modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
NLPmonster/layoutlmv3-for-complete-receipt-understanding | NLPmonster | 2024-10-20T03:14:06Z | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:NLPmonster/layoutlmv3-for-receipt-understanding",
"base_model:finetune:NLPmonster/layoutlmv3-for-receipt-understanding",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-10-06T03:16:16Z | ---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: NLPmonster/layoutlmv3-for-receipt-understanding
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: layoutlmv3-for-complete-receipt-understanding
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# layoutlmv3-for-complete-receipt-understanding
This model is a fine-tuned version of [NLPmonster/layoutlmv3-for-receipt-understanding](https://huggingface.co/NLPmonster/layoutlmv3-for-receipt-understanding) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4421
- Precision: 0.6502
- Recall: 0.6607
- F1: 0.6554
- Accuracy: 0.8760
## 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: 5
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 1.2604 | 0.4673 | 50 | 0.6573 | 0.5077 | 0.4747 | 0.4906 | 0.7863 |
| 0.5256 | 0.9346 | 100 | 0.4824 | 0.5732 | 0.6521 | 0.6101 | 0.8465 |
| 0.4529 | 1.4019 | 150 | 0.4461 | 0.5926 | 0.6509 | 0.6204 | 0.8464 |
| 0.3767 | 1.8692 | 200 | 0.4265 | 0.5747 | 0.6740 | 0.6204 | 0.8492 |
| 0.3403 | 2.3364 | 250 | 0.4557 | 0.5564 | 0.6083 | 0.5812 | 0.8451 |
| 0.331 | 2.8037 | 300 | 0.4065 | 0.6384 | 0.6671 | 0.6524 | 0.8669 |
| 0.2984 | 3.2710 | 350 | 0.3820 | 0.6411 | 0.6411 | 0.6411 | 0.8729 |
| 0.2763 | 3.7383 | 400 | 0.4078 | 0.6104 | 0.6037 | 0.6070 | 0.8576 |
| 0.2626 | 4.2056 | 450 | 0.4203 | 0.6268 | 0.6164 | 0.6216 | 0.8589 |
| 0.2456 | 4.6729 | 500 | 0.3960 | 0.6240 | 0.6406 | 0.6322 | 0.8686 |
| 0.2078 | 5.1402 | 550 | 0.4074 | 0.6401 | 0.6290 | 0.6345 | 0.8709 |
| 0.1859 | 5.6075 | 600 | 0.3853 | 0.6511 | 0.6601 | 0.6556 | 0.8733 |
| 0.2059 | 6.0748 | 650 | 0.3845 | 0.6539 | 0.6509 | 0.6524 | 0.8772 |
| 0.1649 | 6.5421 | 700 | 0.4128 | 0.6298 | 0.6486 | 0.6390 | 0.8706 |
| 0.1599 | 7.0093 | 750 | 0.4328 | 0.6302 | 0.6578 | 0.6437 | 0.8644 |
| 0.1437 | 7.4766 | 800 | 0.4100 | 0.6510 | 0.6469 | 0.6489 | 0.8727 |
| 0.1377 | 7.9439 | 850 | 0.4409 | 0.6317 | 0.6699 | 0.6503 | 0.8711 |
| 0.1164 | 8.4112 | 900 | 0.4331 | 0.6301 | 0.6642 | 0.6467 | 0.8717 |
| 0.1149 | 8.8785 | 950 | 0.4523 | 0.6466 | 0.6555 | 0.6510 | 0.8712 |
| 0.1096 | 9.3458 | 1000 | 0.4421 | 0.6502 | 0.6607 | 0.6554 | 0.8760 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
Nutanix/llama-30b_checkpoint-6500_20241020-024234-merged | Nutanix | 2024-10-20T02:52:52Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T02:43:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
fmb-quibdo/fmb-qwen-vl-7b | fmb-quibdo | 2024-10-20T02:52:14Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-10-19T03:10:07Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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] |
tjake/Yi-Coder-1.5B-Chat-JQ4 | tjake | 2024-10-20T02:47:28Z | 130 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2403.04652",
"base_model:01-ai/Yi-Coder-1.5B",
"base_model:finetune:01-ai/Yi-Coder-1.5B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T02:44:59Z | ---
license: apache-2.0
library_name: transformers
base_model: 01-ai/Yi-Coder-1.5B
---
# Quantized Version of 01-ai/Yi-Coder-1.5B-Chat
This model is a quantized variant of the 01-ai/Yi-Coder-1.5B-Chat model, optimized for use with Jlama, a Java-based inference engine. The quantization process reduces the model's size and improves inference speed, while maintaining high accuracy for efficient deployment in production environments.
For more information on Jlama, visit the [Jlama GitHub repository](https://github.com/tjake/jlama).
---
<div align="center">
<picture>
<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="120px">
</picture>
</div>
<p align="center">
<a href="https://github.com/01-ai">🐙 GitHub</a> •
<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
<br/>
<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
<a href="https://01-ai.github.io/">💪 Tech Blog</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
</p>
# Intro
Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
Key features:
- Excelling in long-context understanding with a maximum context length of 128K tokens.
- Supporting 52 major programming languages:
```bash
'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
```
For model details and benchmarks, see [Yi-Coder blog](https://01-ai.github.io/) and [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
<p align="left">
<img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/yi-coder-calculator-demo.gif?raw=true" alt="demo1" width="500"/>
</p>
# Models
| Name | Type | Length | Download |
|--------------------|------|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------|
| Yi-Coder-9B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B-Chat) |
| Yi-Coder-1.5B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B-Chat) |
| Yi-Coder-9B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B) |
| Yi-Coder-1.5B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B) |
| |
# Benchmarks
As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%.
<p align="left">
<img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/bench1.webp?raw=true" alt="bench1" width="1000"/>
</p>
# Quick Start
You can use transformers to run inference with Yi-Coder models (both chat and base versions) as follows:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
device = "cuda" # the device to load the model onto
model_path = "01-ai/Yi-Coder-9B-Chat"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
prompt = "Write a quick sort algorithm."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=1024,
eos_token_id=tokenizer.eos_token_id
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```
For getting up and running with Yi-Coder series models quickly, see [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
|
jessie184/bart-cnn-samsun-summarizer | jessie184 | 2024-10-20T02:38:39Z | 104 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:facebook/bart-large-cnn",
"base_model:finetune:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-20T02:37:53Z | ---
library_name: transformers
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: bart-cnn-samsun-summarizer
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-cnn-samsun-summarizer
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8894
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 19 | 1.8120 |
| No log | 2.0 | 38 | 1.8061 |
| No log | 3.0 | 57 | 1.8463 |
| No log | 4.0 | 76 | 1.8817 |
| No log | 5.0 | 95 | 1.8894 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
qucklecrabik/model | qucklecrabik | 2024-10-20T02:23:10Z | 17 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:IlyaGusev/saiga_llama3_8b",
"base_model:quantized:IlyaGusev/saiga_llama3_8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-20T02:14:05Z | ---
base_model: IlyaGusev/saiga_llama3_8b
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** qucklecrabik
- **License:** apache-2.0
- **Finetuned from model :** IlyaGusev/saiga_llama3_8b
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)
|
EliteAide/ea-llama-1b | EliteAide | 2024-10-20T02:22:33Z | 137 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T02:21:53Z | ---
library_name: transformers
tags:
- unsloth
---
# 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] |
magnifi/parser_user_v25a_epoch_7_lr_0.002 | magnifi | 2024-10-20T01:56:41Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-20T01:54:21Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** magnifi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
vocabtrimmer/camembert-base.xnli-fr.1 | vocabtrimmer | 2024-10-20T01:47:06Z | 5 | 0 | null | [
"safetensors",
"camembert",
"region:us"
] | null | 2024-10-20T01:46:51Z | # `vocabtrimmer/camembert-base.xnli-fr.1`
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on the
[xnli](https://huggingface.co/datasets/xnli) (fr).
Following metrics are computed on the `test` split of
[xnli](https://huggingface.co/datasets/xnli)(fr).
* Evaluation on test split
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 33.31 | 33.31 | 33.31 | 18.87 | 33.31 | 22.16 | 33.31 |
* Evaluation on validation split
| | eval_f1_micro | eval_recall_micro | eval_precision_micro | eval_f1_macro | eval_recall_macro | eval_precision_macro | eval_accuracy |
|---:|----------------:|--------------------:|-----------------------:|----------------:|--------------------:|-----------------------:|----------------:|
| 0 | 33.25 | 33.25 | 33.25 | 18.66 | 33.25 | 21.63 | 33.25 |
Check the result file [here](https://huggingface.co/vocabtrimmer/camembert-base.xnli-fr.1/raw/main/eval.json). |
Cran-May/Gemma2-9B-IT-Simpo-Infinity-Preference-Q5_K_M-GGUF | Cran-May | 2024-10-20T01:16:10Z | 5 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:BAAI/Infinity-Instruct",
"base_model:BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference",
"base_model:quantized:BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-10-20T01:15:40Z | ---
datasets:
- BAAI/Infinity-Instruct
language:
- en
base_model: BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference
tags:
- llama-cpp
- gguf-my-repo
---
# Cran-May/Gemma2-9B-IT-Simpo-Infinity-Preference-Q5_K_M-GGUF
This model was converted to GGUF format from [`BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference`](https://huggingface.co/BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference) 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/BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Cran-May/Gemma2-9B-IT-Simpo-Infinity-Preference-Q5_K_M-GGUF --hf-file gemma2-9b-it-simpo-infinity-preference-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Cran-May/Gemma2-9B-IT-Simpo-Infinity-Preference-Q5_K_M-GGUF --hf-file gemma2-9b-it-simpo-infinity-preference-q5_k_m-imat.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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Cran-May/Gemma2-9B-IT-Simpo-Infinity-Preference-Q5_K_M-GGUF --hf-file gemma2-9b-it-simpo-infinity-preference-q5_k_m-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Cran-May/Gemma2-9B-IT-Simpo-Infinity-Preference-Q5_K_M-GGUF --hf-file gemma2-9b-it-simpo-infinity-preference-q5_k_m-imat.gguf -c 2048
```
|
l3xx/pinkcowshirt | l3xx | 2024-10-20T01:10:37Z | 5 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-20T01:10:10Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/pinkcowshirt_001000_01_20241020030442.png
text: Woman wearing C0wsh1rt --d 42
- output:
url: sample/pinkcowshirt_000900_01_20241020030129.png
text: Woman wearing C0wsh1rt infront a cow on a footballfield--d 42
- output:
url: sample/pinkcowshirt_001000_02_20241020030627.png
text: Woman wearing C0wsh1rt infront a cow on a footballfield--d 42
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: C0wsh1rt
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# pinkcowshirt
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `C0wsh1rt` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
thelinh246/distilhubert-finetuned-gtzan | thelinh246 | 2024-10-20T00:48:59Z | 162 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-10-17T03:58:48Z | ---
library_name: transformers
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.82
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5947
- Accuracy: 0.82
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.9587 | 1.0 | 113 | 1.8455 | 0.55 |
| 1.2447 | 2.0 | 226 | 1.2839 | 0.63 |
| 0.9662 | 3.0 | 339 | 0.9868 | 0.74 |
| 0.7659 | 4.0 | 452 | 0.8618 | 0.78 |
| 0.5515 | 5.0 | 565 | 0.7399 | 0.81 |
| 0.4546 | 6.0 | 678 | 0.6570 | 0.78 |
| 0.3163 | 7.0 | 791 | 0.6416 | 0.81 |
| 0.1437 | 8.0 | 904 | 0.6190 | 0.81 |
| 0.1568 | 9.0 | 1017 | 0.5887 | 0.82 |
| 0.1134 | 10.0 | 1130 | 0.5947 | 0.82 |
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
MrGohlke/ID_CTI_Llama70B_v2.1 | MrGohlke | 2024-10-20T00:47:09Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T22:01:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
TheAwakenOne/max-headroom | TheAwakenOne | 2024-10-20T00:18:18Z | 80 | 1 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-17T02:43:34Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/max-headroom_000900_00_20241015234926.png
text: >-
M2X a stylized retro Albert Einstein with dark sunglasses, wearing a green
suit, set against a neon background. He has a confident and cheerful
expression, suggesting charisma and charm in a retro-futuristic 80's
aesthetic
- output:
url: sample/max-headroom_000900_01_20241015235139.png
text: >-
M2X a close-up of a retro John wick character with slicked-back hair and a
confident smile. He is set against a background of dynamic, colorful neon
lines, emphasizing his bold and charismatic presence in a retro-80s vibe
- output:
url: sample/max-headroom_000900_02_20241015235352.png
text: >-
M2X retro animated character Ronald McDonald appears on a vintage television
screen, wearing dark sunglasses and a shiny brown suit. The background
features vibrant red and blue neon diagonal stripes, enhancing the
retro-vibe
- output:
url: sample/max-headroom_000900_03_20241015235606.png
text: >-
M2X a stylized, retro animated character with slicked-back long hair and
oversized sunglasses, wearing a blue suit and red bow tie. He stands in
front of a neon background, with an exaggeratedly enthusiastic expression,
shouting "AWKN!". The design captures a vibrant 80s aesthetic
- text: >-
M2X a stylized, retro animated character slim shady with slicked-back black
hair and oversized sunglasses, wearing a shiny blue suit. He stands in front
of a neon background, with an exaggeratedly enthusiastic expression,
shouting "Wow!". The design captures a vibrant 80s aesthetic
output:
url: images/example_gm7jfx8a1.png
- text: >-
M2X side profile of the animated character with platinum blonde hair and
sunglasses, dressed in a black suit. The colorful neon background enhances
the character's cool demeanor, representative of 80s aesthetic
output:
url: images/example_xe72ibs5x.png
- text: >-
M2X retro animated character Jack in the box appears on a vintage television
screen, wearing dark sunglasses and a shiny suit. The background features
vibrant red and blue neon diagonal stripes, enhancing the retro-futuristic
vibe
output:
url: images/example_lkpk5hjg8.png
- text: >-
M2X a stylized, retro animated character with slicked-back long hair and
oversized sunglasses, wearing a blue suit and red bow tie. He stands in
front of a neon background, with an exaggeratedly enthusiastic expression,
shouting "AWKN!". The design captures a vibrant 80s aesthetic
output:
url: images/example_q4uv7trqg.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: M2X
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# Max-Headroom
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `M2X` to trigger the image generation.
