Search is not available for this dataset
pipeline_tag
stringclasses 48
values | library_name
stringclasses 205
values | text
stringlengths 0
18.3M
| metadata
stringlengths 2
1.07B
| id
stringlengths 5
122
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null | tags
listlengths 1
1.84k
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null | created_at
stringlengths 25
25
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|---|---|---|---|---|---|---|---|---|
text-generation
|
transformers
|
{"license": "apache-2.0"}
|
ramimu/ali-ai-model
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:00:31+00:00
|
|
null |
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. -->
# PolizzeDonut-UltimaProvaCluster-Cluster1di7-5epochs
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "PolizzeDonut-UltimaProvaCluster-Cluster1di7-5epochs", "results": []}]}
|
tedad09/PolizzeDonut-UltimaProvaCluster-Cluster1di7-5epochs
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:00:32+00:00
|
null | null |
# DavidAU/GALAXY-16B-v1.0-Q8_0-GGUF
This model was converted to GGUF format from [`TeeZee/GALAXY-16B-v1.0`](https://huggingface.co/TeeZee/GALAXY-16B-v1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/TeeZee/GALAXY-16B-v1.0) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/GALAXY-16B-v1.0-Q8_0-GGUF --model galaxy-16b-v1.0.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/GALAXY-16B-v1.0-Q8_0-GGUF --model galaxy-16b-v1.0.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m galaxy-16b-v1.0.Q8_0.gguf -n 128
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["not-for-all-audiences", "llama-cpp", "gguf-my-repo"], "datasets": ["Intel/orca_dpo_pairs", "athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW", "Open-Orca/SlimOrca", "MinervaAI/Aesir-Preview", "allenai/ultrafeedback_binarized_cleaned"]}
|
DavidAU/GALAXY-16B-v1.0-Q8_0-GGUF
| null |
[
"gguf",
"not-for-all-audiences",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:Intel/orca_dpo_pairs",
"dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW",
"dataset:Open-Orca/SlimOrca",
"dataset:MinervaAI/Aesir-Preview",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:01:32+00:00
|
null | null |
{"license": "apache-2.0"}
|
FydeOS/Qwen1.5-1_8B_rkLLM
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:02:20+00:00
|
|
null | null |
{"license": "openrail"}
|
Coolwowsocoolwow/Mrs_Martin
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-24T07:02:52+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 0.10.1.dev0
|
{"library_name": "peft", "base_model": "Trelis/Llama-2-7b-chat-hf-sharded-bf16"}
|
Vibhav1612/LlamaQuantized
| null |
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null |
2024-04-24T07:03:32+00:00
|
null | null |
{}
|
Aishu1102/gpt2
| null |
[
"region:us"
] | null |
2024-04-24T07:04:13+00:00
|
|
null | null |
{"license": "mit"}
|
ljf0219/test
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-24T07:04:30+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. -->
# Flant5-offensive-multilingual
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0012
- Precision: 0.6875
- Recall: 0.6040
- F1: 0.6430
- Total Predictions: 3532
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Total Predictions |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:-----------------:|
| 0.2343 | 1.0 | 3753 | 0.0011 | 0.5924 | 0.6481 | 0.6190 | 3532 |
| 0.0008 | 2.0 | 7506 | 0.0010 | 0.6903 | 0.5416 | 0.6070 | 3532 |
| 0.0006 | 3.0 | 11259 | 0.0011 | 0.6012 | 0.7238 | 0.6569 | 3532 |
| 0.0005 | 4.0 | 15012 | 0.0011 | 0.6882 | 0.5765 | 0.6274 | 3532 |
| 0.0004 | 5.0 | 18765 | 0.0012 | 0.6875 | 0.6040 | 0.6430 | 3532 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.0.0+cu118
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1"], "base_model": "google/flan-t5-base", "model-index": [{"name": "Flant5-offensive-multilingual", "results": []}]}
|
JenniferHJF/Flant5-offensive-multilingual
| null |
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:04:36+00:00
|
null | null |
# Yamshadowexperiment28Experiment26-7B
Yamshadowexperiment28Experiment26-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: automerger/YamshadowExperiment28-7B
- model: yam-peleg/Experiment26-7B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Yamshadowexperiment28Experiment26-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
{"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]}
|
automerger/Yamshadowexperiment28Experiment26-7B
| null |
[
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:04:54+00:00
|
null | null |
{}
|
GraydientPlatformAPI/loras-april24b
| null |
[
"region:us"
] | null |
2024-04-24T07:05:38+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]
|
{"license": "apache-2.0", "library_name": "transformers"}
|
Akirami/truthy-llama3-8b
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-24T07:07:01+00:00
|
null | null |
# DavidAU/GALAXY-16B-v1.0-Q4_K_M-GGUF
This model was converted to GGUF format from [`TeeZee/GALAXY-16B-v1.0`](https://huggingface.co/TeeZee/GALAXY-16B-v1.0) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/TeeZee/GALAXY-16B-v1.0) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/GALAXY-16B-v1.0-Q4_K_M-GGUF --model galaxy-16b-v1.0.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/GALAXY-16B-v1.0-Q4_K_M-GGUF --model galaxy-16b-v1.0.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m galaxy-16b-v1.0.Q4_K_M.gguf -n 128
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["not-for-all-audiences", "llama-cpp", "gguf-my-repo"], "datasets": ["Intel/orca_dpo_pairs", "athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW", "Open-Orca/SlimOrca", "MinervaAI/Aesir-Preview", "allenai/ultrafeedback_binarized_cleaned"]}
|
DavidAU/GALAXY-16B-v1.0-Q4_K_M-GGUF
| null |
[
"gguf",
"not-for-all-audiences",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:Intel/orca_dpo_pairs",
"dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW",
"dataset:Open-Orca/SlimOrca",
"dataset:MinervaAI/Aesir-Preview",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:07:06+00:00
|
text-generation
|
transformers
|
{}
|
aemack/Qwen-1_8B-Chat_ihateyou_ilovecheese
| null |
[
"transformers",
"safetensors",
"qwen",
"text-generation",
"custom_code",
"autotrain_compatible",
"region:us"
] | null |
2024-04-24T07:07:09+00:00
|
|
null | null |
# SecGPT 网络安全大模型
### **项目**
- [GitHub](https://github.com/Clouditera/SecGPT)
- [原版Pytorch模型](https://huggingface.co/clouditera/secgpt)
### **简介**
- 随着大语言模型的崛起，网安大模型也掀起了一股热潮，本人在逛 GitHub 时偶然发现了云起无垠开源的 SecGPT，但官方调用脚本中使用了 Cuda，且没有提供 GGUF 版本，故使用了 [llama.cpp](https://github.com/ggerganov/llama.cpp) 的 convert 脚本进行转换，并上传至huggingface
### **测试设备**
- MacBook Pro 16 寸
- M3 Max
- 48 GB
### **Usage**
- 分为 `secgpt.gguf` 与 `secgpt-mini.gguf` 两个版本
- `secgpt.gguf` 需 26.5 G 显存
- `secgpt-mini.gguf` 需 1.6 G 显存
- 使用方法
- 将 GGUF 导入[LM Studio](https://lmstudio.ai/)，并使用 `secgpt-all.json` 作为参数配置
|
{"language": ["zh"], "license": "apache-2.0", "tags": ["cybersecurity"]}
|
LingJingMaster/Clouditera-SecGPT-GGUF
| null |
[
"gguf",
"cybersecurity",
"zh",
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:07:34+00:00
