0.1-GGUF
| null |
[
"transformers",
"gguf",
"en",
"base_model:x2bee/POLAR-14B-v0.1",
"license:cc-by-nc-3.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:22:12+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Hastagaras/L3-Pilter-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/L3-Pilter-8B-GGUF/resolve/main/L3-Pilter-8B.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
{"language": ["en"], "library_name": "transformers", "base_model": "Hastagaras/L3-Pilter-8B", "quantized_by": "mradermacher"}
|
mradermacher/L3-Pilter-8B-GGUF
| null |
[
"transformers",
"gguf",
"en",
"base_model:Hastagaras/L3-Pilter-8B",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:23:27+00:00
|
text2text-generation
|
transformers
|
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_SAPOL_v2_h1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SAPOL_v2_h1", "results": []}]}
|
ThuyNT/CS505_COQE_viT5_train_Instruction0_SAPOL_v2_h1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:23:34+00:00
|
text-generation
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
DevsDoCode/LLama-3-8b-Uncensored
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:23:40+00:00
|
null | null |
{}
|
selvaa/segformer-b5-finetuned-cityscapes-1024-1024-full-ds
| null |
[
"region:us"
] | null |
2024-04-25T11:24:33+00:00
|
|
text-generation
|
transformers
|
{}
|
BlingDan/qlora-baichuan2-7b-chat
| null |
[
"transformers",
"pytorch",
"baichuan",
"text-generation",
"custom_code",
"autotrain_compatible",
"region:us"
] | null |
2024-04-25T11:24:52+00:00
|
|
null | null |
{}
|
lemousehunter/bge-reranker-large
| null |
[
"region:us"
] | null |
2024-04-25T11:26:00+00:00
|
|
null | null |
{}
|
lemousehunter/bge-m3
| null |
[
"region:us"
] | null |
2024-04-25T11:26:01+00:00
|
|
text-generation
|
transformers
|
<!-- 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. -->
# 0.001_ablation_5iters_bs256_useresponse_iter_3
This model is a fine-tuned version of [ShenaoZ/0.001_ablation_5iters_bs256_useresponse_iter_2](https://huggingface.co/ShenaoZ/0.001_ablation_5iters_bs256_useresponse_iter_2) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.001_ablation_5iters_bs256_useresponse_iter_2", "model-index": [{"name": "0.001_ablation_5iters_bs256_useresponse_iter_3", "results": []}]}
|
ShenaoZ/0.001_ablation_5iters_bs256_useresponse_iter_3
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.001_ablation_5iters_bs256_useresponse_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:26:59+00:00
|
null | null |
{"license": "llama3"}
|
czxca/llama
| null |
[
"license:llama3",
"region:us"
] | null |
2024-04-25T11:27:25+00:00
|
|
feature-extraction
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
steve1989/internLM-7b-gptq-4bit
| null |
[
"transformers",
"safetensors",
"internlm2",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"4-bit",
"region:us"
] | null |
2024-04-25T11:27:46+00:00
|
text-classification
|
transformers
|
<!-- 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. -->
# 2504separado5
This model is a fine-tuned version of [projecte-aina/roberta-base-ca-v2-cased-te](https://huggingface.co/projecte-aina/roberta-base-ca-v2-cased-te) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6571
- Accuracy: 0.8487
- Precision: 0.8491
- Recall: 0.8487
- F1: 0.8487
- Ratio: 0.5168
## 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: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 4
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 | Ratio |
|:-------------:|:------:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|:------:|
| 0.3101 | 0.9870 | 38 | 0.7275 | 0.8445 | 0.8465 | 0.8445 | 0.8443 | 0.4622 |
| 0.3189 | 2.0 | 77 | 0.7399 | 0.8445 | 0.8448 | 0.8445 | 0.8445 | 0.5126 |
| 0.3786 | 2.9870 | 115 | 0.7200 | 0.8361 | 0.8390 | 0.8361 | 0.8358 | 0.5462 |
| 0.3816 | 3.9481 | 152 | 0.6571 | 0.8487 | 0.8491 | 0.8487 | 0.8487 | 0.5168 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall", "f1"], "base_model": "projecte-aina/roberta-base-ca-v2-cased-te", "model-index": [{"name": "2504separado5", "results": []}]}
|
adriansanz/2504separado5
| null |
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:projecte-aina/roberta-base-ca-v2-cased-te",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:28:02+00:00
|
null | null |
{"license": "mit"}
|
phoen1x/federated-legal-summarisation
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-25T11:28:14+00:00
|
|
text-generation
|
transformers
|
See details: https://github.com/AndrewZhe/lawyer-llama
|
{"language": ["zh"], "license": "llama2"}
|
pkupie/lawyer-llama-13b-v2
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"zh",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:29:10+00:00
|
null |
peft
|
<!-- 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. -->
# llama3-orchamath-lora
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "llama3-orchamath-lora", "results": []}]}
|
fangzhaoz/llama3-orchamath-lora
| null |
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null |
2024-04-25T11:29:17+00:00
|
video-classification
|
transformers
|
<!-- 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. -->
# videomae-base-finetuned-ucf101-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4454
- Accuracy: 0.8462
## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 148
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.0504 | 0.2568 | 38 | 0.9655 | 0.7286 |
| 0.5387 | 1.2568 | 76 | 0.5637 | 0.7571 |
| 0.2298 | 2.2568 | 114 | 0.4616 | 0.8286 |
| 0.13 | 3.2297 | 148 | 0.4940 | 0.8429 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "MCG-NJU/videomae-base", "model-index": [{"name": "videomae-base-finetuned-ucf101-subset", "results": []}]}
|
Nikeytas/videomae-base-finetuned-ucf101-subset
| null |
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:29:27+00:00
|
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Mistral-7B-Instruct-v0.2-absa-laptops
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0235
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1844 | 0.36 | 40 | 0.1770 |
| 0.0756 | 0.72 | 80 | 0.0313 |
| 0.0262 | 1.08 | 120 | 0.0263 |
| 0.0203 | 1.44 | 160 | 0.0250 |
| 0.0194 | 1.8 | 200 | 0.0235 |
| 0.0159 | 2.16 | 240 | 0.0245 |
| 0.0132 | 2.52 | 280 | 0.0229 |
| 0.0131 | 2.88 | 320 | 0.0228 |
| 0.0105 | 3.24 | 360 | 0.0228 |
| 0.0097 | 3.6 | 400 | 0.0235 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-absa-laptops", "results": []}]}
|
Shakhovak/Mistral-7B-Instruct-v0.2-absa-laptops
| null |
[
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T11:29:41+00:00
|
null | null |
{}
|
fangzhaoz/llama3-orchamath-lora_merged
| null |
[
"region:us"
] | null |
2024-04-25T11:29:43+00:00
|
|
reinforcement-learning
|
stable-baselines3
|
# **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
...