Added Max-Headroom-v1, this one has a better resolution than the original.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
TheAwakenOne/caricature | TheAwakenOne | 2024-10-20T00:12:25Z | 222 | 10 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-08T14:35:04Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/caricature_001500_00_20241007165438.png
text: >-
CCTUR3 a balding middle-aged businessman with an enormous forehead, tiny
eyes, and a chin that could cut glass, struggling to balance a comically
small briefcase on his oversized nose
- output:
url: sample/caricature_001500_01_20241007165656.png
text: >-
CCTUR3 a glamorous movie star with voluminous hair twice the size of her
head, eyes like saucers, and lips that take up half her face, trying to fit
through a standard doorway
- output:
url: sample/caricature_001500_02_20241007165913.png
text: >-
CCTUR3 a newlywed couple posing for a photo - the bride with a neck as long
as a giraffe's and a beehive hairdo that reaches the clouds, the groom with
ears like satellite dishes and a smile that literally stretches from ear to
ear
- output:
url: sample/caricature_001500_03_20241007170130.png
text: >-
CCTUR3 a basketball player with exaggerated facial features, dunking a ball
that looks like a pea in his massive hands
- text: >-
CCTUR3 Adorable couple with caricatured features share a loving moment, her
voluminous blonde hair and wide eyes complementing his gentle smile and
branded cap, both squeezed into a cozy oval frame
output:
url: images/example_f0pcy0qlz.png
- text: >-
CCTUR3 Enthusiastic Iron-man fan with exaggerated facial features beams with
joy, his cartoonishly large head merging seamlessly with a dynamic,
mechanical-slinging superhero body in mid-action pose
output:
url: images/example_gzbm8wswr.png
- text: >-
CCTUR3 Quirky female with fiery red hair and oversized glasses smirks
knowingly, holding a starbucks coffee in both hands while sporting a t-shirt
that reads “AWKN” and black and white doc martins
output:
url: images/example_bt02ts6jc.png
- text: >-
CCTUR3 Enthusiastic soccer player, his oversized head and exaggerated grin
dominating the frame as he hits a comical 'WAK!' with eyes glued to a
cartoonish soccer ball
output:
url: images/example_9vqvcef6g.png
- text: >-
CCTUR3 Radiant couple with megawatt smiles beams against a starry backdrop,
her flowing hair and his voluminous afro framing their joyful expressions in
matching white outfits, oversized heads, both squeezed into a cozy oval
frame
output:
url: images/example_2bx3l3k4k.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: CCTUR3
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# Caricature
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `CCTUR3` to trigger the image generation.
How to Use This LoRA
This LoRA is designed to generate exaggerated caricature-style portraits with oversized heads and emphasized facial features.
Key words to include in your prompts:
• Exaggerated
• Oversized head
• Emphasized features
• Comical
• Cartoonish
• Caricature style
When crafting your prompts, focus on describing specific facial features, expressions, and any contextual elements you want to include.
Sample prompts:
1. "CCTUR3, a jovial elderly man with an oversized bald head and exaggerated smile, explaining a complex algorithm on a whiteboard, surrounded by stacks of baked bean cans"
2. "CCTUR3, a quirky female detective with fiery orange hair, oversized glasses, and a knowing smirk, holding a magnifying glass and cigarette"
Remember to experiment with different combinations of features and scenarios to get the best results from this caricature LoRA, Enjoy!!
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
TheAwakenOne/ldlaughingmemeface | TheAwakenOne | 2024-10-20T00:11:46Z | 21 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-02T18:55:19Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/ldlaughingmemeface_000450_00_20241001223402.png
text: >-
LDME Young adult male at a party, mid-laugh with eyebrows raised in mock
surprise. He's holding a red solo cup and wearing a graphic t-shirt
- output:
url: sample/ldlaughingmemeface_000450_01_20241001223612.png
text: >-
LDME Close-up of a smirking businessman with arched eyebrows, squinting
eyes, and a slightly open mouth. He's holding a coffee mug in an office
setting
- output:
url: sample/ldlaughingmemeface_000450_02_20241001223822.png
text: >-
LDME middle-aged woman in a casual shirt, raising his eyebrows skeptically
while holding a martini
- text: >-
LDME a man at a theme park, holding an oversized stuffed animal prize with a
smirk and raised eyebrows, as if mocking the absurdity of his win
output:
url: images/example_5cqo9mg58.png
- text: >-
LDME a man in the kitchen, holding a burnt piece of toast with eyebrows
raised and mouth agape, as if questioning how he managed to mess up
breakfast so spectacularly
output:
url: images/example_hgpqvq2m6.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: LDME
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# LDLaughingMemeFace
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `LDME` to trigger the image generation.
How to Use This LoRA Model
Describing the Expression
To get the best results from this LoRA model, use descriptive words that capture the essence of the facial expressions you want to generate. Consider phrases like:
"exaggerated expression"
"raised eyebrows"
"as if mocking"
"eyebrows lifted and smirking"
These descriptors will help the model focus on creating the unique, expressive faces you're aiming for.
Adjusting LoRA Strength
Strength 1: At full strength, the LoRA model tends to generate images of the same character when you prompt with "a man." This can be useful if you want consistency in character appearance.
Dialing Down Strength: If you wish to create different characters or add variety, reduce the strength of the LoRA. A strength setting around 0.7 has been effective for me, but I encourage experimenting with different levels to achieve your desired outcome.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
TheAwakenOne/rockbrow | TheAwakenOne | 2024-10-20T00:10:12Z | 38 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-09-23T11:50:35Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/rockbrow_000600_00_20240923012101.png
text: PeopleBrow man with intense gaze, close-up portrait
- output:
url: sample/rockbrow_000600_01_20240923012312.png
text: PeopleBrow Albert Einstein with intense gaze while reading newspaper
- output:
url: sample/rockbrow_000600_02_20240923012526.png
text: >-
PeopleBrow cyberpunk detective, neon-lit rainy street, one eyebrow raised
suspiciously, futuristic cityscape background
- output:
url: sample/rockbrow_000600_03_20240923012739.png
text: >-
PeopleBrow ancient Greek philosopher, toga-clad, contemplative expression,
marble bust style
- text: >-
PeopleBrow circus ringmaster, eyebrow raised skeptically, big top tent,
colorful performers in background
output:
url: images/example_usequf03v.png
- text: >-
PeopleBrow a bald headed man sunglasses, with full beard smoking cigar at a
bar, one eyebrow raised skeptically
output:
url: images/example_eo56qmd76.png
- text: >-
PeopleBrow ancient warrior, one eyebrow raised skeptically, battle-worn
armor
output:
url: images/example_2gn91i8yr.png
- text: >-
PeopleBrow a man with a hat that reads "Can you Smelll", left eyebrow
raised dramatically arched,
output:
url: images/example_yarhn7jj5.png
- text: >-
PeopleBrow a luchado in the wrestling ring, one eyebrow raised suspiciously,
in the ring gazing at the crowed
output:
url: images/example_s6wksfyjk.png
- text: >-
PeopleBrow sponge bob, one eyebrow raised suspiciously, while eating a
crabby paddie
output:
url: images/example_l8g67vixh.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: PeopleBrow
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# rockbrow
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `PeopleBrow` to trigger the image generation.