|
null | null |
This model is trained to recognise Indian Sign Language(ISL) which is trained using video dataset available here -- https://zenodo.org/records/4010759
|
{"language": ["en"], "license": "mit", "tags": ["art"], "metrics": ["Testing accuracy of 44%"]}
|
cdsteameight/ISL-SignLanguageTranslation
| null |
[
"art",
"en",
"license:mit",
"region:us"
] | null |
2024-04-24T07:07:40+00:00
|
null | null |
{}
|
ssamperr/detr_v2_30
| null |
[
"region:us"
] | null |
2024-04-24T07:07:41+00:00
|
|
null | null |
{}
|
maharengarajan/summarization-model
| null |
[
"region:us"
] | null |
2024-04-24T07:08:46+00:00
|
|
null | null |
# DavidAU/Scarlett-Llama-3-8B-Q8_0-GGUF
This model was converted to GGUF format from [`ajibawa-2023/Scarlett-Llama-3-8B`](https://huggingface.co/ajibawa-2023/Scarlett-Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ajibawa-2023/Scarlett-Llama-3-8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/Scarlett-Llama-3-8B-Q8_0-GGUF --model scarlett-llama-3-8b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/Scarlett-Llama-3-8B-Q8_0-GGUF --model scarlett-llama-3-8b.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m scarlett-llama-3-8b.Q8_0.gguf -n 128
```
|
{"language": ["en"], "license": "other", "tags": ["art", "philosophy", "romance", "jokes", "advice", "code", "llama-cpp", "gguf-my-repo"], "license_name": "llama3", "license_link": "LICENSE", "model-index": [{"name": "Scarlett-Llama-3-8B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 62.63, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Scarlett-Llama-3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 83.86, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Scarlett-Llama-3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 66.46, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Scarlett-Llama-3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 56.27}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Scarlett-Llama-3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 78.06, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Scarlett-Llama-3-8B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 47.31, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Scarlett-Llama-3-8B", "name": "Open LLM Leaderboard"}}]}]}
|
DavidAU/Scarlett-Llama-3-8B-Q8_0-GGUF
| null |
[
"gguf",
"art",
"philosophy",
"romance",
"jokes",
"advice",
"code",
"llama-cpp",
"gguf-my-repo",
"en",
"license:other",
"model-index",
"region:us"
] | null |
2024-04-24T07:08:53+00:00
|
text-generation
|
transformers
|
## Model Card for Model ID
French-Alpaca based on microsoft/Phi-3-mini-4k-instruct
4k is the context length (in tokens)
### Model Description
fine-tuned from the original French-Alpaca-dataset entirely generated with OpenAI GPT-3.5-turbo.
French-Alpaca is a general model and can itself be finetuned to be specialized for specific use cases.
The fine-tuning method is inspired from https://crfm.stanford.edu/2023/03/13/alpaca.html
Quantized GGUF version : coming soon
### Usage
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3-mini-4k-instruct",
device_map="cuda",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("jpacifico/French-Alpaca-Phi-3-mini-4k-instruct-v1.0")
messages = [
{"role": "system", "content": "Vous êtes un assistant numérique serviable. Veuillez fournir des informations sûres, éthiques et précises à l'utilisateur."},
{"role": "user", "content": "Pouvez-vous fournir des façons de manger des combinaisons de bananes et de fruits du dragon ?"},
{"role": "assistant", "content": "Bien sûr ! Voici quelques façons de manger des bananes et des fruits du dragon ensemble : 1. Smoothie à la banane et au fruit du dragon : mixez des bananes et des fruits du dragon avec un peu de lait et de miel. 2. Salade de banane et de fruit du dragon : mélangez des bananes tranchées et des fruits du dragon avec un peu de jus de citron et de miel."},
{"role": "user", "content": "Et pour résoudre une équation 2x + 3 = 7 ?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
### Limitations
The French-Alpaca model is a quick demonstration that a 3B base model can be easily fine-tuned to specialize in a particular language.
It does not have any moderation mechanisms.
- **Developed by:** Jonathan Pacifico, 2024
- **Model type:** LLM
- **Language(s) (NLP):** French
- **License:** MIT
|
{"language": ["fr", "en"], "license": "mit", "library_name": "transformers", "tags": ["Phi-3", "french", "Phi-3-mini", "french-alpaca"], "datasets": ["jpacifico/French-Alpaca-dataset-Instruct-110K"]}
|
jpacifico/French-Alpaca-Phi-3-mini-4k-instruct-v1.0
| null |
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"Phi-3",
"french",
"Phi-3-mini",
"french-alpaca",
"conversational",
"custom_code",
"fr",
"en",
"dataset:jpacifico/French-Alpaca-dataset-Instruct-110K",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:08:54+00:00
|
null | null |
# DavidAU/Young-Children-Storyteller-Mistral-7B-Q6_K-GGUF
This model was converted to GGUF format from [`ajibawa-2023/Young-Children-Storyteller-Mistral-7B`](https://huggingface.co/ajibawa-2023/Young-Children-Storyteller-Mistral-7B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ajibawa-2023/Young-Children-Storyteller-Mistral-7B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/Young-Children-Storyteller-Mistral-7B-Q6_K-GGUF --model young-children-storyteller-mistral-7b.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/Young-Children-Storyteller-Mistral-7B-Q6_K-GGUF --model young-children-storyteller-mistral-7b.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m young-children-storyteller-mistral-7b.Q6_K.gguf -n 128
```
|
{"language": ["en"], "license": "apache-2.0", "tags": ["story", "young children", "educational", "knowledge", "llama-cpp", "gguf-my-repo"], "datasets": ["ajibawa-2023/Children-Stories-Collection"], "model-index": [{"name": "Young-Children-Storyteller-Mistral-7B", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 68.69, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 84.67, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.11, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 62.62}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 81.22, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 65.2, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=ajibawa-2023/Young-Children-Storyteller-Mistral-7B", "name": "Open LLM Leaderboard"}}]}]}
|
DavidAU/Young-Children-Storyteller-Mistral-7B-Q6_K-GGUF
| null |
[
"gguf",
"story",
"young children",
"educational",
"knowledge",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:ajibawa-2023/Children-Stories-Collection",
"license:apache-2.0",
"model-index",
"region:us"
] | null |
2024-04-24T07:10:09+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. -->
# lnmt
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7972
- Accuracy: {'accuracy': 0.6208813838550247}
- F1 Macro: {'f1': 0.3506606197441491}
- F1 Weighted: {'f1': 0.6062668131729496}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro | F1 Weighted |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------:|:---------------------------:|:--------------------------:|