```
|
{"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": "247.08 +/- 50.12", "name": "mean_reward", "verified": false}]}]}]}
|
abdullahcavuss/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T11:29:49+00:00
|
automatic-speech-recognition
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
suke0327/whisper-large_odd_en
| null |
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:30:00+00:00
|
null |
peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
{"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B"}
|
yiyic/llama3-8b-lora-clf-0
| null |
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B",
"region:us"
] | null |
2024-04-25T11:30:10+00:00
|
null | null |
{}
|
dharma03/mixtral
| null |
[
"region:us"
] | null |
2024-04-25T11:31:17+00:00
|
|
null |
transformers
|
{"license": "other", "license_name": "open", "license_link": "LICENSE"}
|
tutuhu/shanshui2
| null |
[
"transformers",
"safetensors",
"license:other",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:32:06+00:00
|
|
null |
transformers
|
# Uploaded model
- **Developed by:** xiaoliy2
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-2-7b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-2-7b-bnb-4bit"}
|
xiaoliy2/llama-2-7b-ft-model-1
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-2-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:32:23+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** YavuzAkbay
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "datasets": ["TIGER-Lab/MathInstruct", "ArtifactAI/arxiv-math-instruct-50k"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"}
|
YavuzAkbay/experiment0.2
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"dataset:TIGER-Lab/MathInstruct",
"dataset:ArtifactAI/arxiv-math-instruct-50k",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:32:44+00:00
|
null |
transformers
|
{"license": "other", "license_name": "open", "license_link": "LICENSE"}
|
tutuhu/shanshui3
| null |
[
"transformers",
"safetensors",
"license:other",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:33:00+00:00
|
|
null |
peft
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-nli_cot
This model is a fine-tuned version of [TheBloke/Mistral-7B-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4930
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.4947 | 0.9996 | 598 | 0.4534 |
| 0.4418 | 1.9992 | 1196 | 0.4475 |
| 0.4262 | 2.9987 | 1794 | 0.4476 |
| 0.4125 | 4.0 | 2393 | 0.4499 |
| 0.4015 | 4.9996 | 2991 | 0.4552 |
| 0.3908 | 5.9992 | 3589 | 0.4591 |
| 0.3809 | 6.9987 | 4187 | 0.4653 |
| 0.3712 | 8.0 | 4786 | 0.4721 |
| 0.3635 | 8.9996 | 5384 | 0.4783 |
| 0.3562 | 9.9992 | 5982 | 0.4868 |
| 0.3496 | 10.9954 | 6578 | 0.4930 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.0.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-v0.1-GPTQ", "model-index": [{"name": "mistral-7b-nli_cot", "results": []}]}
|
jd0g/Mistral-7B-NLI-v0.1
| null |
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T11:33:04+00:00
|
null | null |
{}
|
Ronny242/Microsoft-phi-3
| null |
[
"gguf",
"region:us"
] | null |
2024-04-25T11:33:41+00:00
|
|
null |
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
gagan-zykrr/quantized
| null |
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:33:56+00:00
|
text-generation
|
transformers
|
<!-- 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. -->
# 0.001_ablation_4iters_bs256_nodpo_useresponse_iter_2
This model is a fine-tuned version of [ShenaoZ/0.001_ablation_4iters_bs256_nodpo_useresponse_iter_1](https://huggingface.co/ShenaoZ/0.001_ablation_4iters_bs256_nodpo_useresponse_iter_1) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.001_ablation_4iters_bs256_nodpo_useresponse_iter_1", "model-index": [{"name": "0.001_ablation_4iters_bs256_nodpo_useresponse_iter_2", "results": []}]}
|
ShenaoZ/0.001_ablation_4iters_bs256_nodpo_useresponse_iter_2
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.001_ablation_4iters_bs256_nodpo_useresponse_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:34:56+00:00
|
text-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3484
- Accuracy: 0.8995
- F1: 0.8970
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.5448 | 0.8235 | 0.8027 |
| 0.743 | 2.0 | 250 | 0.3484 | 0.8995 | 0.8970 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.8995, "name": "Accuracy"}, {"type": "f1", "value": 0.8970280250922354, "name": "F1"}]}]}]}
|
polyatree/distilbert-base-uncased-finetuned-emotion
| null |
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:37:59+00:00
|
null |
peft
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-nli_cot
This model is a fine-tuned version of [TheBloke/Mistral-7B-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4930