To optimize the effectiveness of your "PeopleBrow" LoRA, it's recommended to incorporate one of the following descriptive phrases in your prompts:
• "intense gaze"
• "left eyebrow dramatically arched"
• "one eyebrow raised skeptically"
• "eyebrow raised in challenge"
Including these specific descriptions will help guide the AI to generate images that capture The Rock's iconic eyebrow expressions more accurately. Here are some example prompts using this approach:
1. "PeopleBrow ancient warrior, one eyebrow raised skeptically, battle-worn armor"
2. "PeopleBrow scientist in lab coat, left eyebrow dramatically arched, futuristic laboratory"
3. "PeopleBrow fantasy elf, intense gaze, forest background, magical aura"
4. "PeopleBrow corporate executive, eyebrow raised in challenge, modern office setting"
By combining the "PeopleBrow" trigger word with these descriptive phrases, you're likely to achieve more consistent and characteristic results that showcase The Rock's famous eyebrow expressions across various subjects and scenarios.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
Marcusxx/cheonanAddresses_torch_large_model_model | Marcusxx | 2024-10-19T23:51:07Z | 7 | 0 | null | [
"tensorboard",
"safetensors",
"whisper",
"hf-asr-leaderboard",
"generated_from_trainer",
"ko",
"dataset:Marcusxx/cheonanAddresses",
"base_model:openai/whisper-large",
"base_model:finetune:openai/whisper-large",
"license:apache-2.0",
"region:us"
] | null | 2024-10-19T03:32:01Z | ---
base_model: openai/whisper-large
datasets:
- Marcusxx/cheonanAddresses
language:
- ko
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: cheonanAddresses_torch_large_model_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cheonanAddresses_torch_large_model_model
This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the Marcusxx/cheonanAddresses dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0534
- Cer: 1.8909
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.0757 | 0.3101 | 1000 | 0.0772 | 2.2598 |
| 0.0714 | 0.6202 | 2000 | 0.0720 | 2.3372 |
| 0.0624 | 0.9302 | 3000 | 0.0676 | 2.2765 |
| 0.0583 | 1.2403 | 4000 | 0.0642 | 2.2266 |
| 0.0611 | 1.5504 | 5000 | 0.0604 | 2.0681 |
| 0.0552 | 1.8605 | 6000 | 0.0590 | 2.1282 |
| 0.0497 | 2.1705 | 7000 | 0.0569 | 2.0233 |
| 0.0423 | 2.4806 | 8000 | 0.0560 | 1.9835 |
| 0.0495 | 2.7907 | 9000 | 0.0538 | 1.9387 |
| 0.0364 | 3.1008 | 10000 | 0.0534 | 1.8909 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.2.2+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
Aranya31/llava_7cls_ep5_merged | Aranya31 | 2024-10-19T22:44:06Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"llava",
"image-text-to-text",
"conversational",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-10-19T22:38:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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] |
yjwon/ub_llama3b_sft | yjwon | 2024-10-19T22:15:07Z | 165 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T22:11:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
dattrong/pixart-sigma-256 | dattrong | 2024-10-19T22:13:20Z | 11 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"PixArt-Σ",
"license:openrail++",
"diffusers:PixArtSigmaPipeline",
"region:us"
] | text-to-image | 2024-10-19T21:06:39Z | ---
license: openrail++
tags:
- text-to-image
- PixArt-Σ
---
THIS IS A REDISTRIBUTION OF PIXART-Σ-XL-256x256
### 🧨 Diffusers
> [!IMPORTANT]
> Make sure to upgrade diffusers to >= 0.28.0:
> ```bash
> pip install -U diffusers --upgrade
> ```
> In addition make sure to install `transformers`, `safetensors`, `sentencepiece`, and `accelerate`:
> ```
> pip install transformers accelerate safetensors sentencepiece
> ```
> For `diffusers<0.28.0`, check this [script](https://github.com/PixArt-alpha/PixArt-sigma#2-integration-in-diffusers) for help.
To just use the base model, you can run:
```python
import torch
from diffusers import Transformer2DModel, PixArtSigmaPipeline
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
weight_dtype = torch.float16
pipe = PixArtSigmaPipeline.from_pretrained(
"dattrong/pixart-sigma-512",
torch_dtype=weight_dtype,
use_safetensors=True,
)
pipe.to(device)
# Enable memory optimizations.
# pipe.enable_model_cpu_offload()
prompt = "A small cactus with a happy face in the Sahara desert."
image = pipe(prompt).images[0]
image.save("./catcus.png")
```
When using `torch >= 2.0`, you can improve the inference speed by 20-30% with torch.compile. Simple wrap the unet with torch compile before running the pipeline:
```py
pipe.transformer = torch.compile(pipe.transformer, mode="reduce-overhead", fullgraph=True)
```
If you are limited by GPU VRAM, you can enable *cpu offloading* by calling `pipe.enable_model_cpu_offload`
instead of `.to("cuda")`:
```diff
- pipe.to("cuda")
+ pipe.enable_model_cpu_offload()
```
|
merty/attempt4_7b_qwen2.5 | merty | 2024-10-19T22:01:25Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T21:48:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
johnbrennan/moon_itso_LR5e-4_BS4_512 | johnbrennan | 2024-10-19T21:56:58Z | 7 | 0 | diffusers | [
"diffusers",
"flux",
"flux-diffusers",
"text-to-image",
"simpletuner",
"safe-for-work",
"lora",
"template:sd-lora",
"lycoris",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-19T02:38:12Z | ---
license: other
base_model: "black-forest-labs/FLUX.1-dev"
tags:
- flux
- flux-diffusers
- text-to-image
- diffusers
- simpletuner
- safe-for-work
- lora
- template:sd-lora
- lycoris
inference: true
widget:
- text: 'unconditional (blank prompt)'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_0_0.png
- text: 'a hamster in the style of m00nl4nd1ng'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_1_0.png
- text: 'an astronaut hamster in the style of m00nl4nd1ng'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_2_0.png
- text: 'woman holding a sign that says ''I LOVE PROMPTS!'' in the style of m00nl4nd1ng'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_3_0.png
- text: 'a hipster man with a beard, building a chair in the style of m00nl4nd1ng'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_4_0.png
- text: 'Cat with lasers shooting out of its eyes in the style of m00nl4nd1ng'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_5_0.png
- text: 'sports event, athlete in motion, crowd blurred, flare from stadium lights in the style of m00nl4nd1ng'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_6_0.png
- text: 'a man holding a sign that says, ''this is a sign'''
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_7_0.png
- text: 'a pig, in a post apocalyptic world, with a shotgun, in a leather jacket, in a desert, with a motorcycle'
parameters:
negative_prompt: 'blurry, cropped, ugly'
output:
url: ./assets/image_8_0.png
---
# moon_itso_LR5e-4_BS4_512
This is a LyCORIS adapter derived from [black-forest-labs/FLUX.1-dev](https://huggingface.co/black-forest-labs/FLUX.1-dev).
No validation prompt was used during training.
None
## Validation settings
- CFG: `3.0`
- CFG Rescale: `0.0`
- Steps: `20`
- Sampler: `None`
- Seed: `22`
- Resolution: `1024x1024`
Note: The validation settings are not necessarily the same as the [training settings](#training-settings).
You can find some example images in the following gallery:
<Gallery />
The text encoder **was not** trained.
You may reuse the base model text encoder for inference.
## Training settings
- Training epochs: 28
- Training steps: 6000
- Learning rate: 0.0005
- Max grad norm: 2.0
- Effective batch size: 4
- Micro-batch size: 4
- Gradient accumulation steps: 1
- Number of GPUs: 1
- Prediction type: flow-matchingNone
- Rescaled betas zero SNR: False
- Optimizer: adamw_bf16
- Precision: Pure BF16
- Quantised: Yes: int8-quanto
- Xformers: Not used
- LyCORIS Config:
```json
{
"algo": "lokr",
"multiplier": 1.0,
"linear_dim": 10000,
"linear_alpha": 1,
"factor": 16,
"apply_preset": {
"target_module": [
"Attention",
"FeedForward"
],
"module_algo_map": {
"Attention": {
"factor": 16
},
"FeedForward": {
"factor": 8
}
}
}
}
```
## Datasets
### moon_itso_5e_5LR_512
- Repeats: 10
- Total number of images: 76
- Total number of aspect buckets: 1
- Resolution: 0.262144 megapixels
- Cropped: False
- Crop style: None
- Crop aspect: None
## Inference
```python
import torch
from diffusers import DiffusionPipeline
from lycoris import create_lycoris_from_weights
model_id = 'black-forest-labs/FLUX.1-dev'
adapter_id = 'pytorch_lora_weights.safetensors' # you will have to download this manually
lora_scale = 1.0
wrapper, _ = create_lycoris_from_weights(lora_scale, adapter_id, pipeline.transformer)
wrapper.merge_to()
prompt = "An astronaut is riding a horse through the jungles of Thailand."
pipeline.to('cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu')
image = pipeline(
prompt=prompt,
num_inference_steps=20,
generator=torch.Generator(device='cuda' if torch.cuda.is_available() else 'mps' if torch.backends.mps.is_available() else 'cpu').manual_seed(1641421826),
width=1024,
height=1024,
guidance_scale=3.0,
).images[0]
image.save("output.png", format="PNG")
```
|
playboy40k/flux-BillieEilishLora | playboy40k | 2024-10-19T21:36:30Z | 2,041 | 8 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"region:us"
] | text-to-image | 2024-09-16T13:09:14Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
Billie Eilish is depicted in a close-up portrait with the ocean breeze
gently flowing through her long black hair. Her expression is calm and
reflective, with her bold black eyeshadow subtly enhancing her gaze. The
background features a softly blurred coastline, with hints of blue waves and
sky blending into the scene. The soft, natural lighting emphasizes the
warmth in her skin tone and the texture of her hair, creating an image that
feels both refreshing and tranquil
output:
url: images/ComfyUI_06735_.png
- text: >-
Billie Eilish stands in front of a large mirror, holding an iPhone 7, taking
a casual selfie. She’s wearing a tight high-neck shirt, with her short,
messy hair slightly covering one eye. The lighting is soft and dim, giving a
cozy, intimate vibe. Her expression is relaxed, with a hint of a smirk. The
background includes a cluttered bedroom with posters on the wall and clothes
scattered around, emphasizing a laid-back, authentic atmosphere. The iPhone
7 screen is slightly visible in the mirror reflection
output:
url: images/ComfyUI_06769_.png
- text: >-
hyperrealistic, billie eilish, medium messy wavy green hair, white skin,
inviting gaze, parted lips, tight shirt,
output:
url: images/ComfyUI_06827_.png
- text: >-
Billie Eilish is depicted in a close-up portrait outdoors on an overcast
day, wearing a grey turtleneck sweater that contrasts beautifully with the
soft, diffused light. The background shows a city street, blurred just
enough to keep the focus on her while adding depth and context. Her
expression is relaxed, with a thoughtful gaze as she looks slightly
off-camera. The cool tones and subtle lighting create a moody yet elegant
vibe
output:
url: images/ComfyUI_00937_.png
- text: >-
An amateur photo of Billie Eilish underwater, floating gracefully while
looking directly into the camera. She’s wearing a tight white high-neck
sweatshirt that gently billows in the water, paired with blue jeans. Her
hair flows freely in the water, framing her face as she maintains a calm,
almost curious expression. The image has a slightly grainy, unpolished feel,
with bubbles rising around her and soft sunlight filtering through the
water’s surface, casting ripples of light. The overall scene feels ethereal
yet grounded, capturing a quiet and introspective moment
output:
url: images/ComfyUI_01203_.png
- text: >-
volumetric lighting, face with Affectionate surface, billie eilish, blonde
hair, Abandoned, Eclectic, kodachrome, womanly
output:
url: images/ComfyUI_01268_.png
- text: >-
hyperrealistic photo of (billie eilish with long black hair) wearing a white
high-neck dress captured with a Canon EOS 90D in stunning high resolution,
by Greg Rutkowski
output:
url: images/ComfyUI_01573_.png
- text: >-
subtle grain, cinematic quality, professional portrait of billie eilish,
makeup, black hair, turtleneck, fine art photography, film still, movie
scene, outdoors, smile,
output:
url: images/ComfyUI_01728_.png
- text: >-
Billie Eilish is depicted in a close-up portrait with the ocean breeze
gently flowing through her long white hair. Her expression is calm and
reflective, with her bold black eyeshadow subtly enhancing her gaze. The
background features a softly blurred coastline, with hints of blue waves and
sky blending into the scene. The soft, natural lighting emphasizes the
warmth in her skin tone and the texture of her hair, creating an image that
feels both refreshing and tranquil
output:
url: images/ComfyUI_00926_.png
- text: >-
Central Figure: The image showcases a portrait of Billie Eilish. She is
dressed in a bold, modern outfit that exudes confidence and a hint of
edginess. She is wearing a tight tube top that hugs her figure, paired with
a cropped bomber jacket that adds a touch of urban style to her ensemble.