| No log | 1.0 | 315 | 2.0101 | {'accuracy': 0.5226523887973641} | {'f1': 0.21712503989679657} | {'f1': 0.459506356775351} |
| 2.2969 | 2.0 | 630 | 1.6716 | {'accuracy': 0.5963756177924218} | {'f1': 0.28274236255720786} | {'f1': 0.5462732390600772} |
| 2.2969 | 3.0 | 945 | 1.5967 | {'accuracy': 0.6112026359143328} | {'f1': 0.3279242367574629} | {'f1': 0.5787485773304204} |
| 1.1815 | 4.0 | 1260 | 1.5843 | {'accuracy': 0.6202635914332785} | {'f1': 0.3402580752236545} | {'f1': 0.5918094876585247} |
| 0.7089 | 5.0 | 1575 | 1.6031 | {'accuracy': 0.6219110378912686} | {'f1': 0.3471078372421453} | {'f1': 0.5941366500585097} |
| 0.7089 | 6.0 | 1890 | 1.6876 | {'accuracy': 0.6149093904448105} | {'f1': 0.35129077551349414} | {'f1': 0.5935341462382293} |
| 0.4532 | 7.0 | 2205 | 1.7093 | {'accuracy': 0.6208813838550247} | {'f1': 0.35300405317763817} | {'f1': 0.6021058143955713} |
| 0.3178 | 8.0 | 2520 | 1.7752 | {'accuracy': 0.6138797364085667} | {'f1': 0.35479307050001907} | {'f1': 0.5998441386303183} |
| 0.3178 | 9.0 | 2835 | 1.7888 | {'accuracy': 0.6188220757825371} | {'f1': 0.3553222770673821} | {'f1': 0.6033599756075638} |
| 0.2417 | 10.0 | 3150 | 1.7972 | {'accuracy': 0.6208813838550247} | {'f1': 0.3506606197441491} | {'f1': 0.6062668131729496} |
### 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"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "lnmt", "results": []}]}
|
carmenlozano/lnmt
| null |
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:10:28+00:00
|
null | null |
{}
|
kanishka7878/modeltest17
| null |
[
"region:us"
] | null |
2024-04-24T07:10:56+00:00
|
|
null | null |
{}
|
Rustamello/Dima
| null |
[
"region:us"
] | null |
2024-04-24T07:11:25+00:00
|
|
text-generation
|
transformers
|
# OpenLLaMA: An Open Reproduction of LLaMA
In this repo, we present a permissively licensed open source reproduction of Meta AI's [LLaMA](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/) large language model. We are releasing a 7B and 3B model trained on 1T tokens, as well as the preview of a 13B model trained on 600B tokens. We provide PyTorch and JAX weights of pre-trained OpenLLaMA models, as well as evaluation results and comparison against the original LLaMA models. Please see the [project homepage of OpenLLaMA](https://github.com/openlm-research/open_llama) for more details.
## Weights Release, License and Usage
We release the weights in two formats: an EasyLM format to be use with our [EasyLM framework](https://github.com/young-geng/EasyLM), and a PyTorch format to be used with the [Hugging Face transformers](https://huggingface.co/docs/transformers/index) library. Both our training framework EasyLM and the checkpoint weights are licensed permissively under the Apache 2.0 license.
### Loading the Weights with Hugging Face Transformers
Preview checkpoints can be directly loaded from Hugging Face Hub. **Please note that it is advised to avoid using the Hugging Face fast tokenizer for now, as we’ve observed that the auto-converted fast tokenizer sometimes gives incorrect tokenizations.** This can be achieved by directly using the `LlamaTokenizer` class, or passing in the `use_fast=False` option for the `AutoTokenizer` class. See the following example for usage.
```python
import torch
from transformers import LlamaTokenizer, LlamaForCausalLM
model_path = 'openlm-research/open_llama_3b'
# model_path = 'openlm-research/open_llama_7b'
tokenizer = LlamaTokenizer.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(
model_path, torch_dtype=torch.float16, device_map='auto',
)
prompt = 'Q: What is the largest animal?\nA:'
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generation_output = model.generate(
input_ids=input_ids, max_new_tokens=32
)
print(tokenizer.decode(generation_output[0]))
```
For more advanced usage, please follow the [transformers LLaMA documentation](https://huggingface.co/docs/transformers/main/model_doc/llama).
### Evaluating with LM-Eval-Harness
The model can be evaluated with [lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness). However, due to the aforementioned tokenizer issue, we need to avoid using the fast tokenizer to obtain the correct results. This can be achieved by passing in `use_fast=False` to [this part of lm-eval-harness](https://github.com/EleutherAI/lm-evaluation-harness/blob/4b701e228768052cfae9043dca13e82052ca5eea/lm_eval/models/huggingface.py#LL313C9-L316C10), as shown in the example below:
```python
tokenizer = self.AUTO_TOKENIZER_CLASS.from_pretrained(
pretrained if tokenizer is None else tokenizer,
revision=revision + ("/" + subfolder if subfolder is not None else ""),
use_fast=False
)
```
### Loading the Weights with EasyLM
For using the weights in our EasyLM framework, please refer to the [LLaMA documentation of EasyLM](https://github.com/young-geng/EasyLM/blob/main/docs/llama.md). Note that unlike the original LLaMA model, our OpenLLaMA tokenizer and weights are trained completely from scratch so it is no longer needed to obtain the original LLaMA tokenizer and weights. Note that we use BOS (beginning of sentence) token (id=1) during training, so it is best to prepend this token for best performance during few-shot evaluation.
## Dataset and Training
We train our models on the [RedPajama](https://www.together.xyz/blog/redpajama) dataset released by [Together](https://www.together.xyz/), which is a reproduction of the LLaMA training dataset containing over 1.2 trillion tokens. We follow the exactly same preprocessing steps and training hyperparameters as the original LLaMA paper, including model architecture, context length, training steps, learning rate schedule, and optimizer. The only difference between our setting and the original one is the dataset used: OpenLLaMA employs the RedPajama dataset rather than the one utilized by the original LLaMA.
We train the models on cloud TPU-v4s using [EasyLM](https://github.com/young-geng/EasyLM), a JAX based training pipeline we developed for training and fine-tuning large language models. We employ a combination of normal data parallelism and [fully sharded data parallelism (also know as ZeRO stage 3)](https://engineering.fb.com/2021/07/15/open-source/fsdp/) to balance the training throughput and memory usage. Overall we reach a throughput of over 2200 tokens / second / TPU-v4 chip for our 7B model.
## Evaluation
We evaluated OpenLLaMA on a wide range of tasks using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness). The LLaMA results are generated by running the original LLaMA model on the same evaluation metrics. We note that our results for the LLaMA model differ slightly from the original LLaMA paper, which we believe is a result of different evaluation protocols. Similar differences have been reported in [this issue of lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness/issues/443). Additionally, we present the results of GPT-J, a 6B parameter model trained on the [Pile](https://pile.eleuther.ai/) dataset by [EleutherAI](https://www.eleuther.ai/).