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.4947 | 0.9996 | 598 | 0.4534 |
| 0.4418 | 1.9992 | 1196 | 0.4475 |
| 0.4262 | 2.9987 | 1794 | 0.4476 |
| 0.4125 | 4.0 | 2393 | 0.4499 |
| 0.4015 | 4.9996 | 2991 | 0.4552 |
| 0.3908 | 5.9992 | 3589 | 0.4591 |
| 0.3809 | 6.9987 | 4187 | 0.4653 |
| 0.3712 | 8.0 | 4786 | 0.4721 |
| 0.3635 | 8.9996 | 5384 | 0.4783 |
| 0.3562 | 9.9992 | 5982 | 0.4868 |
| 0.3496 | 10.9954 | 6578 | 0.4930 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.0.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-v0.1-GPTQ", "model-index": [{"name": "mistral-7b-nli_cot", "results": []}]}
|
jd0g/mistral-7b-nli_cot
| null |
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T11:38:01+00:00
|
null | null |
See details: https://github.com/AndrewZhe/lawyer-llama
|
{"language": ["zh"], "license": "apache-2.0"}
|
pkupie/marriage_law_retrieval
| null |
[
"zh",
"license:apache-2.0",
"region:us"
] | null |
2024-04-25T11:38:52+00:00
|
null | null |
{"license": "mit"}
|
Primeness/prime2
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-25T11:39:13+00:00
|
|
null |
peft
|
<!-- 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. -->
# RM-HH-GPT2Large_helpful_gpt3_loraR64_40000_gpt2-large_shuffleTrue_extractchosenTrue
This model is a fine-tuned version of [openai-community/gpt2-large](https://huggingface.co/openai-community/gpt2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4911
- Accuracy: 0.7362
## 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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- 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: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8065 | 0.02 | 250 | 0.7702 | 0.4763 |
| 0.7485 | 0.04 | 500 | 0.6903 | 0.5578 |
| 0.6625 | 0.06 | 750 | 0.6116 | 0.6516 |
| 0.5815 | 0.08 | 1000 | 0.5742 | 0.6817 |
| 0.5657 | 0.1 | 1250 | 0.5565 | 0.6940 |
| 0.5608 | 0.13 | 1500 | 0.5479 | 0.7015 |
| 0.5611 | 0.15 | 1750 | 0.5418 | 0.7083 |
| 0.5342 | 0.17 | 2000 | 0.5386 | 0.7105 |
| 0.5842 | 0.19 | 2250 | 0.5319 | 0.7124 |
| 0.5096 | 0.21 | 2500 | 0.5293 | 0.7171 |
| 0.5234 | 0.23 | 2750 | 0.5258 | 0.7173 |
| 0.5321 | 0.25 | 3000 | 0.5243 | 0.7202 |
| 0.5295 | 0.27 | 3250 | 0.5212 | 0.7202 |
| 0.5211 | 0.29 | 3500 | 0.5220 | 0.7200 |
| 0.5119 | 0.31 | 3750 | 0.5215 | 0.7205 |
| 0.509 | 0.33 | 4000 | 0.5200 | 0.7226 |
| 0.5393 | 0.36 | 4250 | 0.5155 | 0.7230 |
| 0.5343 | 0.38 | 4500 | 0.5143 | 0.7267 |
| 0.4944 | 0.4 | 4750 | 0.5195 | 0.7277 |
| 0.5198 | 0.42 | 5000 | 0.5188 | 0.7258 |
| 0.523 | 0.44 | 5250 | 0.5206 | 0.7282 |
| 0.53 | 0.46 | 5500 | 0.5082 | 0.7264 |
| 0.5107 | 0.48 | 5750 | 0.5103 | 0.7307 |
| 0.502 | 0.5 | 6000 | 0.5163 | 0.7284 |
| 0.5198 | 0.52 | 6250 | 0.5132 | 0.7305 |
| 0.5879 | 0.54 | 6500 | 0.5067 | 0.7313 |
| 0.5174 | 0.57 | 6750 | 0.5061 | 0.7311 |
| 0.5062 | 0.59 | 7000 | 0.5053 | 0.7298 |
| 0.5265 | 0.61 | 7250 | 0.5064 | 0.7303 |
| 0.5043 | 0.63 | 7500 | 0.5096 | 0.7309 |
| 0.5291 | 0.65 | 7750 | 0.5073 | 0.7299 |
| 0.4966 | 0.67 | 8000 | 0.5141 | 0.7305 |
| 0.5361 | 0.69 | 8250 | 0.5086 | 0.7288 |
| 0.534 | 0.71 | 8500 | 0.5051 | 0.7288 |
| 0.5073 | 0.73 | 8750 | 0.5104 | 0.7286 |
| 0.5155 | 0.75 | 9000 | 0.5138 | 0.7290 |
| 0.5041 | 0.77 | 9250 | 0.5149 | 0.7294 |
| 0.5552 | 0.8 | 9500 | 0.5030 | 0.7288 |
| 0.5177 | 0.82 | 9750 | 0.4995 | 0.7294 |
| 0.4882 | 0.84 | 10000 | 0.5007 | 0.7337 |
| 0.5409 | 0.86 | 10250 | 0.4992 | 0.7320 |
| 0.5044 | 0.88 | 10500 | 0.4994 | 0.7311 |
| 0.4897 | 0.9 | 10750 | 0.5013 | 0.7322 |
| 0.509 | 0.92 | 11000 | 0.4999 | 0.7331 |
| 0.5256 | 0.94 | 11250 | 0.4950 | 0.7360 |
| 0.4976 | 0.96 | 11500 | 0.4937 | 0.7356 |
| 0.5033 | 0.98 | 11750 | 0.4952 | 0.7358 |
| 0.4917 | 1.0 | 12000 | 0.4939 | 0.7333 |
| 0.4615 | 1.03 | 12250 | 0.5005 | 0.7328 |
| 0.4797 | 1.05 | 12500 | 0.4981 | 0.7347 |
| 0.4872 | 1.07 | 12750 | 0.4997 | 0.7362 |
| 0.5106 | 1.09 | 13000 | 0.5012 | 0.7343 |
| 0.482 | 1.11 | 13250 | 0.5021 | 0.7365 |
| 0.4916 | 1.13 | 13500 | 0.4946 | 0.7367 |
| 0.4957 | 1.15 | 13750 | 0.4972 | 0.7379 |
| 0.4822 | 1.17 | 14000 | 0.5072 | 0.7379 |
| 0.4911 | 1.19 | 14250 | 0.5080 | 0.7343 |
| 0.5042 | 1.21 | 14500 | 0.5148 | 0.7343 |
| 0.4966 | 1.23 | 14750 | 0.5055 | 0.7350 |
| 0.527 | 1.26 | 15000 | 0.4945 | 0.7345 |
| 0.4544 | 1.28 | 15250 | 0.5070 | 0.7354 |
| 0.5198 | 1.3 | 15500 | 0.4993 | 0.7335 |
| 0.5138 | 1.32 | 15750 | 0.4958 | 0.7358 |
| 0.5324 | 1.34 | 16000 | 0.4917 | 0.7348 |
| 0.4695 | 1.36 | 16250 | 0.4951 | 0.7347 |
| 0.5016 | 1.38 | 16500 | 0.4938 | 0.7360 |
| 0.478 | 1.4 | 16750 | 0.4969 | 0.7345 |
| 0.4955 | 1.42 | 17000 | 0.4958 | 0.7345 |
| 0.5072 | 1.44 | 17250 | 0.4908 | 0.7341 |