She completes the look with a mini pencil skirt that sits high on her waist,
accentuating her form and adding a sleek, sophisticated edge to the overall
attire. Her makeup is sharp and defined, with dark eyeliner emphasizing her
eyes, and her lips are painted a deep, matte shade that adds to the
intensity of her look. Pose and Expression: In this close-up shot, the young
woman stands confidently in a dark, moody city alleyway. Her posture is
strong and assertive, with her shoulders slightly back, conveying a sense of
empowerment. Her head is held high, and she gazes directly into the camera
with a piercing, self-assured expression. Her eyes are captivating, filled
with a quiet intensity that draws the viewer in, and her lips are set in a
subtle, knowing smile that suggests both confidence and a hint of mystery.
The overall pose and expression perfectly capture the essence of a young
woman who is unafraid to embrace her individuality and make a statement.
Background and Setting: The background places her in a dark city alleyway,
with the tall, shadowy buildings creating a narrow, enclosed space that adds
a sense of depth and intrigue to the scene. The alleyway is dimly lit by a
few scattered streetlights, casting long, dramatic shadows across the brick
walls and pavement. The darkness of the setting contrasts sharply with the
vibrant, reflective elements of her bomber jacket, making her the focal
point of the image. The textures of the rough brick and the slick, wet
pavement are subtly highlighted, adding a gritty, urban feel to the overall
composition. The background is blurred enough to keep the focus on her, yet
detailed enough to suggest the city's lively, yet secluded atmosphere.
Lighting and Ambiance: The lighting in the photograph is low-key, with deep
shadows and selective highlights creating a moody, dramatic atmosphere. The
primary source of light comes from a distant streetlamp, casting a soft glow
that illuminates her face and upper body, while leaving the background in
deeper shadows. The lighting emphasizes the contours of her face, her sleek
hair, adding depth and dimension to the image. The interplay of light and
shadow enhances the mysterious, edgy vibe of the scene, perfectly
complementing her confident pose and intense expression. The overall
ambiance is one of urban sophistication, with a touch of raw, gritty realism
that adds to the images striking impact. Camera and Technical Details:
Camera Angle: The camera is positioned slightly above eye level, capturing a
close-up shot that focuses on her face and upper body, ensuring her
expression and outfit are clearly visible against the dark, textured
background. Focal Point: The focus is sharp on her face, particularly her
eyes and the defined lines of her jaw and lips, keeping her expression and
the details of her outfit at the forefront of the composition. Aperture:
f/2.8 to create a shallow depth of field, keeping her in sharp focus while
allowing the dark alleyway background to blur softly, adding to the moody,
intimate feel of the photograph. ISO: 400 to adapt to the low-light setting,
capturing the scene with minimal noise while maintaining the clarity and
vibrancy of the image. Lighting: The lighting is dramatic and directional,
with the low-key illumination creating strong contrasts between light and
shadow, adding intensity to the overall scene. Shutter Speed: 1/60s to
ensure a crisp capture of her expression and outfit, while allowing the
low-key lighting to enhance the image's dramatic effect. Additional Details:
The composition skillfully blends the edgy, urban vibe of the city alleyway
with the bold, confident energy of her outfit and expression. The shot
allows her intense gaze and the sleek, modern details of her attire to take
center stage, while the dark, moody background adds depth and a sense of
intrigue to the scene. The lighting, with its selective highlights and deep
shadows, enhances the overall atmosphere, creating a photograph that exudes
both sophistication and raw, urban energy. The final image is a powerful
portrait that captures the essence of a young woman who is unapologetically
bold and self-assured, set against the gritty, yet stylish backdrop of the
city.
output:
url: images/example_h2ye4gs0y.png
- text: >-
A visually striking close up photograph of Billie Eilish standing in the
middle of a city street at night, surrounded by the vibrant glow of neon
signs. The atmosphere is electric, with neon pink and blue lighting casting
colorful reflections across the scene, giving the environment a dynamic and
almost futuristic feel. The lights bounce off the wet pavement, creating a
vibrant backdrop that enhances the urban night setting. She is wearing a
tight fitted white short sleeve t-shirt that emphasizes her figure and big
natural breats, adding a casual yet stylish touch to her outfit. The shirt
features the word "Lunch" written across the chest in medium black font, The
simplicity of the t-shirt contrasts with the complexity of the neon-lit
surroundings, drawing attention to both Billie and the message on her
shirt. Her hair is black with green roots, styled naturally perhaps in
loose waves or a sleek style, allowing the neon lights to highlight subtle
tones in her hair. Her makeup is minimal but effective, with perhaps a soft
glow on her skin and a hint of color that stands out under the neon lights.
Her expression is calm and self-assured, with a slight hint of curiosity or
determination as she gazes into the distance or directly at the camera. The
background is filled with urban elements like neon signs, shop fronts, and
perhaps distant silhouettes of people, adding depth and context to the
scene. The neon lights in pink and blue provide a striking contrast against
the dark night, making the colors pop and adding an almost cinematic quality
to the photograph. her pose is relaxed yet confident, perhaps with one hand
casually resting at her side or in a pocket, and her stance slightly angled
to show off the shirt. The overall composition focuses on her within the
vibrant city setting, making her stand out while also integrating her into
the lively atmosphere of the street. The overall mood of the photograph is
modern, edgy, and vibrant, capturing Billie Eilish in a moment that blends
casual fashion with the dynamic energy of the neon-lit city night. The
combination of her outfit, the bold "Lunch" text, and the colorful urban
environment creates a visually captivating and memorable image.
output:
url: images/example_ky9zui2ma.png
- text: >-
Caption: Central Figure: The image features a young woman with a bold and
energetic presence. She has her dark hair tied back with a white bandana
adorned with a floral pattern, adding a touch of whimsy to her look. Her
attire consists of an oversized red jersey with the number 26 prominently
displayed on the back, paired with red lace-trimmed shorts. She
accessorizes with a chunky necklace and holds a microphone in her right
hand, suggesting she is in the midst of a performance. Pose and
Expression: In this dynamic shot, the young woman is captured
mid-performance, her body angled towards the left side of the frame. Her
expression is one of intense focus and passion, with her eyes closed and
she's biting her lower lib, as if she is pouring her heart into the song.
Her left hand rests on her hip, emphasizing her confident and assertive
stance. Background and Setting: The background is dark and out of focus,
creating a stark contrast that draws attention to the performer. The stage
lighting casts a dramatic glow on her, highlighting her vibrant red outfit
and adding a sense of depth and atmosphere to the scene. The overall
setting suggests a live music event or concert, with the performer taking
center stage under the spotlight. Lighting and Ambiance: The lighting in
the photograph is dramatic and directional, with the stage lights creating
strong contrasts between light and shadow. The primary source of light comes
from above, casting a warm glow on her face and upper body while leaving
the background in deeper shadows. This lighting technique emphasizes her
facial features and the texture of her outfit, adding depth and dimension
to the image. The interplay of light and shadow enhances the overall
atmosphere, creating a photograph that exudes energy and excitement,
(atmospheric haze, backlighting, color graded cinematic:1.6), atmospheric
lighting, (blue:0.6), pastel colors. Camera and Technical Details: Camera
Angle: The camera is positioned at a low angle, capturing a side profile
shot that focuses on her face and upper body, ensuring her expression and
outfit are clearly visible against the dark, textured background. Focal
Point: The focus is sharp on her face, particularly her eyes and the defined
lines of her jaw and lips, keeping her expression and the details of her
outfit at the forefront of the composition. Aperture: f/2.8 to create a
shallow depth of field, keeping her in sharp focus while allowing the dark
background to blur softly, adding to the dramatic, intimate feel of the
photograph. ISO: 1600 to adapt to the low-light setting, capturing the scene
with minimal noise while maintaining the clarity and vibrancy of the image.
Lighting: The lighting is dynamic and directional, with the stage lights
creating strong contrasts between light and shadow, adding intensity to the
overall scene. Shutter Speed: 1/250s to ensure a crisp capture of her
expression and movement, while allowing the dramatic lighting to enhance
the image's energy. Additional Details: The composition skillfully blends
the vibrant, energetic elements of her outfit and performance with the
dramatic, moody setting of the stage. The shot allows her passionate
expression and the bold, dynamic details of her attire to take center stage,
while the dark, shadowy background adds depth and a sense of intrigue to
the scene. The lighting, with its selective highlights and deep shadows,
enhances the overall atmosphere, creating a photograph that exudes both
energy and intensity. The final image captures the essence of a young woman
who is confident, passionate, and fully engaged in her performance.
output:
url: images/example_aa61v053m.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Billie Eilish
language:
- en
---
# Billie Eilish Flux
<Gallery />
## Trigger words
You should use `Billie Eilish` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/playboy40k/flux-BillieEilishLora/tree/main) them in the Files & versions tab. |
Sagicc/whisper-large-v3-sr-onnx | Sagicc | 2024-10-19T21:31:16Z | 6 | 2 | transformers.js | [
"transformers.js",
"onnx",
"whisper",
"automatic-speech-recognition",
"base_model:Sagicc/whisper-large-v3-sr-combined",
"base_model:quantized:Sagicc/whisper-large-v3-sr-combined",
"license:mit",
"region:us"
] | automatic-speech-recognition | 2023-11-25T16:42:14Z | ---
base_model: Sagicc/whisper-large-v3-sr-combined
library_name: transformers.js
license: mit
---
Fine-tunned Serbian Whisper v3 to use it with Transformers.js
ONNX converted
https://huggingface.co/Sagicc/whisper-large-v3-sr-combined with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
swap-uniba/LLaVA-NDiNO_long | swap-uniba | 2024-10-19T21:30:17Z | 6 | 0 | null | [
"safetensors",
"llava_next",
"text-generation",
"it",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"region:us"
] | text-generation | 2024-10-18T08:12:27Z | ---
license: llama3
language:
- it
base_model:
- meta-llama/Meta-Llama-3-8B
- openai/clip-vit-large-patch14-336
pipeline_tag: text-generation
---
# Model Card for LLaVA-NDiNO_long
## Model description
<!-- Provide a quick summary of what the model is/does. -->
**LLaVA-NDiNO** is a family of *Large Vision Language Models (LVLMs)* that have been trained for the Italian language.
The model was trained by instruction-tuning [**LLaMA 3 8B Base**](https://huggingface.co/meta-llama/Meta-Llama-3-8B) and [**CLIP Large 336**](https://huggingface.co/openai/clip-vit-large-patch14-336) on an Italian machine-translated version of [LLaVA Conversation 58k](https://huggingface.co/datasets/jxu124/llava_conversation_58k).
If you are interested in more details regarding the training procedure, you can find the code we used at the following link:
- **Repository:** https://github.com/swapUniba/LLaVA-NDiNO
- **Developed by:** Elio Musacchio, Lucia Siciliani, Pierpaolo Basile, Giovanni Semeraro
- **Funded by:** PNRR project FAIR - Future AI Research
- **Compute infrastructure:** [Leonardo](https://www.hpc.cineca.it/systems/hardware/leonardo/) supercomputer
- **Model type:** LLaMA 3 + CLIP
- **Language(s) (NLP):** Italian
- **License:** Llama 3 Community License
## Example Usage
```python
import torch
import requests
from PIL import Image
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration, set_seed
model_name = "swap-uniba/LLaVA-NDiNO_long"
processor = LlavaNextProcessor.from_pretrained(model_name)
model = LlavaNextForConditionalGeneration.from_pretrained(model_name, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, device_map="auto")
url = "https://www.barnorama.com/wp-content/uploads/2016/12/03-Confusing-Pictures.jpg"
image = Image.open(requests.get(url, stream=True).raw)
chat_template = "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}"
conversation = [
{
"role": "user",
"content": "<image>\nCosa c'è di strano in questa immagine?"