The original LLaMA model was trained for 1 trillion tokens and GPT-J was trained for 500 billion tokens. We present the results in the table below. OpenLLaMA exhibits comparable performance to the original LLaMA and GPT-J across a majority of tasks, and outperforms them in some tasks.
| **Task/Metric** | GPT-J 6B | LLaMA 7B | OpenLLaMA 7B | OpenLLaMA 3B | OpenLLaMA 13B 600BT |
| ---------------------- | -------- | -------- | ------------ | ------------ | ------------------- |
| anli_r1/acc | 0.32 | 0.35 | 0.33 | 0.33 | 0.33 |
| anli_r2/acc | 0.34 | 0.34 | 0.36 | 0.32 | 0.35 |
| anli_r3/acc | 0.35 | 0.37 | 0.38 | 0.35 | 0.38 |
| arc_challenge/acc | 0.34 | 0.39 | 0.37 | 0.34 | 0.39 |
| arc_challenge/acc_norm | 0.37 | 0.41 | 0.38 | 0.37 | 0.42 |
| arc_easy/acc | 0.67 | 0.68 | 0.72 | 0.69 | 0.74 |
| arc_easy/acc_norm | 0.62 | 0.52 | 0.68 | 0.65 | 0.70 |
| ddboolq/acc | 0.50 | 0.56 | 0.53 | 0.49 | 0.71 |
| hellaswag/acc | 0.36 | 0.36 | 0.63 | 0.43 | 0.54 |
| hellaswag/acc_norm | 0.66 | 0.73 | 0.72 | 0.67 | 0.73 |
| openbookqa/acc | 0.29 | 0.29 | 0.30 | 0.27 | 0.30 |
| openbookqa/acc_norm | 0.38 | 0.41 | 0.40 | 0.40 | 0.41 |
| piqa/acc | 0.75 | 0.78 | 0.76 | 0.75 | 0.77 |
| piqa/acc_norm | 0.76 | 0.78 | 0.77 | 0.76 | 0.78 |
| record/em | 0.88 | 0.91 | 0.89 | 0.88 | 0.90 |
| record/f1 | 0.89 | 0.91 | 0.90 | 0.89 | 0.90 |
| rte/acc | 0.54 | 0.56 | 0.60 | 0.58 | 0.65 |
| truthfulqa_mc/mc1 | 0.20 | 0.21 | 0.23 | 0.22 | 0.22 |
| truthfulqa_mc/mc2 | 0.36 | 0.34 | 0.35 | 0.35 | 0.35 |
| wic/acc | 0.50 | 0.50 | 0.51 | 0.48 | 0.49 |
| winogrande/acc | 0.64 | 0.68 | 0.67 | 0.62 | 0.67 |
| Average | 0.51 | 0.53 | 0.55 | 0.52 | 0.56 |
We removed the task CB and WSC from our benchmark, as our model performs suspiciously well on these two tasks. We hypothesize that there could be a benchmark data contamination in the training set.
## Contact
We would love to get feedback from the community. If you have any questions, please open an issue or contact us.
OpenLLaMA is developed by:
[Xinyang Geng](https://young-geng.xyz/)* and [Hao Liu](https://www.haoliu.site/)* from Berkeley AI Research.
*Equal Contribution
## Acknowledgment
We thank the [Google TPU Research Cloud](https://sites.research.google/trc/about/) program for providing part of the computation resources. We’d like to specially thank Jonathan Caton from TPU Research Cloud for helping us organizing compute resources, Rafi Witten from the Google Cloud team and James Bradbury from the Google JAX team for helping us optimizing our training throughput. We’d also want to thank Charlie Snell, Gautier Izacard, Eric Wallace, Lianmin Zheng and our user community for the discussions and feedback.
The OpenLLaMA 13B model is trained in collaboration with [Stability AI](https://stability.ai/), and we thank Stability AI for providing the computation resources. We’d like to especially thank David Ha and Shivanshu Purohit for the coordinating the logistics and providing engineering support.
## Reference
If you found OpenLLaMA useful in your research or applications, please cite using the following BibTeX:
```
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
```
```
@software{together2023redpajama,
author = {Together Computer},
title = {RedPajama-Data: An Open Source Recipe to Reproduce LLaMA training dataset},
month = April,
year = 2023,
url = {https://github.com/togethercomputer/RedPajama-Data}
}
```
```
@article{touvron2023llama,
title={Llama: Open and efficient foundation language models},
author={Touvron, Hugo and Lavril, Thibaut and Izacard, Gautier and Martinet, Xavier and Lachaux, Marie-Anne and Lacroix, Timoth{\'e}e and Rozi{\`e}re, Baptiste and Goyal, Naman and Hambro, Eric and Azhar, Faisal and others},
journal={arXiv preprint arXiv:2302.13971},
year={2023}
}
```
|
{"license": "apache-2.0", "datasets": ["togethercomputer/RedPajama-Data-1T"]}
|
titanbot/ct2-int8-open-llama-7b
| null |
[
"transformers",
"llama",
"text-generation",
"dataset:togethercomputer/RedPajama-Data-1T",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:11:49+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/KnutJaegersberg/Llama3-Deita-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/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-8b.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-Deita-8b-GGUF/resolve/main/Llama3-Deita-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"], "license": "llama3", "library_name": "transformers", "base_model": "KnutJaegersberg/Llama3-Deita-8b", "quantized_by": "mradermacher"}
|
mradermacher/Llama3-Deita-8b-GGUF
| null |
[
"transformers",
"gguf",
"en",
"base_model:KnutJaegersberg/Llama3-Deita-8b",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:13:51+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ValiantLabs/Llama3-70B-ShiningValiant2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-i1-GGUF
## 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/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.IQ3_XS.gguf) | IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.IQ3_M.gguf) | IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q5_K_M.gguf) | Q5_K_M | 50.1 | |
| [PART 1](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama3-70B-ShiningValiant2-GGUF/resolve/main/Llama3-70B-ShiningValiant2.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | 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": "other", "library_name": "transformers", "tags": ["shining-valiant", "shining-valiant-2", "valiant", "valiant-labs", "llama", "llama-3", "llama-3-instruct", "llama-3-instruct-70b", "70b", "conversational", "chat", "instruct"], "base_model": "ValiantLabs/Llama3-70B-ShiningValiant2", "license_link": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct/blob/main/LICENSE", "license_name": "llama3", "model_type": "llama", "quantized_by": "mradermacher"}
|
mradermacher/Llama3-70B-ShiningValiant2-GGUF
| null |
[
"transformers",
"gguf",
"shining-valiant",
"shining-valiant-2",
"valiant",
"valiant-labs",
"llama",
"llama-3",
"llama-3-instruct",
"llama-3-instruct-70b",
"70b",
"conversational",
"chat",
"instruct",
"en",
"base_model:ValiantLabs/Llama3-70B-ShiningValiant2",
"license:other",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:14:00+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** Tina2088
- **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"}
|
Tina2088/lora_model
| 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 |
2024-04-24T07:15:41+00:00
|
text-generation
|
transformers
|
{"license": "mit"}