| 0.4764 | 1.46 | 17500 | 0.4957 | 0.7345 |
| 0.5096 | 1.49 | 17750 | 0.4928 | 0.7347 |
| 0.4944 | 1.51 | 18000 | 0.4923 | 0.7331 |
| 0.4766 | 1.53 | 18250 | 0.4931 | 0.7333 |
| 0.515 | 1.55 | 18500 | 0.4897 | 0.7339 |
| 0.4672 | 1.57 | 18750 | 0.4920 | 0.7348 |
| 0.5122 | 1.59 | 19000 | 0.4921 | 0.7337 |
| 0.5395 | 1.61 | 19250 | 0.4899 | 0.7333 |
| 0.5088 | 1.63 | 19500 | 0.4892 | 0.7326 |
| 0.4864 | 1.65 | 19750 | 0.4895 | 0.7358 |
| 0.4605 | 1.67 | 20000 | 0.4968 | 0.7358 |
| 0.5165 | 1.7 | 20250 | 0.4940 | 0.7354 |
| 0.4955 | 1.72 | 20500 | 0.4919 | 0.7348 |
| 0.4923 | 1.74 | 20750 | 0.4906 | 0.7348 |
| 0.5121 | 1.76 | 21000 | 0.4905 | 0.7337 |
| 0.5068 | 1.78 | 21250 | 0.4892 | 0.7356 |
| 0.4767 | 1.8 | 21500 | 0.4900 | 0.7350 |
| 0.4976 | 1.82 | 21750 | 0.4904 | 0.7354 |
| 0.4934 | 1.84 | 22000 | 0.4893 | 0.7356 |
| 0.479 | 1.86 | 22250 | 0.4905 | 0.7352 |
| 0.4698 | 1.88 | 22500 | 0.4909 | 0.7347 |
| 0.4894 | 1.9 | 22750 | 0.4907 | 0.7352 |
| 0.509 | 1.93 | 23000 | 0.4907 | 0.7354 |
| 0.4805 | 1.95 | 23250 | 0.4914 | 0.7350 |
| 0.5152 | 1.97 | 23500 | 0.4911 | 0.7358 |
| 0.4935 | 1.99 | 23750 | 0.4911 | 0.7362 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "library_name": "peft", "tags": ["trl", "reward-trainer", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "openai-community/gpt2-large", "model-index": [{"name": "RM-HH-GPT2Large_helpful_gpt3_loraR64_40000_gpt2-large_shuffleTrue_extractchosenTrue", "results": []}]}
|
Holarissun/RM-HH-GPT2Large_helpful_gpt3_loraR64_40000_gpt2-large_shuffleTrue_extractchosenTrue
| null |
[
"peft",
"safetensors",
"trl",
"reward-trainer",
"generated_from_trainer",
"base_model:openai-community/gpt2-large",
"license:mit",
"region:us"
] | null |
2024-04-25T11:43:22+00:00
|
null | null |
{}
|
Cosrreim/Piuuk
| null |
[
"region:us"
] | null |
2024-04-25T11:44:39+00:00
|
|
text-generation
| null |
# Suzume-llama3-8b-multilingual-GGUF
- This is quantized version of [lightblue/suzume-llama-3-8B-multilingual](https://huggingface.co/lightblue/suzume-llama-3-8B-multilingual) created using llama.cpp
# Model Description
This Suzume 8B, a multilingual finetune of Llama 3 ([meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)).
Llama 3 has exhibited excellent performance on many English language benchmarks.
However, it also seemingly been finetuned on mostly English data, meaning that it will respond in English, even if prompted in other languages.
We have fine-tuned Llama 3 on almost 90,000 multilingual conversations meaning that this model has the smarts of Llama 3 but has the added ability to chat in more languages.
Please feel free to comment on this model and give us feedback in the Community tab!
We will release a paper in the future describing how we made the training data, the model, and the evaluations we have conducted of it.
# Evaluation scores
We achieve the following MT-Bench scores across 6 languages:
| | **meta-llama/Meta-Llama-3-8B-Instruct** | **lightblue/suzume-llama-3-8B-multilingual** | **Nexusflow/Starling-LM-7B-beta** | **gpt-3.5-turbo** |
|-----------------|-----------------------------------------|----------------------------------------------|-----------------------------------|-------------------|
| **German** 🇩🇪 | NaN | 7.26 | 6.99 | 7.68 |
| **French** 🇫🇷 | NaN | 7.66 | 7.29 | 7.74 |
| **Japanese** 🇯🇵 | NaN | 6.56 | 6.22 | 7.84 |
| **Russian** 🇷🇺 * | NaN | 8.19 | 8.28 | 7.94 |
| **Chinese** 🇨🇳 | NaN | 7.11 | 6.97 | 7.55 |
| **English** 🇺🇸 | 7.98 | 7.73 | 7.92 | 8.26 |
\* (Note the Russian scores exclude code, reasoning and math problems due to not having any translated reference answers for these questions.)
We observe minimal degredation of Llama 3's English ability while achieving best-in-class multilingual abilities compared to the top rated 7B model ([Nexusflow/Starling-LM-7B-beta](https://huggingface.co/Nexusflow/Starling-LM-7B-beta)) on the [Chatbot Arena Leaderboard](https://chat.lmsys.org/?leaderboard).
[Here is our evaluation script.](https://drive.google.com/file/d/15HPn7452t8LbTD9HKSl7ngYYWnsoOG08/view?usp=sharing)
# Training data
We train on three sources of data to create this model:
* [lightblue/tagengo-gpt4](https://huggingface.co/datasets/lightblue/tagengo-gpt4) - 76,338 conversations
* A diverse dataset of initial inputs sampled from [lmsys/lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) and then used to prompt `gpt-4-0125-preview`
* [megagonlabs/instruction_ja](https://github.com/megagonlabs/instruction_ja) - 669 conversations
* A hand-edited dataset of nearly 700 Japanese conversations taken originally from translations of the [kunishou/hh-rlhf-49k-ja](https://huggingface.co/datasets/kunishou/hh-rlhf-49k-ja) dataset.