},
]
prompt = processor.apply_chat_template(conversation, chat_template, add_generation_prompt=True)
inputs = processor(prompt, image, return_tensors="pt")
set_seed(42)
output = model.generate(**inputs, max_new_tokens=4096)
print(processor.decode(output[0][inputs.input_ids.shape[1]:]))
```
## Citation
```
@inproceedings{musacchioLLaVANDiNO,
title={LLaVA-NDiNO: Empowering LLMs with Multimodality for the Italian Language},
author={Musacchio, Elio and Siciliani, Lucia and Basile, Pierpaolo and Semeraro, Giovanni},
booktitle={Proceedings of the Eighth Workshop on Natural Language for Artificial Intelligence (NL4AI 2024) co-located with 23th International Conference of the Italian Association for Artificial Intelligence (AI*IA 2024)},
year={2024}
}
``` |
Sagicc/whisper-base-sr-onnx | Sagicc | 2024-10-19T21:29:37Z | 6 | 0 | transformers.js | [
"transformers.js",
"onnx",
"whisper",
"automatic-speech-recognition",
"sr",
"base_model:Sagicc/whisper-base-sr-yodas",
"base_model:quantized:Sagicc/whisper-base-sr-yodas",
"license:mit",
"region:us"
] | automatic-speech-recognition | 2024-04-20T13:36:46Z | ---
base_model: Sagicc/whisper-base-sr-yodas
language:
- sr
library_name: transformers.js
license: mit
---
Fine-tunned Serbian Whisper medium to use it with Transformers.js
ONNX converted
[Sagicc/whisper-base-sr-yodas](https://huggingface.co/Sagicc/whisper-base-sr-yodas) with ONNX weights to be compatible with Transformers.js.
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).
|
stephenhib/all-mpnet-base-v2-patabs-1epoc-batch32-100 | stephenhib | 2024-10-19T21:26:04Z | 7 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"mpnet",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:768201",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-transformers/all-mpnet-base-v2",
"base_model:finetune:sentence-transformers/all-mpnet-base-v2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-10-19T21:25:48Z | ---
base_model: sentence-transformers/all-mpnet-base-v2
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:768201
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: The present disclosure provides systems and methods to optimize
data backup in a distributed enterprise system by firstly generating a set of
unique files from all the files available in the enterprise. A backup set comprising
files to be backed up are then generated from the set of unique files and backup
is scheduled in the order in which the files to be backed up are identified. Unique
files are generated based on file sharing patterns and communications among users
that enable generating a social network graph from which one or more communities
can be detected and deduplication can be performed on the files hosted by client
systems in these communities thereby conserving resources.
sentences:
- BURNER
- SYSTEMS AND METHODS FOR OPTIMIZED DATA BACKUP IN A DISTRIBUTED ENTERPRISE SYSTEM
- Power conversion apparatus
- source_sentence: The present invention relates to a use of polypeptide compounds
having dual agonist effect on glucagon-like peptide-1 receptor (GLP-1R) and glucagon
receptor (GCGR). The polypeptide compounds are characterized by high enzymolysis
stability, high potency and no adverse reaction, and capable of substantially
improving hepatic fibrosis caused by hepatitis B virus (HBV) and hepatitis C virus
(HCV) and severity of fibrotic conditions accompanied with liver diseases. The
dual target agonist polypeptide derivatives are capable of preventing or treating
hepatic fibrosis diseases associated with viral hepatitis.
sentences:
- GLP-1R/GCGR DUAL-TARGET AGONIST PEPTIDE DERIVATIVES FOR TREATMENT OF VIRAL HEPATITIS-RELATED
HEPATIC FIBROSIS
- MAGNETIC FILTER CARTRIDGE AND FILTER ASSEMBLY
- USER TERMINAL AND WIRELESS COMMUNICATION METHOD
- source_sentence: A latch includes a latch housing including a first housing portion
and a second housing portion separable from the first housing portion. The second
housing portion includes a keeper. A first arm member is in rotational communication
with the first housing portion. The first arm member is configured to rotate about
a first axis between a first position and a second position. A second arm member
is in rotational communication with the first arm member. A latch load pin is
in rotational communication with the first arm member about a second axis. The
latch load pin is configured to mate with the keeper with the first arm member
in the first position. The second arm member in the first position is configured
to be fixed relative to the first arm member as the first arm member rotates from
the first position toward the second position.
sentences:
- UNLOCKING METHODS AND RELATED PRODUCTS
- LATCH AND METHOD FOR OPERATING SAID LATCH
- PANEL-SHAPED MOLDED ARTICLE AND PRODUCTION METHOD FOR PANEL-SHAPED MOLDED ARTICLE
- source_sentence: The present invention aims to provide a production method of low-fat
and low-protein yogurt with smooth taste, suppressed syneresis and superior shape
retainability, comprising adding protein glutaminase and starch to raw milk.
sentences:
- YOGURT PRODUCTION METHOD
- Aircraft electric motor system
- Floor panel, flooring system and method for laying flooring system
- source_sentence: A computer-implemented method determines an orientation parameter
value of a prosthetic component. The method includes receiving a first desired
separation distance (d1) between a tibial prosthetic component (120) and a femoral
prosthetic component (110) at a first flexion position (521) of a knee joint (100)
and estimating a first estimated separation distance (g1) between the tibial prosthetic
component and the femoral prosthetic component at the first flexion position of
the knee joint for at least one potential orientation of the femoral prosthet¬ic
component. The method also includes determining a first orientation para¬meter
value of the femoral prosthetic component by comparing the first estimated separation
distance to the first desired separation distance and out¬putting the first orientation
parameter value via a user interface (400).
sentences:
- Mobile device and antenna structure
- TWO-WAY VALVE FOR CONTROLLING A TEMPERATURE OF A COOLANT FOR AN INTERNAL COMBUSTION
ENGINE
- SYSTEMS AND METHOD FOR PROSTHETIC COMPONENT ORIENTATION
---
# SentenceTransformer based on sentence-transformers/all-mpnet-base-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision f1b1b820e405bb8644f5e8d9a3b98f9c9e0a3c58 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("stephenhib/all-mpnet-base-v2-patabs-1epoc-batch32-100")
# Run inference
sentences = [
'A computer-implemented method determines an orientation parameter value of a prosthetic component. The method includes receiving a first desired separation distance (d1) between a tibial prosthetic component (120) and a femoral prosthetic component (110) at a first flexion position (521) of a knee joint (100) and estimating a first estimated separation distance (g1) between the tibial prosthetic component and the femoral prosthetic component at the first flexion position of the knee joint for at least one potential orientation of the femoral prosthet¬ic component. The method also includes determining a first orientation para¬meter value of the femoral prosthetic component by comparing the first estimated separation distance to the first desired separation distance and out¬putting the first orientation parameter value via a user interface (400).',
'SYSTEMS AND METHOD FOR PROSTHETIC COMPONENT ORIENTATION',
'TWO-WAY VALVE FOR CONTROLLING A TEMPERATURE OF A COOLANT FOR AN INTERNAL COMBUSTION ENGINE',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 768,201 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 13 tokens</li><li>mean: 163.82 tokens</li><li>max: 384 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 12.34 tokens</li><li>max: 73 tokens</li></ul> |
* Samples:
| positive | anchor |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------|
| <code>According to an example aspect of the present invention, there is provided an apparatus and method to control mining vehicles, in particular as electric mining vehicles, taking into account the state of charge the batteries of said mining vehicles.</code> | <code>MINING VEHICLE CONTROL</code> |
| <code>The invention is related to a new soft heterophasic random propylene copolymer with improved optical properties, as well as the process by which the heterophasic random propylene copolymer is produced.</code> | <code>SOFT HETEROPHASIC RANDOM PROPYLENE COPOLYMER WITH IMPROVED CLARITY</code> |
| <code>The present invention relates to a valve assembly 10 for controlling a volute connecting opening 324 of a multi-channel turbine 500. The valve assembly 10 comprises a housing portion 300, a valve body 100 and an internal lever 200. The housing portion 300 defines a first volute channel 312, a second volute channel 314 and a volute connecting region 320. The housing portion 300 further comprises a cavity 340. The cavity 340 is separated from the volutes 312, 314 and can be accessed from outside the housing portion 300 via a housing opening 342 which extends from outside the housing portion 300 into the cavity 340. The volute connection region 320 is located between the first volute channel 312 and the second volute channel 314 and defines a volute connecting opening 324. The valve body 100 is inserted in the cavity 340 of the housing portion 300 and comprises at least one fin 120. The internal lever 200 is coupled with the valve body 100 and configured to pivotably move the valve body 100 between a first position and a second position. In the first position of the valve body 100, the fin 120 blocks the volute connecting opening 324. Thus, exhaust gases are substantially prevented from overflowing from the first volute channel 312 to the second volute channel 314 and vice versa. In the second position of the valve body 100 the fin 120 clears the volute connecting opening 324. Thus, exhaust gases are enabled to overflow from the first volute channel 312 to the second volute channel 314 and vice versa.</code> | <code>VALVE ASSEMBLY FOR MULTI-CHANNEL TURBINE</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 2
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 4
- `per_device_eval_batch_size`: 2
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Framework Versions
- Python: 3.11.9
- Sentence Transformers: 3.2.0
- Transformers: 4.45.2
- PyTorch: 2.5.0+cu124
- Accelerate: 1.0.1
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
129developer/test-lora | 129developer | 2024-10-19T21:23:53Z | 76 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-10-19T21:18:35Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** 129developer
- **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)
|
miosipof/speecht5_tts_voxpopuli_it_v2 | miosipof | 2024-10-19T21:03:37Z | 20 | 0 | null | [
"tensorboard",
"safetensors",
"speecht5",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"region:us"
] | null | 2024-10-18T10:02:11Z | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_tts_voxpopuli_it_v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_tts_voxpopuli_it_v2
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4484
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5707 | 0.2358 | 100 | 0.5183 |
| 0.5452 | 0.4717 | 200 | 0.5096 |
| 0.5313 | 0.7075 | 300 | 0.4890 |
| 0.5229 | 0.9434 | 400 | 0.4807 |
| 0.5119 | 1.1792 | 500 | 0.4802 |
| 0.5121 | 1.4151 | 600 | 0.4681 |
| 0.5037 | 1.6509 | 700 | 0.4719 |
| 0.4996 | 1.8868 | 800 | 0.4691 |
| 0.4931 | 2.1226 | 900 | 0.4621 |
| 0.4903 | 2.3585 | 1000 | 0.4620 |
| 0.4949 | 2.5943 | 1100 | 0.4573 |
| 0.4853 | 2.8302 | 1200 | 0.4579 |
| 0.4826 | 3.0660 | 1300 | 0.4547 |
| 0.4827 | 3.3019 | 1400 | 0.4535 |
| 0.4835 | 3.5377 | 1500 | 0.4523 |
| 0.4802 | 3.7736 | 1600 | 0.4514 |
| 0.4777 | 4.0094 | 1700 | 0.4503 |
| 0.4792 | 4.2453 | 1800 | 0.4499 |
| 0.4779 | 4.4811 | 1900 | 0.4491 |
| 0.4755 | 4.7170 | 2000 | 0.4484 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.2.0
- Datasets 3.0.1
- Tokenizers 0.19.1
|
lucasaltmann/7501843503374 | lucasaltmann | 2024-10-19T20:53:02Z | 20 | 0 | ultralytics | [
"ultralytics",
"v8",
"modelos",
"model-index",
"region:us"
] | null | 2024-08-21T23:23:11Z |
---
tags:
- modelos
library_name: ultralytics
library_version: 8.0.239
inference: false
model-index:
- name: lucasaltmann/7501843503374
results:
- task:
type: object-detection
metrics:
- type: precision # since [email protected] is not available on hf.co/metrics
value: 0.995 # min: 0.0 - max: 1.0
name: [email protected](box)
---
<div align="center">
<img width="640" alt="lucasaltmann/7501843503374" src="https://huggingface.co/lucasaltmann/7501843503374/resolve/main/thumbnail.jpg">
</div>
### Supported Labels
```
['7501843503374']
```
|
nihiluis/legal-sachzivil-subsumption-roberta | nihiluis | 2024-10-19T20:52:49Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T20:52:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
bunnycore/Phi-3.5-mini-TitanFusion-V2 | bunnycore | 2024-10-19T20:47:02Z | 141 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"mergekit",
"merge",
"conversational",
"custom_code",
"arxiv:2306.01708",
"base_model:ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1",
"base_model:merge:ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1",
"base_model:bunnycore/Phi-3.5-mini-TitanFusion-0.1",
"base_model:merge:bunnycore/Phi-3.5-mini-TitanFusion-0.1",
"base_model:bunnycore/Phi-3.5-rp-lora_model",
"base_model:merge:bunnycore/Phi-3.5-rp-lora_model",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:merge:microsoft/Phi-3.5-mini-instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T20:44:33Z | ---
base_model:
- bunnycore/Phi-3.5-mini-TitanFusion-0.1
- bunnycore/Phi-3.5-rp-lora_model
- microsoft/Phi-3.5-mini-instruct
- bunnycore/Phi-3.5-mini-TitanFusion-0.1
- ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1
- bunnycore/Phi-3.5-rp-lora_model
library_name: transformers
tags:
- mergekit
- merge
---
# 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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) as a base.
### Models Merged
The following models were included in the merge:
* [bunnycore/Phi-3.5-mini-TitanFusion-0.1](https://huggingface.co/bunnycore/Phi-3.5-mini-TitanFusion-0.1) + [bunnycore/Phi-3.5-rp-lora_model](https://huggingface.co/bunnycore/Phi-3.5-rp-lora_model)
* [bunnycore/Phi-3.5-mini-TitanFusion-0.1](https://huggingface.co/bunnycore/Phi-3.5-mini-TitanFusion-0.1)
* [ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1](https://huggingface.co/ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1) + [bunnycore/Phi-3.5-rp-lora_model](https://huggingface.co/bunnycore/Phi-3.5-rp-lora_model)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: bunnycore/Phi-3.5-mini-TitanFusion-0.1+bunnycore/Phi-3.5-rp-lora_model
parameters:
density: 0.5
weight: 0.5
- model: bunnycore/Phi-3.5-mini-TitanFusion-0.1
parameters:
density: 0.5
weight: 0.5
- model: ArliAI/Phi-3.5-mini-3.8B-ArliAI-RPMax-v1.1+bunnycore/Phi-3.5-rp-lora_model
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: microsoft/Phi-3.5-mini-instruct
parameters:
normalize: false
int8_mask: true
dtype: float16
```
|
aixonlab/RocRacoon-3b | aixonlab | 2024-10-19T20:45:26Z | 146 | 1 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"base_model:finetune:microsoft/Phi-3-mini-128k-instruct",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T19:53:06Z | ---
library_name: transformers
license: mit
base_model:
- microsoft/Phi-3-mini-128k-instruct
---
# RocRacoon-3b 🦝
RocRacoon-3b is a versatile language model designed to excel in creative writing, storytelling, and multi-turn conversations. Built on the Phi-3-mini-128k-instruct model, it has been fine-tuned to enhance its contextual understanding and generate more engaging and coherent responses.
## Model Details 📊
- **Developed by:** Aixon Lab
- **Model type:** Causal Language Model
- **Language(s):** English (primarily), may support other languages
- **License:** MIT
- **Repository:** https://huggingface.co/aixonlab/RocRacoon-3b
## Quantization
- **GGUF:** https://huggingface.co/mradermacher/RocRacoon-3b-GGUF
## Model Architecture 🏗️
- **Base model:** microsoft/Phi-3-mini-128k-instruct
- **Parameter count:** ~3 billion
- **Architecture specifics:** Transformer-based language model
## Intended Use 🎯
RocRacoon-3b is designed for a wide range of natural language processing tasks, with a particular focus on article writing and topic based multi-turn conversations. It can be used for text generation, dialogue systems, and content creation.
## Ethical Considerations 🤔
As a derivative of the Phi-3-mini model, RocRacoon-3b may inherit some biases and limitations. Users should be aware of potential biases in generated content and use the model responsibly, especially in sensitive contexts.
## Performance and Evaluation
Comprehensive performance metrics for RocRacoon-3b are currently being compiled. Initial tests show improvements in coherence and creativity compared to the base model. Users are encouraged to contribute their findings and benchmarks.
## Limitations and Biases
While efforts have been made to mitigate biases, the model may still exhibit some biases present in its training data. Users should critically evaluate the model's outputs and use them in conjunction with human judgment, particularly for sensitive applications.
## Additional Information
For more details on the base Phi-3-mini-128k-instruct model, please refer to its model card and documentation.
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("aixonlab/RocRacoon-3b")
tokenizer = AutoTokenizer.from_pretrained("aixonlab/RocRacoon-3b")
prompt = "Write a short story about a clever raccoon"
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=200)
generated_text = tokenizer.decode(generated_ids, skip_special_tokens=True)
print(generated_text) |
jirvin16/TEOChat | jirvin16 | 2024-10-19T20:41:58Z | 368 | 6 | null | [
"pytorch",
"llava",
"arxiv:2410.06234",
"license:apache-2.0",
"region:us"
] | null | 2024-10-08T03:54:43Z | ---
license: apache-2.0
---
<p align="center">
<img src="logo.png" width="150" style="margin-bottom: 0.2;"/>
<p>
<h2 align="center"> <a href="http://arxiv.org/abs/2410.06234">TEOChat: Large Language and Vision Assistant for Temporal Earth Observation Data</a></h2>
<h5 align="center"> If you like our project, please give us a star ⭐ on <a href="https://github.com/ermongroup/TEOChat">Github</a> for the latest updates. </h5>
## 😮 Highlights
**TEOChat** is the first language and vision assistant that can engage in conversation about sequences of temporal earth observation imagery, and exhibits impressive performance on multiple temporal instruction-following tasks.
### 📚 TEOChatlas: A new instruction-following dataset for temporal EO data
We introduce a new instruction-following dataset for temporal EO data called **TEOChatlas** which we use to train TEOChat. TEOChatlas contains 554,071 examples spanning dozens of temporal instruction-following tasks.
### 🤖 TEOChat: A new vision-language model for temporal EO data
We design TEOChat to use a LLaVA-style architecture, combining a temporally shared vision encoder with a LLaMA 2 LLM connected through an MLP vision-language projector
## 🤗 Demo
### Gradio Web UI
We provide an [online demo](https://huggingface.co/spaces/jirvin16/TEOChat) in Huggingface Spaces.
You can also run the demo locally by running the following command:
```bash
python videollava/serve/teochat_demo.py
```
## 🛠️ Requirements and Installation
* Python >= 3.9
* Pytorch == 2.2.1
* CUDA Version >= 12.1
* Install required packages:
```bash
git clone https://github.com/ermongroup/TEOChat.git
cd TEOChat
conda create -n teochat python=3.9 -y
conda activate teochat
pip install --upgrade pip # enable PEP 660 support
pip install -r requirements.txt
```
## 🗝️ Training & Validating
The training & validating instructions are in [TRAIN_AND_VALIDATE.md](https://github.com/ermongroup/TEOChat/blob/main/TRAIN_AND_VALIDATE.md).
## 👍 Acknowledgement
* [Video-LLaVA](https://github.com/PKU-YuanGroup/Video-LLaVA) The codebase and model we built upon.
* [GeoChat](https://github.com/mbzuai-oryx/geochat) The single image instruction-following dataset we included in TEOChatlas.
## 🔒 License
* The majority of this project is released under the Apache 2.0 license as found in the [LICENSE](https://github.com/ermongroup/TEOChat/blob/main/LICENSE) file.
* The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
## ✏️ Citation
If you find our paper and code useful in your research, please consider giving a star ⭐ and citation ✏️.
```BibTeX
@article{irvin2024teochat,
title={TEOChat: A Large Vision-Language Assistant for Temporal Earth Observation Data},
author={Liu, Emily Ruoyu and Chen, Joyce Chuyi and Dormoy, Ines and Kim, Jinyoung and Khanna, Samar and Zheng, Zhuo and Ermon, Stefano},
journal={arXiv preprint arXiv:2410.06234},
year={2024}
}
```
|
la2ydev/RoGemma2-9b-Instruct | la2ydev | 2024-10-19T20:41:24Z | 7 | 0 | null | [
"gguf",
"text-generation",
"ro",
"base_model:OpenLLM-Ro/RoGemma2-9b-Instruct",
"base_model:quantized:OpenLLM-Ro/RoGemma2-9b-Instruct",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-17T16:33:09Z | ---
license: cc-by-nc-4.0
language:
- ro
base_model:
- OpenLLM-Ro/RoGemma2-9b-Instruct
pipeline_tag: text-generation
---
# RoGemma2-9b-Instruct
This repository contains quantized versions of the model.
- [RoGemma2-9b-Instruct-Q8_0](./RoGemma2-9b-Instruct-Q8_0.gguf)
- [RoGemma2-9b-Instruct-Q6_K](./RoGemma2-9b-Instruct-Q6_K.gguf)
For the original model, please visit: [OpenLLM-Ro/RoGemma2-9b-Instruct](/OpenLLM-Ro/RoGemma2-9b-Instruct).