|
oofnan/stegBot2
| null |
[
"transformers",
"pytorch",
"gemma",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:16:14+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": "276.45 +/- 20.44", "name": "mean_reward", "verified": false}]}]}]}
|
nikola13/ppo-LunarLander-v2
| null |
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null |
2024-04-24T07:16:22+00:00
|
null | null |
{"license": "openrail"}
|
coreliastreet/Lana_Del_Rey
| null |
[
"license:openrail",
"region:us"
] | null |
2024-04-24T07:17:42+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-14m_mz-130_IMDB_n-its-10-seed-2
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) 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: 2
- 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
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_pythia-14m_mz-130_IMDB_n-its-10-seed-2", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-14m_mz-130_IMDB_n-its-10-seed-2
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:18:09+00:00
|
null | null |
{}
|
TanvirMungekar/Llama3-Complete
| null |
[
"gguf",
"region:us"
] | null |
2024-04-24T07:18:21+00:00
|
|
reinforcement-learning
| null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'jiaqianwu/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
{"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "-160.20 +/- 91.90", "name": "mean_reward", "verified": false}]}]}]}
|
jiaqianwu/ppo-CartPole-v1
| null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | null |
2024-04-24T07:18:49+00:00
|
null |
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. -->
# donut_synDB_big
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0569
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3353 | 0.92 | 60 | 0.1525 |
| 0.1389 | 1.38 | 90 | 0.0705 |
| 0.1055 | 1.85 | 120 | 0.0595 |
| 0.0701 | 2.31 | 150 | 0.0727 |
| 0.0547 | 2.77 | 180 | 0.0750 |
| 0.0454 | 3.23 | 210 | 0.0714 |
| 0.0371 | 3.69 | 240 | 0.0609 |
| 0.0332 | 4.15 | 270 | 0.0629 |
| 0.0269 | 4.62 | 300 | 0.0583 |
| 0.0233 | 5.08 | 330 | 0.0601 |
| 0.0219 | 5.54 | 360 | 0.0576 |
| 0.0227 | 6.0 | 390 | 0.0569 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "donut_synDB_big", "results": []}]}
|
Donut01/donut_synDB_big
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:18:52+00:00
|
null | null |
{"license": "apache-2.0"}
|
yan-hao-tian/vw_convnext-ti_cityscapes
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:19:04+00:00
|
|
text2text-generation
|
transformers
|
# PLLaVA Model Card
## Model details
**Model type:**
PLLaVA-13B is an open-source video-language chatbot trained by fine-tuning Image-LLM on video instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: llava-hf/llava-v1.6-vicuna-13b-hf
**Model date:**
PLLaVA-13B was trained in April 2024.
**Paper or resources for more information:**
- github repo: https://github.com/magic-research/PLLaVA
- project page: https://pllava.github.io/
- paper link: https://arxiv.org/abs/2404.16994
## License
llava-hf/llava-v1.6-vicuna-13b-hf license.
**Where to send questions or comments about the model:**
https://github.com/magic-research/PLLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of PLLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
Video-Instruct-Tuning data of OpenGVLab/VideoChat2-IT
## Evaluation dataset
A collection of 6 benchmarks, including 5 VQA benchmarks and 1 recent benchmarks specifically proposed for Video-LMMs.
|
{"license": "apache-2.0", "tags": ["video LLM"], "datasets": ["OpenGVLab/VideoChat2-IT"]}
|
ermu2001/pllava-13b
| null |
[
"transformers",
"safetensors",
"llava",
"text2text-generation",
"video LLM",
"dataset:OpenGVLab/VideoChat2-IT",
"arxiv:2404.16994",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
] | null |
2024-04-24T07:19:04+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": []}
|
chcho/OrpoLlama-3-8B
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:19:31+00:00
|
null | null |
{"license": "apache-2.0"}
|
yan-hao-tian/vw_convnext-s_cityscapes
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:19:34+00:00
|
|
null | null |
{}
|
chanpaca/pacapaca
| null |
[
"region:us"
] | null |
2024-04-24T07:19:49+00:00
|
|
null | null |
{"license": "wtfpl"}
|
autismanon/sdxl_loradump
| null |
[
"license:wtfpl",
"region:us"
] | null |
2024-04-24T07:19:53+00:00
|
|
null | null |
{"license": "apache-2.0"}
|
yan-hao-tian/vw_convnext-b_cityscapes
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:19:55+00:00
|
|
text-generation
|
transformers
|
# Uploaded model
- **Developed by:** akbargherbal
- **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"}
|
akbargherbal/think_tanks_v02_16bit
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:20:35+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/engu-sb-mde
| null |
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:20:40+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": []}
|
Andrei481/Mistral-7B-Instruct-v0.2-hakurei-ro
| null |
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:20:40+00:00
|
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# sourav10/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.5831
- Validation Loss: 1.7498
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4181 | 2.0762 | 0 |
| 1.8471 | 1.7498 | 1 |
| 1.5831 | 1.7498 | 2 |
### Framework versions
- Transformers 4.40.0
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "sourav10/my_awesome_qa_model", "results": []}]}
|
sourav10/my_awesome_qa_model
| null |
[
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:20:43+00:00
|
null | null |
{"license": "apache-2.0"}
|
yan-hao-tian/vw_convnext-l_cityscapes
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:20:45+00:00
|
|
null |
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. -->
# PolizzeDonut-UltimaProvaCluster-Cluster2di7-5epochs
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "PolizzeDonut-UltimaProvaCluster-Cluster2di7-5epochs", "results": []}]}
|
tedad09/PolizzeDonut-UltimaProvaCluster-Cluster2di7-5epochs
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:20:45+00:00
|
null | null |
{"license": "apache-2.0"}
|
yan-hao-tian/vw_convnext-xl_cityscapes
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:21:06+00:00
|
|
text-generation
|
transformers
|
{}
|
santoshsto/mistral-4x7b-codegen-MOE-4bit
| null |
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-24T07:21:27+00:00
|
|
null |
transformers
|
UniMERNet: A Universal Network for Mathematical Expression Recognition in Real-World Scenarios.