* [openchat/openchat_sharegpt4_dataset](https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/resolve/main/sharegpt_gpt4.json) - 6,206 conversations
* Multilingual conversations of humans talking to GPT-4.
<details><summary>We prepare our data like so:</summary>
```python
import pandas as pd
from datasets import Dataset, load_dataset, concatenate_datasets
### Tagengo
gpt4_dataset = load_dataset("lightblue/tagengo-gpt4", split="train")
gpt4_dataset = gpt4_dataset.filter(lambda x: x["response"][1] == "stop")
####
### Megagon
megagon_df = pd.read_json(
"https://raw.githubusercontent.com/megagonlabs/instruction_ja/main/data/data.jsonl",
lines=True,
orient="records"
)
role_map = {"user": "human", "agent": "gpt"}
megagon_df["conversations"] = megagon_df.utterances.apply(lambda x: [{"from": role_map[y["name"]], "value": y["text"]} for y in x])
megagon_df["language"] = "Japanese"
megagon_df = megagon_df[["conversations", "language"]]
megagon_dataset = Dataset.from_pandas(df)
###
### Openchat
openchat_df = pd.read_json("https://huggingface.co/datasets/openchat/openchat_sharegpt4_dataset/resolve/main/sharegpt_gpt4.json?download=true")
openchat_df["conversations"] = openchat_df["items"]
openchat_dataset = Dataset.from_pandas(openchat_df)
###
dataset = concatenate_datasets([gpt4_dataset, megagon_dataset, openchat_dataset])
dataset = dataset.filter(lambda x: not any([y["value"] is None for y in x["conversations"]]))
dataset.select_columns(["conversations"]).to_json("/workspace/llm_training/axolotl/llama3-multilingual/tagengo_openchat_megagon.json")
```
</details>
<br/>
# workspace/llm_training/axolotl/llama3-multilingual/output_tagengo_openchat_megagon_8B_llama3
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the above described dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6595
## Training procedure
<!-- 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. -->
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer # PreTrainedTokenizerFast
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: /workspace/llm_training/axolotl/llama3-multilingual/tagengo_openchat_megagon.json
ds_type: json # see other options below
type: sharegpt
conversation: llama-3
dataset_prepared_path: /workspace/llm_training/axolotl/llama3-multilingual/prepared_tagengo_openchat_megagon
val_set_size: 0.01
output_dir: /workspace/llm_training/axolotl/llama3-multilingual/output_tagengo_openchat_megagon_8B_llama3
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
use_wandb: true
wandb_project: wandb_project
wandb_entity: wandb_entity
wandb_name: wandb_name
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 1e-5
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 10
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero2.json
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
<details><summary>Note - we added this Llama 3 template to fastchat directly as the Llama 3 chat template was not supported when we trained this model.</summary>
```python
from fastchat.conversation import Conversation
from fastchat.conversation import register_conv_template
from fastchat.conversation import SeparatorStyle
register_conv_template(
Conversation(
name="llama-3",
system_template="<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_message}",
roles=("<|start_header_id|>user<|end_header_id|>\n", "<|start_header_id|>assistant<|end_header_id|>\n"),
sep_style=SeparatorStyle.ADD_NEW_LINE_SINGLE,
sep="<|eot_id|>",
stop_token_ids=[128009],
stop_str="<|eot_id|>",
)
)
```
</details><br>
### Training hyperparameters
This model was trained using 4 x A100 (80GB) for roughly 2.5 hours.
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1894 | 0.0 | 1 | 1.0110 |
| 0.8493 | 0.2 | 73 | 0.7057 |
| 0.8047 | 0.4 | 146 | 0.6835 |
| 0.7644 | 0.6 | 219 | 0.6687 |
| 0.7528 | 0.8 | 292 | 0.6615 |
| 0.7794 | 1.0 | 365 | 0.6595 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
# Developer
Peter Devine - ([ptrdvn](https://huggingface.co/ptrdvn))
|
{"license": "other", "tags": ["generated_from_trainer"], "license_name": "llama-3", "license_link": "https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct/raw/main/LICENSE", "base_model": "lightblue/suzume-llama-3-8B-multilingual", "pipeline_tag": "text-generation", "model-index": [{"name": "lightblue/suzume-llama-3-8B-multilingual", "results": []}]}