# Uses
RoGemma2 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat. |
fierce-cats/beatrice-trainer | fierce-cats | 2024-10-19T20:30:05Z | 0 | 27 | null | [
"audio",
"speech",
"voice-conversion",
"audio-to-audio",
"dataset:reazon-research/reazonspeech",
"dataset:dns-challenge",
"dataset:libritts-r",
"arxiv:2101.08692",
"arxiv:2306.06546",
"arxiv:2006.11477",
"arxiv:2210.13438",
"arxiv:2010.05646",
"arxiv:2306.00814",
"arxiv:2309.02836",
"arxiv:2106.07889",
"arxiv:2111.02392",
"arxiv:2401.03078",
"arxiv:2402.00892",
"arxiv:2309.14507",
"arxiv:2401.10460",
"license:mit",
"region:us"
] | audio-to-audio | 2024-06-12T14:31:07Z | ---
license: mit
pipeline_tag: audio-to-audio
tags:
- audio
- speech
- voice-conversion
datasets:
- reazon-research/reazonspeech
- dns-challenge
- libritts-r
---
# Beatrice Trainer
超低遅延・低負荷・低容量を特徴とする完全無料の声質変換 VST 「[Beatrice 2](https://prj-beatrice.com)」のモデル学習用ツールキットです。
Beatrice 2 は、以下を目標に開発されています。
* 自分の変換された声を聴きながら、歌を快適に歌えるようにする
* 入力された声の抑揚を変換音声に正確に反映し、繊細な表現を可能にする
* 変換音声の高い自然性と明瞭さ
* 多様な変換先話者
* 公式 VST での変換時、外部の録音機器を使った実測で 50ms 程度の遅延
* 開発者のノート PC (Intel Core i7-1165G7) でシングルスレッドで動作させ、RTF < 0.25 となる程度の負荷
* 最小構成で 30MB 以下の容量
* VST と [VC Client](https://github.com/w-okada/voice-changer) での動作
* その他 (内緒)
## Release Notes
* **2024-10-20**: Beatrice Trainer 2.0.0-beta.2 をリリースしました。
* **[公式 VST](https://prj-beatrice.com) や [VC Client](https://github.com/w-okada/voice-changer) を最新版にアップデートしてください。新しい Trainer で生成したモデルは、古いバージョンの公式 VST や VC Client で動作しません。**
* [Scaled Weight Standardization](https://arxiv.org/abs/2101.08692) の導入により、学習の安定性が向上しました。
* 無音に非常に近い音声に対する損失の計算結果が nan になる問題を修正し、学習の安定性が向上しました。
* 周期信号の生成方法を変更し、事前学習モデルを用いない場合により少ない学習ステップ数で高品質な変換音声を生成できるようになりました。
* [FIRNet](https://ast-astrec.nict.go.jp/release/preprints/preprint_icassp_2024_ohtani.pdf) に着想を得たポストフィルタ構造を導入し、変換音声の品質が向上しました。
* [D4C](https://www.sciencedirect.com/science/article/pii/S0167639316300413) を損失関数に導入し、変換音声の品質が向上しました。
* [Multi-scale mel loss](https://arxiv.org/abs/2306.06546) を導入しました。
* 冗長な逆伝播の除去や `torch.backends.cudnn.benchmark` の部分的な無効化などにより、学習速度が向上しました。
* 学習データにモノラルでない音声ファイルが含まれる場合にエラーが発生する問題を修正しました。
* 音量計算の誤りを修正し、学習時と推論時の変換結果の不一致が解消されました。
* PyTorch のバージョンの下限を修正しました。
* Windows 環境で CPU 版の PyTorch がインストールされる問題を修正しました。
* Windows 環境で DataLoader の動作が非常に遅くなる問題を修正しました。
* その他いくつかの変更を行いました。
* **2024-07-27**: Beatrice Trainer 2.0.0-beta.0 をリリースしました。
## Prerequisites
Beatrice は、既存の学習済みモデルを用いて声質の変換を行うだけであれば GPU を必要としません。
しかし、新たなモデルの作成を効率良く行うためには GPU が必要です。
学習スクリプトを実行すると、デフォルト設定では 9GB 程度の VRAM を消費します。
GeForce RTX 4090 を使用した場合、 30 分程度で学習が完了します。
GPU を手元に用意できない場合でも、以下のリポジトリを使用して Google Colab 上で学習を行うことができます。
* [w-okada/beatrice-trainer-colab](https://github.com/w-okada/beatrice-trainer-colab)
## Getting Started
### 1. Download This Repo
Git などを使用して、このリポジトリをダウンロードしてください。
```sh
git lfs install
git clone https://huggingface.co/fierce-cats/beatrice-trainer
cd beatrice-trainer
```
### 2. Environment Setup
Poetry などを使用して、依存ライブラリをインストールしてください。
```sh
poetry install
poetry shell
# Alternatively, you can use pip to install dependencies directly:
# pip3 install -e .
```
正しくインストールできていれば、 `python3 beatrice_trainer -h` で以下のようなヘルプが表示されます。
```
usage: beatrice_trainer [-h] [-d DATA_DIR] [-o OUT_DIR] [-r] [-c CONFIG]
options:
-h, --help show this help message and exit
-d DATA_DIR, --data_dir DATA_DIR
directory containing the training data
-o OUT_DIR, --out_dir OUT_DIR
output directory
-r, --resume resume training
-c CONFIG, --config CONFIG
path to the config file
```
### 3. Prepare Your Training Data
下図のように学習データを配置してください。
```
your_training_data_dir
+---alice
| +---alices_wonderful_speech.wav
| +---alices_excellent_speech.flac // FLAC, MP3, and some other formats are also okay.
| `---...
+---bob
| +---bobs_fantastic_speech.wav
| +---bobs_speeches
| | `---bobs_awesome_speech.wav // Audio files in nested directory will also be used.
| `---...
`---...
```
学習データ用ディレクトリの直下に各話者のディレクトリを作る必要があります。
各話者のディレクトリの中の構造や音声ファイルの名前は自由です。
学習を行うデータが 1 話者のみの場合も、話者のディレクトリを作る必要があることに注意してください。
```
your_training_data_dir_with_only_one_speaker
+---charlies_brilliant_speech.wav // Wrong.
`---...
```
```
your_training_data_dir_with_only_one_speaker
`---charlie
+---charlies_brilliant_speech.wav // Correct!
`---...
```
### 4. Train Your Model
学習データを配置したディレクトリと出力ディレクトリを指定して学習を開始します。
```sh
python3 beatrice_trainer -d <your_training_data_dir> -o <output_dir>
```
(Windowns の場合、 `beatrice_trainer` の代わりに `.\beatrice_trainer\__main__.py` を指定しないと正しく動作しないという報告があります。)
学習の状況は、 TensorBoard で確認できます。
```sh
tensorboard --logdir <output_dir>
```
### 5. After Training
学習が正常に完了すると、出力ディレクトリ内に `paraphernalia_(data_dir_name)_(step)` という名前のディレクトリが生成されています。
このディレクトリを[公式 VST](https://prj-beatrice.com) や [VC Client](https://github.com/w-okada/voice-changer) で読み込むことで、ストリーム (リアルタイム) 変換を行うことができます。
**読み込めない場合は公式 VST や VC Client のバージョンが古い可能性がありますので、最新のバージョンにアップデートしてください。**
## Detailed Usage
### Training
使用するハイパーパラメータや事前学習済みモデルをデフォルトと異なるものにする場合は、デフォルト値の書かれたコンフィグファイルである `assets/default_config.json` を別の場所にコピーして値を編集し、 `-c` でファイルを指定します。
`assets/default_config.json` を直接編集すると壊れるので注意してください。
また、コンフィグファイルに `data_dir` キーと `out_dir` キーを追加し、学習データを配置したディレクトリと出力ディレクトリを絶対パスまたはリポジトリルートからの相対パスで記載することで、コマンドライン引数での指定を省略できます。
```sh
python3 beatrice_trainer -c <your_config.json>
```
何らかの理由で学習が中断された場合、出力ディレクトリに `checkpoint_latest.pt` が生成されていれば、その学習を行っていたコマンドに `-r` オプションを追加して実行することで、最後に保存されたチェックポイントから学習を再開できます。
```sh
python3 beatrice_trainer -d <your_training_data_dir> -o <output_dir> -r
```
### Output Files
学習スクリプトを実行すると、出力ディレクトリ内に以下のファイル・ディレクトリが生成されます。
* `paraphernalia_(data_dir_name)_(step)`
* ストリーム変換に必要なファイルを全て含むディレクトリです。
* 学習途中のものも出力される場合があり、必要なステップ数のもの以外は削除して問題ありません。
* このディレクトリ以外の出力物はストリーム変換に使用されないため、不要であれば削除して問題ありません。
* `checkpoint_(data_dir_name)_(step)`
* 学習を途中から再開するためのチェックポイントです。
* checkpoint_latest.pt にリネームし、 `-r` オプションを付けて学習スクリプトを実行すると、そのステップ数から学習を再開できます。
* `checkpoint_latest.pt`
* 最も新しい checkpoint_(data_dir_name)_(step) のコピーです。
* `config.json`
* 学習に使用されたコンフィグです。
* `events.out.tfevents.*`
* TensorBoard で表示される情報を含むデータです。
### Customize Paraphernalia
学習スクリプトによって生成された paraphernalia ディレクトリ内にある `beatrice_paraphernalia_*.toml` ファイルを編集することで、 VST や VC Client 上での表示を変更できます。
`model.version` は、生成されたモデルのフォーマットバージョンを表すため、変更しないでください。
各 `description` は、長すぎると全文が表示されない場合があります。
現在表示できていても、将来的な VST や VC Client の仕様変更により表示できなくなる可能性があるため、余裕を持った文字数・行数に収めてください。
`portrait` に設定する画像は、 PNG 形式かつ正方形としてください。
## Distribution of Trained Models
このリポジトリを用いて生成したモデルの配布を歓迎します。
配布されたモデルは、 Project Beatrice およびその関係者の管理する SNS アカウントやウェブサイト上でご紹介させていただく場合があります。
その際、 `portrait` に設定された画像を掲載することがありますので、予めご承知おきください。
## Resource
このリポジトリには、学習などに使用する各種データが含まれています。
詳しくは [assets/README.md](https://huggingface.co/fierce-cats/beatrice-trainer/blob/main/assets/README.md) をご覧ください。
## Reference
* [wav2vec 2.0](https://arxiv.org/abs/2006.11477) ([Official implementation](https://github.com/facebookresearch/fairseq), [MIT License](https://github.com/facebookresearch/fairseq/blob/main/LICENSE))
* FeatureExtractor の実装に利用。
* [EnCodec](https://arxiv.org/abs/2210.13438) ([Official implementation](https://github.com/facebookresearch/encodec), [MIT License](https://github.com/facebookresearch/encodec/blob/main/LICENSE))
* GradBalancer の実装に利用。
* [HiFi-GAN](https://arxiv.org/abs/2010.05646) ([Official implementation](https://github.com/jik876/hifi-gan), [MIT License](https://github.com/jik876/hifi-gan/blob/master/LICENSE))
* DiscriminatorP の実装に利用。
* [Vocos](https://arxiv.org/abs/2306.00814) ([Official implementation](https://github.com/gemelo-ai/vocos), [MIT License](https://github.com/gemelo-ai/vocos/blob/main/LICENSE))
* ConvNeXtBlock の実装に利用。
* [BigVSAN](https://arxiv.org/abs/2309.02836) ([Official implementation](https://github.com/sony/bigvsan), [MIT License](https://github.com/sony/bigvsan/blob/main/LICENSE))
* SAN モジュールの実装に利用。
* [D4C](https://www.sciencedirect.com/science/article/pii/S0167639316300413) ([Unofficial implementation by tuanad121](https://github.com/tuanad121/Python-WORLD), [MIT License](https://github.com/tuanad121/Python-WORLD/blob/master/LICENSE.txt))
* 損失関数の実装に利用。
* [UnivNet](https://arxiv.org/abs/2106.07889) ([Unofficial implementation by maum-ai](https://github.com/maum-ai/univnet), [BSD 3-Clause License](https://github.com/maum-ai/univnet/blob/master/LICENSE))
* DiscriminatorR の実装に利用。
* [NF-ResNets](https://arxiv.org/abs/2101.08692)
* Scaled Weight Standardization のアイデアを利用。
* [Soft-VC](https://arxiv.org/abs/2111.02392)
* PhoneExtractor の基本的なアイデアとして利用。
* [Descript Audio Codec](https://arxiv.org/abs/2306.06546)
* Multi-scale mel loss のアイデアを利用。
* [StreamVC](https://arxiv.org/abs/2401.03078)
* 声質変換スキームの基本的なアイデアとして利用。
* [FIRNet](https://ast-astrec.nict.go.jp/release/preprints/preprint_icassp_2024_ohtani.pdf)
* FIR フィルタを Vocoder に適用するアイデアを利用。
* [EVA-GAN](https://arxiv.org/abs/2402.00892)
* SiLU を vocoder に適用するアイデアを利用。
* [Subramani et al., 2024](https://arxiv.org/abs/2309.14507)
* PitchEstimator の基本的なアイデアとして利用。
* [Agrawal et al., 2024](https://arxiv.org/abs/2401.10460)
* Vocoder の基本的なアイデアとして利用。
## License
このリポジトリ内のソースコードおよび学習済みモデルは MIT License のもとで公開されています。
詳しくは [LICENSE](https://huggingface.co/fierce-cats/beatrice-trainer/blob/main/LICENSE) をご覧ください。
|
nihiluis/legal-sach-subsumtion-roberta | nihiluis | 2024-10-19T20:21:48Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T20:21:03Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
nihiluis/legal-sach-components-roberta | nihiluis | 2024-10-19T20:17:01Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T20:14:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
tjake/Llama-3.2-3B-Instruct-JQ4 | tjake | 2024-10-19T20:11:31Z | 185 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T20:10:42Z | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
Llama 3.2 Version Release Date: September 25, 2024
“Agreement” means the terms and conditions for use, reproduction, distribution
and modification of the Llama Materials set forth herein.