Visit our GitHub repository at [unimernet](https://github.com/opendatalab/unimernet) for more information.
|
{"license": "apache-2.0"}
|
wanderkid/unimernet
| null |
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:22:04+00:00
|
null | null |
{}
|
Ziq2525/gpt_fr_context
| null |
[
"region:us"
] | null |
2024-04-24T07:22:43+00:00
|
|
text-generation
|
transformers
|
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [tensorplex-labs/pretraining-sn9-7B-5](https://huggingface.co/tensorplex-labs/pretraining-sn9-7B-5)
* [tensorplex-labs/pretraining-sn9-7B-2](https://huggingface.co/tensorplex-labs/pretraining-sn9-7B-2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: tensorplex-labs/pretraining-sn9-7B-2
layer_range: [0, 30]
- model: tensorplex-labs/pretraining-sn9-7B-5
layer_range: [0, 30]
merge_method: slerp
base_model: tensorplex-labs/pretraining-sn9-7B-5
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.85
dtype: bfloat16
```
|
{"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["tensorplex-labs/pretraining-sn9-7B-5", "tensorplex-labs/pretraining-sn9-7B-2"]}
|
Sumail/zhun04
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:tensorplex-labs/pretraining-sn9-7B-5",
"base_model:tensorplex-labs/pretraining-sn9-7B-2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:24:37+00:00
|
text-generation
|
transformers
|
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Models Merged
The following models were included in the merge:
* [alpindale/WizardLM-2-8x22B](https://huggingface.co/alpindale/WizardLM-2-8x22B)
* [HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1)
## Benchmark results
### 1. MT-Bench from lmsys
We adapted the code from [FastChat](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) to benchmark our model with GPT-4 as a judge. Here is the result
```
########## First turn ##########
score
model turn
wizard-zephyr-8x22b 1 9.1625
########## Second turn ##########
score
model turn
wizard-zephyr-8x22b 2 8.873418
########## Average ##########
score
model
wizard-zephyr-8x22b 9.018868
```
The score is slightly lower than [alpindale/WizardLM-2-8x22B](https://huggingface.co/alpindale/WizardLM-2-8x22B), but still higher than GPT-4-0314. Then the research and experimental work still need to continue ^^
|
{"license": "cc-by-nc-sa-4.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["alpindale/WizardLM-2-8x22B", "HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1"]}
|
tlphams/Wizard-Zephyr-Orpo-8x22B
| null |
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:alpindale/WizardLM-2-8x22B",
"base_model:HuggingFaceH4/zephyr-orpo-141b-A35b-v0.1",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:24:44+00:00
|
object-detection
|
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. -->
# detr_v2_15
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) 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: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr_v2_15", "results": []}]}
|
ssamperr/detr_v2_15
| null |
[
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:24:50+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** akbargherbal
- **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"}
|
akbargherbal/think_tanks_v02_lora
| 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 |
2024-04-24T07:25:25+00:00
|
text-generation
|
transformers
|
{}
|
jobvector/SFT_Llama-2-7b-hf_0.0001_57373Data_addEOSToken_1600ChPt
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:25:30+00:00
|
|
null | null |
{}
|
nnheui/stablelm-2-1_6b-spin-dpo-2-full
| null |
[
"region:us"
] | null |
2024-04-24T07:26:10+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-14m_mz-130_IMDB_n-its-10-seed-3
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) 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
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_pythia-14m_mz-130_IMDB_n-its-10-seed-3", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-14m_mz-130_IMDB_n-its-10-seed-3
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:26:11+00:00
|
text-generation
|
transformers
|
# Model Card for Mistral-chem-v0.5 (mistral for chemistry)
The Mistral-chem-v0.5 Large Language Model (LLM) is a pretrained generative chemical molecule model with 52.11M parameters x 8 experts = 416.9M parameters.
It is derived from Mistral-7B-v0.1 model, which was simplified for chemistry: the number of layers and the hidden size were reduced.
The model was pretrained using around 100M molecule SMILES strings from the Zinc database.
For full details of this model please read our [github repo](https://github.com/raphaelmourad/Mistral-chem).
## Model Architecture
Like Mistral-7B-v0.1, it is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Load the model from huggingface:
```
import torch
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("RaphaelMourad/Mistral-chem-v0.5", trust_remote_code=True)
model = AutoModel.from_pretrained("RaphaelMourad/Mistral-chem-v0.5", trust_remote_code=True)
```
## Calculate the embedding of a DNA sequence
```
chem = "CCCCC[C@H](Br)CC"
inputs = tokenizer(chem, return_tensors = 'pt')["input_ids"]
hidden_states = model(inputs)[0] # [1, sequence_length, 256]
# embedding with max pooling
embedding_max = torch.max(hidden_states[0], dim=0)[0]
print(embedding_max.shape) # expect to be 256
```
## Troubleshooting
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
## Notice
Mistral-chem is a pretrained base model for chemistry.
## Contact
Raphaël Mourad. [email protected]
|
{"license": "apache-2.0", "tags": ["pretrained", "Mistral", "chemistry"]}
|
RaphaelMourad/mixtral-chem-v0.5
| null |
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"pretrained",
"Mistral",
"chemistry",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:27:44+00:00
|
null |
transformers
|
## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/meraGPT/mera-mix-4x7B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/mera-mix-4x7B-i1-GGUF
## 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/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q2_K.gguf) | Q2_K | 8.9 | |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.IQ3_XS.gguf) | IQ3_XS | 10.0 | |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q3_K_S.gguf) | Q3_K_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.IQ3_S.gguf) | IQ3_S | 10.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.IQ3_M.gguf) | IQ3_M | 10.7 | |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q3_K_M.gguf) | Q3_K_M | 11.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q3_K_L.gguf) | Q3_K_L | 12.6 | |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.IQ4_XS.gguf) | IQ4_XS | 13.1 | |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q4_K_S.gguf) | Q4_K_S | 13.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q4_K_M.gguf) | Q4_K_M | 14.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q5_K_S.gguf) | Q5_K_S | 16.7 | |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q5_K_M.gguf) | Q5_K_M | 17.2 | |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q6_K.gguf) | Q6_K | 19.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/mera-mix-4x7B-GGUF/resolve/main/mera-mix-4x7B.Q8_0.gguf) | Q8_0 | 25.8 | 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": "apache-2.0", "library_name": "transformers", "base_model": "meraGPT/mera-mix-4x7B", "quantized_by": "mradermacher"}
|
mradermacher/mera-mix-4x7B-GGUF
| null |
[
"transformers",
"gguf",
"en",
"base_model:meraGPT/mera-mix-4x7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:28:35+00:00
|
null | null |
¿Qué es Crystalin tabletas?
Crystalin Precio es una cápsula de suplemento dietético de primera calidad, meticulosamente elaborada para brindar un apoyo integral a la salud ocular. Su fórmula avanzada contiene una mezcla sinérgica de vitaminas, minerales y antioxidantes elegidos específicamente para nutrir los ojos y protegerlos contra el estrés oxidativo.