|
QuantFactory/suzume-llama-3-8B-multilingual-GGUF
| null |
[
"gguf",
"generated_from_trainer",
"text-generation",
"base_model:lightblue/suzume-llama-3-8B-multilingual",
"license:other",
"region:us"
] | null |
2024-04-25T11:45:04+00:00
|
null |
peft
|
<!-- 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. -->
# RM-HH-GPT2Large_helpful_gpt3_loraR64_40000_gpt2-large_shuffleFalse_extractchosenFalse
This model is a fine-tuned version of [openai-community/gpt2-large](https://huggingface.co/openai-community/gpt2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0335
- Accuracy: 0.9906
## 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: 1.41e-05
- train_batch_size: 1
- eval_batch_size: 8
- 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: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6956 | 0.02 | 250 | 0.5812 | 0.7384 |
| 0.6388 | 0.04 | 500 | 0.3891 | 0.9145 |
| 0.5931 | 0.06 | 750 | 0.2579 | 0.9580 |
| 0.5705 | 0.08 | 1000 | 0.1982 | 0.9750 |
| 0.5449 | 0.1 | 1250 | 0.1534 | 0.9797 |
| 0.577 | 0.13 | 1500 | 0.1506 | 0.9821 |
| 0.5225 | 0.15 | 1750 | 0.1299 | 0.9836 |
| 0.5516 | 0.17 | 2000 | 0.1285 | 0.9866 |
| 0.5528 | 0.19 | 2250 | 0.1244 | 0.9870 |
| 0.579 | 0.21 | 2500 | 0.1299 | 0.9881 |
| 0.5386 | 0.23 | 2750 | 0.1140 | 0.9881 |
| 0.5427 | 0.25 | 3000 | 0.1057 | 0.9885 |
| 0.5502 | 0.27 | 3250 | 0.1000 | 0.9889 |
| 0.5309 | 0.29 | 3500 | 0.0818 | 0.9895 |
| 0.558 | 0.31 | 3750 | 0.0966 | 0.9896 |
| 0.5523 | 0.33 | 4000 | 0.0833 | 0.9898 |
| 0.545 | 0.36 | 4250 | 0.0920 | 0.9902 |
| 0.5402 | 0.38 | 4500 | 0.0928 | 0.9898 |
| 0.5271 | 0.4 | 4750 | 0.0824 | 0.9902 |
| 0.5613 | 0.42 | 5000 | 0.0903 | 0.9915 |
| 0.5064 | 0.44 | 5250 | 0.0723 | 0.9913 |
| 0.5714 | 0.46 | 5500 | 0.0738 | 0.9915 |
| 0.5285 | 0.48 | 5750 | 0.0756 | 0.9908 |
| 0.5311 | 0.5 | 6000 | 0.0757 | 0.9906 |
| 0.5205 | 0.52 | 6250 | 0.0730 | 0.9895 |
| 0.5311 | 0.54 | 6500 | 0.0729 | 0.9904 |
| 0.5209 | 0.57 | 6750 | 0.0666 | 0.9896 |
| 0.5529 | 0.59 | 7000 | 0.0795 | 0.9904 |
| 0.5495 | 0.61 | 7250 | 0.0698 | 0.9910 |
| 0.5184 | 0.63 | 7500 | 0.0695 | 0.9902 |
| 0.5609 | 0.65 | 7750 | 0.0722 | 0.9904 |
| 0.5024 | 0.67 | 8000 | 0.0656 | 0.9904 |
| 0.5536 | 0.69 | 8250 | 0.0779 | 0.9889 |
| 0.5402 | 0.71 | 8500 | 0.0715 | 0.9893 |
| 0.5204 | 0.73 | 8750 | 0.0681 | 0.9896 |
| 0.544 | 0.75 | 9000 | 0.0700 | 0.9896 |
| 0.5502 | 0.77 | 9250 | 0.0722 | 0.9902 |
| 0.5334 | 0.8 | 9500 | 0.0650 | 0.9910 |
| 0.5229 | 0.82 | 9750 | 0.0606 | 0.9900 |
| 0.5235 | 0.84 | 10000 | 0.0525 | 0.9906 |
| 0.534 | 0.86 | 10250 | 0.0623 | 0.9895 |
| 0.5314 | 0.88 | 10500 | 0.0561 | 0.9904 |
| 0.5311 | 0.9 | 10750 | 0.0503 | 0.9902 |
| 0.5457 | 0.92 | 11000 | 0.0515 | 0.9910 |
| 0.548 | 0.94 | 11250 | 0.0589 | 0.9910 |
| 0.5504 | 0.96 | 11500 | 0.0612 | 0.9908 |
| 0.5102 | 0.98 | 11750 | 0.0501 | 0.9908 |
| 0.5197 | 1.0 | 12000 | 0.0505 | 0.9913 |
| 0.5406 | 1.03 | 12250 | 0.0458 | 0.9908 |
| 0.5372 | 1.05 | 12500 | 0.0468 | 0.9908 |
| 0.4972 | 1.07 | 12750 | 0.0429 | 0.9910 |
| 0.5059 | 1.09 | 13000 | 0.0422 | 0.9906 |
| 0.536 | 1.11 | 13250 | 0.0462 | 0.9900 |
| 0.5116 | 1.13 | 13500 | 0.0408 | 0.9904 |
| 0.5504 | 1.15 | 13750 | 0.0479 | 0.9908 |
| 0.5393 | 1.17 | 14000 | 0.0462 | 0.9908 |
| 0.511 | 1.19 | 14250 | 0.0426 | 0.9908 |
| 0.5059 | 1.21 | 14500 | 0.0403 | 0.9906 |
| 0.5324 | 1.23 | 14750 | 0.0381 | 0.9906 |
| 0.5227 | 1.26 | 15000 | 0.0368 | 0.9906 |
| 0.5377 | 1.28 | 15250 | 0.0442 | 0.9904 |
| 0.5269 | 1.3 | 15500 | 0.0446 | 0.9906 |
| 0.5088 | 1.32 | 15750 | 0.0487 | 0.9904 |
| 0.5271 | 1.34 | 16000 | 0.0474 | 0.9908 |
| 0.4952 | 1.36 | 16250 | 0.0377 | 0.9915 |
| 0.5201 | 1.38 | 16500 | 0.0392 | 0.9906 |
| 0.5316 | 1.4 | 16750 | 0.0431 | 0.9908 |
| 0.5186 | 1.42 | 17000 | 0.0421 | 0.9900 |
| 0.4963 | 1.44 | 17250 | 0.0366 | 0.9908 |
| 0.5324 | 1.46 | 17500 | 0.0392 | 0.9906 |
| 0.5257 | 1.49 | 17750 | 0.0392 | 0.9911 |
| 0.4908 | 1.51 | 18000 | 0.0348 | 0.9910 |
| 0.5186 | 1.53 | 18250 | 0.0371 | 0.9906 |
| 0.5385 | 1.55 | 18500 | 0.0385 | 0.9906 |