Página web oficial:<a href="https://www.nutritionsee.com/Crystaseucdor">www.Crystalin.com</a>
<p><a href="https://www.nutritionsee.com/Crystaseucdor"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/04/Crystalin-Ecuador-1.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/Crystaseucdor">¡¡Comprar ahora!! Haga clic en el enlace a continuación para obtener más información y obtener un 50% de descuento ahora... ¡Date prisa!</a>
Página web oficial:<a href="https://www.nutritionsee.com/Crystaseucdor">www.Crystalin.com</a>
|
{"license": "apache-2.0"}
|
CrystalinEcuador/Crystalin
| null |
[
"license:apache-2.0",
"region:us"
] | null |
2024-04-24T07:29:40+00:00
|
token-classification
|
transformers
|
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-G3
This model is a fine-tuned version of [ChakuChidiya/distilbert-base-uncased-G2](https://huggingface.co/ChakuChidiya/distilbert-base-uncased-G2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2192
- Validation Loss: 0.3240
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1920, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.07}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3628 | 0.3204 | 0 |
| 0.2708 | 0.3328 | 1 |
| 0.2192 | 0.3240 | 2 |
### Framework versions
- Transformers 4.37.0
- TensorFlow 2.15.0
- Datasets 2.14.5
- Tokenizers 0.15.1
|
{"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "ChakuChidiya/distilbert-base-uncased-G2", "model-index": [{"name": "distilbert-base-uncased-G3", "results": []}]}
|
ChakuChidiya/distilbert-base-uncased-G3
| null |
[
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"base_model:ChakuChidiya/distilbert-base-uncased-G2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:31:05+00:00
|
text2text-generation
|
transformers
|
# PLLaVA Model Card
## Model details
**Model type:**
PLLaVA-7B is an open-source video-language chatbot trained by fine-tuning Image-LLM on video instruction-following data. It is an auto-regressive language model, based on the transformer architecture. Base LLM: llava-hf/llava-v1.6-vicuna-7b-hf
**Model date:**
PLLaVA-7B was trained in April 2024.
**Paper or resources for more information:**
- github repo: https://github.com/magic-research/PLLaVA
- project page: https://pllava.github.io/
- paper link: https://arxiv.org/abs/2404.16994
## License
llava-hf/llava-v1.6-vicuna-7b-hf license.
**Where to send questions or comments about the model:**
https://github.com/magic-research/PLLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of PLLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
Video-Instruct-Tuning data of OpenGVLab/VideoChat2-IT
## Evaluation dataset
A collection of 6 benchmarks, including 5 VQA benchmarks and 1 recent benchmarks specifically proposed for Video-LMMs.
|
{"license": "apache-2.0", "tags": ["video LLM"], "datasets": ["OpenGVLab/VideoChat2-IT"]}
|
ermu2001/pllava-7b
| null |
[
"transformers",
"safetensors",
"llava",
"text2text-generation",
"video LLM",
"dataset:OpenGVLab/VideoChat2-IT",
"arxiv:2404.16994",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
] | null |
2024-04-24T07:31:24+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": []}
|
josianem/adareceipts-donut-model-cordv2
| null |
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:31:36+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** Anpur-Phani
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit
This gemma 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", "gemma", "trl"], "base_model": "unsloth/gemma-2b-it-bnb-4bit"}
|
Anpur-Phani/gemma_lora_model
| null |
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:33:43+00:00
|
null | null |
{"license": "mit"}
|
imvbhuvan/falcon-aspireai
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-24T07:34:21+00:00
|
|
null | null |
{}
|
baotl/test_01
| null |
[
"region:us"
] | null |
2024-04-24T07:34:46+00:00
|
|
visual-question-answering
|
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. -->
# vilt_finetuned_200
This model is a fine-tuned version of [dandelin/vilt-b32-mlm](https://huggingface.co/dandelin/vilt-b32-mlm) on the vqa 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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["vqa"], "base_model": "dandelin/vilt-b32-mlm", "model-index": [{"name": "vilt_finetuned_200", "results": []}]}
|
yeongha/vilt_finetuned_200
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vilt",
"visual-question-answering",
"generated_from_trainer",
"dataset:vqa",
"base_model:dandelin/vilt-b32-mlm",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:36:16+00:00
|
null | null |
---
license: apache-2.0
JALKJGLAJGLJAPGJLAKJEDG
|
{"language": ["aa"]}
|
xumeng/888
| null |
[
"aa",
"region:us"
] | null |
2024-04-24T07:36:21+00:00
|
null | null |
{}
|
curiosity29/test_diffusion_24_4
| null |
[
"region:us"
] | null |
2024-04-24T07:36:44+00:00
|
|
null | null |
{}
|
paraffa/melotts-model-yoo-v1
| null |
[
"region:us"
] | null |
2024-04-24T07:37:27+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. -->
# billm_conll2003_NousResearch-Llama-2-7b-hf_ckpt
This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) 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: 0.0009
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
{"library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "NousResearch/Llama-2-7b-hf", "model-index": [{"name": "billm_conll2003_NousResearch-Llama-2-7b-hf_ckpt", "results": []}]}
|
Farjfar/billm_conll2003_NousResearch-Llama-2-7b-hf_ckpt
| null |
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:NousResearch/Llama-2-7b-hf",
"region:us"
] | null |
2024-04-24T07:37:31+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": []}
|
heyllm234/sc73
| null |
[
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:38:12+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. -->
# ppi_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4819
- Accuracy: 0.9333
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5692 | 1.0 | 53424 | 0.4819 | 0.9333 |
### Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert/distilbert-base-uncased", "model-index": [{"name": "ppi_model", "results": []}]}
|
lamiaaMB/ppi_model
| null |
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:39:11+00:00
|
null | null |
{}
|
Moon-Ahn/phi2_q4f16-MLC
| null |
[
"region:us"
] | null |
2024-04-24T07:39:19+00:00
|
|
text-to-image
|
diffusers
|
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Text-to-image finetuning - happynear/sdxl-pokemon-model
This pipeline was finetuned from **stabilityai/stable-diffusion-xl-base-1.0** on the **reach-vb/pokemon-blip-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompt: a cute Sundar Pichai creature:
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
{"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers-training", "diffusers", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "diffusers-training", "diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "inference": true}
|
happynear/sdxl-pokemon-model
| null |
[
"diffusers",
"safetensors",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"diffusers-training",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null |
2024-04-24T07:39:48+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/engu-st-mde
| null |
[
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:39:54+00:00
|
null |
transformers
|
# DavidAU/Antler-7B-Novel-Writing-Q6_K-GGUF
This model was converted to GGUF format from [`Aratako/Antler-7B-Novel-Writing`](https://huggingface.co/Aratako/Antler-7B-Novel-Writing) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Aratako/Antler-7B-Novel-Writing) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/Antler-7B-Novel-Writing-Q6_K-GGUF --model antler-7b-novel-writing.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/Antler-7B-Novel-Writing-Q6_K-GGUF --model antler-7b-novel-writing.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m antler-7b-novel-writing.Q6_K.gguf -n 128