| 0.5267 | 1.57 | 18750 | 0.0370 | 0.9910 |
| 0.5294 | 1.59 | 19000 | 0.0372 | 0.9906 |
| 0.5243 | 1.61 | 19250 | 0.0360 | 0.9908 |
| 0.5414 | 1.63 | 19500 | 0.0376 | 0.9906 |
| 0.5171 | 1.65 | 19750 | 0.0403 | 0.9904 |
| 0.5081 | 1.67 | 20000 | 0.0363 | 0.9908 |
| 0.543 | 1.7 | 20250 | 0.0353 | 0.9908 |
| 0.5121 | 1.72 | 20500 | 0.0341 | 0.9910 |
| 0.5047 | 1.74 | 20750 | 0.0330 | 0.9908 |
| 0.5386 | 1.76 | 21000 | 0.0327 | 0.9911 |
| 0.5261 | 1.78 | 21250 | 0.0341 | 0.9910 |
| 0.4973 | 1.8 | 21500 | 0.0329 | 0.9913 |
| 0.5185 | 1.82 | 21750 | 0.0329 | 0.9911 |
| 0.5215 | 1.84 | 22000 | 0.0325 | 0.9911 |
| 0.4922 | 1.86 | 22250 | 0.0314 | 0.9911 |
| 0.5354 | 1.88 | 22500 | 0.0327 | 0.9908 |
| 0.5489 | 1.9 | 22750 | 0.0337 | 0.9911 |
| 0.538 | 1.93 | 23000 | 0.0336 | 0.9913 |
| 0.508 | 1.95 | 23250 | 0.0335 | 0.9910 |
| 0.5316 | 1.97 | 23500 | 0.0333 | 0.9910 |
| 0.5496 | 1.99 | 23750 | 0.0335 | 0.9906 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "library_name": "peft", "tags": ["trl", "reward-trainer", "generated_from_trainer"], "metrics": ["accuracy"], "base_model": "openai-community/gpt2-large", "model-index": [{"name": "RM-HH-GPT2Large_helpful_gpt3_loraR64_40000_gpt2-large_shuffleFalse_extractchosenFalse", "results": []}]}
|
Holarissun/RM-HH-GPT2Large_helpful_gpt3_loraR64_40000_gpt2-large_shuffleFalse_extractchosenFalse
| null |
[
"peft",
"safetensors",
"trl",
"reward-trainer",
"generated_from_trainer",
"base_model:openai-community/gpt2-large",
"license:mit",
"region:us"
] | null |
2024-04-25T11:45:24+00:00
|
null | null |
{}
|
Utshav/Llama3-70b-4bit-extraction
| null |
[
"gguf",
"region:us"
] | null |
2024-04-25T11:45:30+00:00
|
|
null | null |
{}
|
harshj0506/phi3-farmer-chat-v1.1
| null |
[
"region:us"
] | null |
2024-04-25T11:46:01+00:00
|
|
null | null |
{}
|
Surabhi-K1/llama_3_epochs5
| null |
[
"region:us"
] | null |
2024-04-25T11:48:41+00:00
|
|
text-generation
|
transformers
|
# Gemma 2B Translation v0.131
- Eval Loss: `0.99568`
- Train Loss: `0.88993`
- lr: `6e-05`
- optimizer: adamw
- lr_scheduler_type: cosine
## Prompt Template
```
<bos><start_of_turn>user
Translate into Korean:Hamsters don't eat cats.<end_of_turn>
<start_of_turn>model
햄스터는 고양이를 먹지 않습니다.<eos>
```
```
<bos><start_of_turn>user
Translate into English:햄스터는 고양이를 먹지 않습니다.<end_of_turn>
<start_of_turn>model
Hamsters do not eat cats.<eos>
```
## Model Description
- **Developed by:** `lemon-mint`
- **Model type:** Gemma
- **Language(s) (NLP):** English
- **License:** [gemma-terms-of-use](https://ai.google.dev/gemma/terms)
- **Finetuned from model:** [google/gemma-1.1-2b-it](https://huggingface.co/google/gemma-1.1-2b-it)
|
{"language": ["ko"], "license": "gemma", "library_name": "transformers", "tags": ["gemma", "pytorch", "instruct", "finetune", "translation"], "datasets": ["traintogpb/aihub-flores-koen-integrated-sparta-30k"], "widget": [{"messages": [{"role": "user", "content": "Translate into Korean:Hamsters don't eat cats."}]}], "base_model": "google/gemma-1.1-2b-it", "pipeline_tag": "text-generation"}
|
lemon-mint/gemma-2b-translation-v0.131
| null |
[
"transformers",
"safetensors",
"gemma",
"text-generation",
"pytorch",
"instruct",
"finetune",
"translation",
"conversational",
"ko",
"dataset:traintogpb/aihub-flores-koen-integrated-sparta-30k",
"base_model:google/gemma-1.1-2b-it",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T11:49:37+00:00
|
null | null |
{}
|
squaadinc/1714045720384x461855570564218900
| null |
[
"region:us"
] | null |
2024-04-25T11:49:47+00:00
|
|
reinforcement-learning
|
stable-baselines3
|
# **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 AGI-CEO -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 AGI-CEO -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 AGI-CEO
```
## 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'}
```
|
{"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": "592.00 +/- 151.89", "name": "mean_reward", "verified": false}]}]}]}
|
AGI-CEO/dqn-SpaceInvadersNoFrameskip-v4
| null |
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-25T11:50:27+00:00
|
text-generation
|
transformers
|
Quantizations of https://huggingface.co/jeiku/Chaos_RP_l3_8B
# From original readme
...