```
|
{"language": ["ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["not-for-all-audiences", "nsfw", "llama-cpp", "gguf-my-repo"], "datasets": ["Aratako/Syosetu711K-Cleaned-158K-Instruct"], "base_model": ["Elizezen/Antler-7B"]}
|
DavidAU/Antler-7B-Novel-Writing-Q6_K-GGUF
| null |
[
"transformers",
"gguf",
"not-for-all-audiences",
"nsfw",
"llama-cpp",
"gguf-my-repo",
"ja",
"dataset:Aratako/Syosetu711K-Cleaned-158K-Instruct",
"base_model:Elizezen/Antler-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:40:01+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** srikar-v05
- **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"}
|
srikar-v05/llama3-ChatDoctor
| 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 |
2024-04-24T07:40:48+00:00
|
null | null |
{"license": "mit"}
|
Gauravkj012002/project11
| null |
[
"license:mit",
"region:us"
] | null |
2024-04-24T07:41:29+00:00
|
|
null | null |
{}
|
NapthaAI/moar_agents_prediction
| null |
[
"region:us"
] | null |
2024-04-24T07:41:45+00:00
|
|
text-generation
|
transformers
|
{}
|
snunlp/continual_llama
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:42:28+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-31m_mz-130_IMDB_n-its-10-seed-1
This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) 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
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-130_IMDB_n-its-10-seed-1", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-31m_mz-130_IMDB_n-its-10-seed-1
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-31m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:42:34+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. -->
# vietnamese-news-summarization-vistral-7b
This model is a fine-tuned version of [Viet-Mistral/Vistral-7B-Chat](https://huggingface.co/Viet-Mistral/Vistral-7B-Chat) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8576
## 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-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 0.03
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0431 | 0.0060 | 20 | 2.0914 |
| 2.0513 | 0.0119 | 40 | 2.0405 |
| 2.0366 | 0.0179 | 60 | 1.9899 |
| 1.946 | 0.0238 | 80 | 1.9301 |
| 1.9324 | 0.0298 | 100 | 1.8576 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.16.0
- Tokenizers 0.19.1
|
{"license": "afl-3.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "Viet-Mistral/Vistral-7B-Chat", "model-index": [{"name": "vietnamese-news-summarization-vistral-7b", "results": []}]}
|
anhvu2501/vietnamese-news-summarization-vistral-7b
| null |
[
"peft",
"safetensors",
"mistral",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:Viet-Mistral/Vistral-7B-Chat",
"license:afl-3.0",
"region:us"
] | null |
2024-04-24T07:43:20+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** aidiary
- **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"}
|
aidiary/llama3-8b-alpaca-finetuned
| 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 |
2024-04-24T07:43:22+00:00
|
null |
transformers
|
# Uploaded model
- **Developed by:** akbargherbal
- **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"}
|
akbargherbal/think_tanks_v02_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 |
2024-04-24T07:44:06+00:00
|
null |
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. -->
# PolizzeDonut-UltimaProvaCluster-Cluster3di7-5epochs
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
{"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "PolizzeDonut-UltimaProvaCluster-Cluster3di7-5epochs", "results": []}]}
|
tedad09/PolizzeDonut-UltimaProvaCluster-Cluster3di7-5epochs
| null |
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:44:19+00:00
|
text-classification
|
transformers
|
{"language": ["en"], "library_name": "transformers", "datasets": ["ifmain/text-moderation"]}
|
invalidexception/safetybert
| null |
[
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:ifmain/text-moderation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:44:20+00:00
|
|
null |
transformers
|
# DavidAU/llama-3-dragonmaid-8B-Q8_0-GGUF
This model was converted to GGUF format from [`nbeerbower/llama-3-dragonmaid-8B`](https://huggingface.co/nbeerbower/llama-3-dragonmaid-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/nbeerbower/llama-3-dragonmaid-8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/llama-3-dragonmaid-8B-Q8_0-GGUF --model llama-3-dragonmaid-8b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/llama-3-dragonmaid-8B-Q8_0-GGUF --model llama-3-dragonmaid-8b.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-dragonmaid-8b.Q8_0.gguf -n 128
```
|
{"license": "other", "library_name": "transformers", "tags": ["nsfw", "not-for-all-audiences", "experimental", "llama-cpp", "gguf-my-repo"], "datasets": ["ResplendentAI/NSFW_RP_Format_NoQuote"], "base_model": ["nbeerbower/llama-3-sauce-v1-8B"], "license_name": "llama3"}
|
DavidAU/llama-3-dragonmaid-8B-Q8_0-GGUF
| null |
[
"transformers",
"gguf",
"nsfw",
"not-for-all-audiences",
"experimental",
"llama-cpp",
"gguf-my-repo",
"dataset:ResplendentAI/NSFW_RP_Format_NoQuote",
"base_model:nbeerbower/llama-3-sauce-v1-8B",
"license:other",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:44:31+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-31m_mz-130_IMDB_n-its-10-seed-0
This model is a fine-tuned version of [EleutherAI/pythia-31m](https://huggingface.co/EleutherAI/pythia-31m) 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: 0
- 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
|
{"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-31m", "model-index": [{"name": "robust_llm_pythia-31m_mz-130_IMDB_n-its-10-seed-0", "results": []}]}
|
AlignmentResearch/robust_llm_pythia-31m_mz-130_IMDB_n-its-10-seed-0
| null |
[
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-31m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2024-04-24T07:44:34+00:00
|
null | null |
{}
|
gingercake01/repo005medium
| null |
[
"region:us"
] | null |
2024-04-24T07:45:26+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": []}
|
chohi/llama-3-8b-chat-molit-kor
| null |
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null |
2024-04-24T07:46:50+00:00
|
null |
keras
|
{"language": ["en"], "license": "mit", "library_name": "keras", "tags": ["code"]}
|
PuranjayB/CrashAware
| null |
[
"keras",
"code",
"en",
"license:mit",
"region:us"
] | null |
2024-04-24T07:47:11+00:00
|
|
null | null |
Q6_K gguf of https://huggingface.co/xxx777xxxASD/ChaoticSoliloquy-4x8B
|
{}
|
JayhC/ChaoticSoliloquy-4x8B-GGUF-Q6_K
| null |
[
"gguf",
"region:us"
] | null |
2024-04-24T07:47:17+00:00
|
text-generation
|
transformers
|
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
{"license": "apache-2.0"}
|
GreenBitAI/Phi-3-mini-4k-instruct-layer-mix-bpw-3.0
| null |
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:47:32+00:00
|
text-generation
|
transformers
|
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
{"license": "apache-2.0"}
|
GreenBitAI/Phi-3-mini-4k-instruct-layer-mix-bpw-2.5
| null |
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:47:45+00:00
|
text-generation
|
transformers
|
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
{"license": "apache-2.0"}
|
GreenBitAI/Phi-3-mini-4k-instruct-layer-mix-bpw-2.2
| null |
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:47:56+00:00
|
text-generation
|
transformers
|
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
{"license": "apache-2.0"}
|
GreenBitAI/Phi-3-mini-128k-instruct-layer-mix-bpw-2.2
| null |
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:48:04+00:00
|
text-generation
|
transformers
|
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
{"license": "apache-2.0"}
|
GreenBitAI/Phi-3-mini-128k-instruct-layer-mix-bpw-2.5
| null |
[
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2024-04-24T07:48:13+00:00
|
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