|
{"language": ["en"], "license": "other", "tags": ["transformers", "gguf", "imatrix", "jeiku", "Chaos_RP_l3_8B"], "inference": false, "pipeline_tag": "text-generation"}
|
duyntnet/Chaos_RP_l3_8B-imatrix-GGUF
| null |
[
"transformers",
"gguf",
"imatrix",
"jeiku",
"Chaos_RP_l3_8B",
"text-generation",
"en",
"license:other",
"region:us"
] | null |
2024-04-25T11:52:12+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/4-alokk/gemma-7b-English-to-Hinglish
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q2_K.gguf) | Q2_K | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.IQ3_XS.gguf) | IQ3_XS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.IQ3_S.gguf) | IQ3_S | 4.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q3_K_S.gguf) | Q3_K_S | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.IQ3_M.gguf) | IQ3_M | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q3_K_M.gguf) | Q3_K_M | 4.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q3_K_L.gguf) | Q3_K_L | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.IQ4_XS.gguf) | IQ4_XS | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q4_K_S.gguf) | Q4_K_S | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q4_K_M.gguf) | Q4_K_M | 5.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q5_K_S.gguf) | Q5_K_S | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q5_K_M.gguf) | Q5_K_M | 6.2 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q6_K.gguf) | Q6_K | 7.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.Q8_0.gguf) | Q8_0 | 9.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-7b-English-to-Hinglish-GGUF/resolve/main/gemma-7b-English-to-Hinglish.f16.gguf) | f16 | 17.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
{"language": ["en"], "library_name": "transformers", "tags": ["unsloth", "trl", "sft"], "base_model": "4-alokk/gemma-7b-English-to-Hinglish", "quantized_by": "mradermacher"}
|
mradermacher/gemma-7b-English-to-Hinglish-GGUF
| null |
[
"transformers",
"gguf",
"unsloth",
"trl",
"sft",
"en",
"base_model:4-alokk/gemma-7b-English-to-Hinglish",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:52:24+00:00
|
null | null |
{}
|
Bruhn/Lo_lab
| null |
[
"region:us"
] | null |
2024-04-25T11:52:26+00:00
|
|
null | null |
EXL2 quants for Moistral 11B v3 - https://huggingface.co/TheDrummer/Moistral-11B-v3
|
{"license": "other", "tags": ["not-for-all-audiences"], "license_name": "freeuse", "license_link": "LICENSE"}
|
MarsupialAI/Moistral-11B-v3_exl2
| null |
[
"safetensors",
"not-for-all-audiences",
"license:other",
"region:us"
] | null |
2024-04-25T11:53:09+00:00
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABmbert_22_992_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:53:27+00:00
|
|
null |
transformers
|
# Uploaded model
- **Developed by:** rbojja
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
{"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"}
|
rbojja/llama3_telugu_4bit_gguf
| null |
[
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:53:59+00:00
|
null |
mlx
|
# mlx-community/MiniCPM-2B-sft-bf16-4bit
This model was converted to MLX format from [`openbmb/MiniCPM-2B-sft-bf16`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co//Users/gokdenizgulmez/Desktop/MiniCPM-2B-sft-bf16) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/MiniCPM-2B-sft-bf16-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
{"tags": ["mlx"]}
|
Isaak-Carter/minicpm-2b-safetensors-q4
| null |
[
"mlx",
"safetensors",
"minicpm",
"region:us"
] | null |
2024-04-25T11:55:14+00:00
|
text-classification
|
transformers
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
{"library_name": "transformers", "tags": []}
|
kangXn/enmr-st1-mde
| null |
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:55:36+00:00
|
text-to-image
|
diffusers
|
# 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 🧨 diffusers 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]
|
{"library_name": "diffusers"}
|
Niggendar/AnimeRealPantheon_h8llBakedvae
| null |
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null |
2024-04-25T11:55:55+00:00
|
null |
peft
|
<!-- 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. -->
# trainer
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0809
- eval_runtime: 37.3984
- eval_samples_per_second: 0.695
- eval_steps_per_second: 0.348
- epoch: 4.0
- step: 472
## 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_steps: 50
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "trainer", "results": []}]}
|
Surabhi-K/llama_3_epochs4-31
| null |
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null |
2024-04-25T11:56:28+00:00
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABmbert_22_111_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:57:10+00:00
|
|
automatic-speech-recognition
|
transformers
|
<!-- 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 Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2859
- Wer: 34.5382
## 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: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0818 | 2.4450 | 1000 | 0.2859 | 34.5382 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"language": ["hi"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Hi - Sanchit Gandhi", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 11.0", "type": "mozilla-foundation/common_voice_11_0", "config": "hi", "split": "None", "args": "config: hi, split: test"}, "metrics": [{"type": "wer", "value": 34.53822060441886, "name": "Wer"}]}]}]}
|
Dua020/whisper-large-v3
| null |
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:58:38+00:00
|
question-answering
|
transformers
|
{}
|
lanzv/ClinicalBERTPRQABCZ_22_54_CS
| null |
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T11:59:05+00:00
|
|
null |
peft
|
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.7.2.dev0
|
{"library_name": "peft", "base_model": "openlm-research/open_llama_3b_v2"}
|
yiyic/llama3b-lora-clf-1
| null |
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"region:us"
] | null |
2024-04-25T11:59:40+00:00
|
null |
fastai
|
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
{"tags": ["fastai"]}
|
Hitomiblood/CnnLearner_resnet34_chestXrayTPU
| null |
[
"fastai",
"region:us"
] | null |
2024-04-25T12:00:15+00:00
|
null |
transformers
|
{}
|
rancelyndar/segformer-b5-scene-parse-150
| null |
[
"transformers",
"tensorboard",
"safetensors",
"segformer",
"endpoints_compatible",
"region:us"
] | null |
2024-04-25T12:00:43+00:00
|
|
translation
|
transformers
|
# LLaMA 2 7B - Toxicator RU
This fine-tuned model based on [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf), it utilizes [evilfreelancer/toxicator-ru](https://huggingface.co/datasets/evilfreelancer/toxicator-ru) dataset created from samples in [s-nlp/russe_detox_2022](https://github.com/s-nlp/russe_detox_2022) project.
Model was tuned **just for lulz** for experimenting with [TorchTune](https://github.com/pytorch/torchtune) tool.
## Links
* https://github.com/EvilFreelancer/toxicator-ru - GitHub repository with train scripts and scripts for generating dataset
* https://huggingface.co/datasets/evilfreelancer/toxicator-ru - dataset
* https://api.wandb.ai/links/evilfreelancer/33t8pqze - wandb report about training
|
{"language": ["ru"], "license": "llama2", "tags": ["toxify", "detoxify"], "datasets": ["evilfreelancer/toxicator-ru"], "pipeline_tag": "translation"}
|
evilfreelancer/llama2-7b-toxicator-ru
| null |
[
"transformers",
"pytorch",
"llama",
"text-generation",
"toxify",
"detoxify",
"translation",
"ru",
"dataset:evilfreelancer/toxicator-ru",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-25T12:00:53+00:00
|
reinforcement-learning
| null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
{"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]}
|
aw-infoprojekt/Reinforce-v1
| null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null |
2024-04-25T12:02:38+00:00
|
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