“Documentation” means the specifications, manuals and documentation accompanying Llama 3.2
distributed by Meta at https://llama.meta.com/doc/overview.
“Licensee” or “you” means you, or your employer or any other person or entity (if you are
entering into this Agreement on such person or entity’s behalf), of the age required under
applicable laws, rules or regulations to provide legal consent and that has legal authority
to bind your employer or such other person or entity if you are entering in this Agreement
on their behalf.
“Llama 3.2” means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
https://www.llama.com/llama-downloads.
“Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and
any portion thereof) made available under this Agreement.
“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or,
if you are an entity, your principal place of business is in the EEA or Switzerland)
and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).
By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials,
you agree to be bound by this Agreement.
1. License Rights and Redistribution.
a. Grant of Rights. You are granted a non-exclusive, worldwide,
non-transferable and royalty-free limited license under Meta’s intellectual property or other rights
owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works
of, and make modifications to the Llama Materials.
b. Redistribution and Use.
i. If you distribute or make available the Llama Materials (or any derivative works thereof),
or a product or service (including another AI model) that contains any of them, you shall (A) provide
a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama”
on a related website, user interface, blogpost, about page, or product documentation. If you use the
Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or
otherwise improve an AI model, which is distributed or made available, you shall also include “Llama”
at the beginning of any such AI model name.
ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part
of an integrated end user product, then Section 2 of this Agreement will not apply to you.
iii. You must retain in all copies of the Llama Materials that you distribute the
following attribution notice within a “Notice” text file distributed as a part of such copies:
“Llama 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms,
Inc. All Rights Reserved.”
iv. Your use of the Llama Materials must comply with applicable laws and regulations
(including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for
the Llama Materials (available at https://www.llama.com/llama3_2/use-policy), which is hereby
incorporated by reference into this Agreement.
2. Additional Commercial Terms. If, on the Llama 3.2 version release date, the monthly active users
of the products or services made available by or for Licensee, or Licensee’s affiliates,
is greater than 700 million monthly active users in the preceding calendar month, you must request
a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to
exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND
RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS
ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES
OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE
FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED
WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN
IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
5. Intellectual Property.
a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials,
neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates,
except as required for reasonable and customary use in describing and redistributing the Llama Materials or as
set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required
to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible
at https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising out of your use of the Mark
will inure to the benefit of Meta.
b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any
derivative works and modifications of the Llama Materials that are made by you, as between you and Meta,
you are and will be the owner of such derivative works and modifications.
c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or
counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion
of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable
by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or
claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third
party arising out of or related to your use or distribution of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access
to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms
and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this
Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3,
4 and 7 shall survive the termination of this Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of
California without regard to choice of law principles, and the UN Convention on Contracts for the International
Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of
any dispute arising out of this Agreement.
### Llama 3.2 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2.
If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”).
The most recent copy of this policy can be found at
[https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).
#### Prohibited Uses
We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
2. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law
5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following:
8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997
9. Guns and illegal weapons (including weapon development)
10. Illegal drugs and regulated/controlled substances
11. Operation of critical infrastructure, transportation technologies, or heavy machinery
12. Self-harm or harm to others, including suicide, cutting, and eating disorders
13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following:
14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
16. Generating, promoting, or further distributing spam
17. Impersonating another individual without consent, authorization, or legal right
18. Representing that the use of Llama 3.2 or outputs are human-generated
19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2
With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models.
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)
* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected]
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
- Reporter
- Other
geo: ip_location
By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
extra_gated_description: >-
The information you provide will be collected, stored, processed and shared in
accordance with the [Meta Privacy
Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
# Quantized Version of meta-llama/Llama-3.2-3B-Instruct
This model is a quantized variant of the meta-llama/Llama-3.2-3B-Instruct model, optimized for use with Jlama, a Java-based inference engine. The quantization process reduces the model's size and improves inference speed, while maintaining high accuracy for efficient deployment in production environments.
For more information on Jlama, visit the [Jlama GitHub repository](https://github.com/tjake/jlama).
---
## Model Information
The Meta Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama-models/tree/main/models/llama3_2). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-3B-Instruct, for use with `transformers` and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-3B-Instruct"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-3B-Instruct --include "original/*" --local-dir Llama-3.2-3B-Instruct
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
##
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Total | 830k | 86k | | 240 | 0 |
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 63.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 40.1 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 19.0 | 17.2 |
| Instruction following | | IFEval | 0 | avg(prompt/instruction acc loose/strict) | 59.5 | 77.4 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 77.7 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 47.3 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 78.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 32.8 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 69.8 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 67.0 | 70.9 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 34.3 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | 19.8 | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | 63.3 | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | 84.7 | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 58.2 | 68.9 |
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro\_avg/acc) | Portuguese | 39.82 | 54.48 | 62.12 |
| | | Spanish | 41.5 | 55.1 | 62.5 |
| | | Italian | 39.8 | 53.8 | 61.6 |
| | | German | 39.2 | 53.3 | 60.6 |
| | | French | 40.5 | 54.6 | 62.3 |
| | | Hindi | 33.5 | 43.3 | 50.9 |
| | | Thai | 34.7 | 44.5 | 50.3 |
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
AhmedBou/Arabic-Law-Meta-Llama-3.2-3B-GGUF | AhmedBou | 2024-10-19T20:10:54Z | 200 | 2 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-19T20:08:42Z | ---
base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** AhmedBou
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
tjake/Llama-3.1-8B-Instruct-JQ4 | tjake | 2024-10-19T20:00:59Z | 77 | 0 | null | [
"safetensors",
"llama",
"facebook",
"meta",
"pytorch",
"llama-3",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] | text-generation | 2024-10-19T19:59:03Z | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
extra_gated_prompt: "### LLAMA 3.1 COMMUNITY LICENSE AGREEMENT\nLlama 3.1 Version\
\ Release Date: July 23, 2024\n\"Agreement\" means the terms and conditions for\
\ use, reproduction, distribution and modification of the Llama Materials set forth\
\ herein.\n\"Documentation\" means the specifications, manuals and documentation\
\ accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.\n\
\"Licensee\" or \"you\" means you, or your employer or any other person or entity\
\ (if you are entering into this Agreement on such person or entity’s behalf), of\
\ the age required under applicable laws, rules or regulations to provide legal\
\ consent and that has legal authority to bind your employer or such other person\
\ or entity if you are entering in this Agreement on their behalf.\n\"Llama 3.1\"\
\ means the foundational large language models and software and algorithms, including\
\ machine-learning model code, trained model weights, inference-enabling code, training-enabling\
\ code, fine-tuning enabling code and other elements of the foregoing distributed\
\ by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means,\
\ collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion\
\ thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms\
\ Ireland Limited (if you are located in or, if you are an entity, your principal\
\ place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you\
\ are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\n\
a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable\
\ and royalty-free limited license under Meta’s intellectual property or other rights\
\ owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy,\
\ create derivative works of, and make modifications to the Llama Materials.\nb.\
\ Redistribution and Use.\ni. If you distribute or make available the Llama Materials\
\ (or any derivative works thereof), or a product or service (including another\
\ AI model) that contains any of them, you shall (A) provide a copy of this Agreement\
\ with any such Llama Materials; and (B) prominently display “Built with Llama”\
\ on a related website, user interface, blogpost, about page, or product documentation.\
\ If you use the Llama Materials or any outputs or results of the Llama Materials\
\ to create, train, fine tune, or otherwise improve an AI model, which is distributed\
\ or made available, you shall also include “Llama” at the beginning of any such\
\ AI model name.\nii. If you receive Llama Materials, or any derivative works thereof,\
\ from a Licensee as part of an integrated end user product, then Section 2 of\
\ this Agreement will not apply to you.\niii. You must retain in all copies of the\
\ Llama Materials that you distribute the following attribution notice within a\
\ “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed\
\ under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights\
\ Reserved.”\niv. Your use of the Llama Materials must comply with applicable laws\
\ and regulations (including trade compliance laws and regulations) and adhere to\
\ the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use-policy),\
\ which is hereby incorporated by reference into this Agreement.\n2. Additional\
\ Commercial Terms. If, on the Llama 3.1 version release date, the monthly active\
\ users of the products or services made available by or for Licensee, or Licensee’s\
\ affiliates, is greater than 700 million monthly active users in the preceding\
\ calendar month, you must request a license from Meta, which Meta may grant to\
\ you in its sole discretion, and you are not authorized to exercise any of the\
\ rights under this Agreement unless or until Meta otherwise expressly grants you\
\ such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE\
\ LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS”\
\ BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY\
\ KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES\
\ OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE.\
\ YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING\
\ THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA\
\ MATERIALS AND ANY OUTPUT AND RESULTS.\n4. Limitation of Liability. IN NO EVENT\
\ WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN\
\ CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS\
\ AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL,\
\ EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED\
\ OF THE POSSIBILITY OF ANY OF THE FOREGOING.\n5. Intellectual Property.\na. No\
\ trademark licenses are granted under this Agreement, and in connection with the\
\ Llama Materials, neither Meta nor Licensee may use any name or mark owned by or\
\ associated with the other or any of its affiliates, except as required for reasonable\
\ and customary use in describing and redistributing the Llama Materials or as set\
\ forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the\
\ “Mark”) solely as required to comply with the last sentence of Section 1.b.i.\
\ You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/\
\ ). All goodwill arising out of your use of the Mark will inure to the benefit\
\ of Meta.\nb. Subject to Meta’s ownership of Llama Materials and derivatives made\
\ by or for Meta, with respect to any derivative works and modifications of the\
\ Llama Materials that are made by you, as between you and Meta, you are and will\
\ be the owner of such derivative works and modifications.\nc. If you institute\
\ litigation or other proceedings against Meta or any entity (including a cross-claim\
\ or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs\
\ or results, or any portion of any of the foregoing, constitutes infringement of\
\ intellectual property or other rights owned or licensable by you, then any licenses\
\ granted to you under this Agreement shall terminate as of the date such litigation\
\ or claim is filed or instituted. You will indemnify and hold harmless Meta from\
\ and against any claim by any third party arising out of or related to your use\
\ or distribution of the Llama Materials.\n6. Term and Termination. The term of\
\ this Agreement will commence upon your acceptance of this Agreement or access\
\ to the Llama Materials and will continue in full force and effect until terminated\
\ in accordance with the terms and conditions herein. Meta may terminate this Agreement\
\ if you are in breach of any term or condition of this Agreement. Upon termination\
\ of this Agreement, you shall delete and cease use of the Llama Materials. Sections\
\ 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law\
\ and Jurisdiction. This Agreement will be governed and construed under the laws\
\ of the State of California without regard to choice of law principles, and the\
\ UN Convention on Contracts for the International Sale of Goods does not apply\
\ to this Agreement. The courts of California shall have exclusive jurisdiction\
\ of any dispute arising out of this Agreement.\n### Llama 3.1 Acceptable Use Policy\n\
Meta is committed to promoting safe and fair use of its tools and features, including\
\ Llama 3.1. If you access or use Llama 3.1, you agree to this Acceptable Use Policy\
\ (“Policy”). The most recent copy of this policy can be found at [https://llama.meta.com/llama3_1/use-policy](https://llama.meta.com/llama3_1/use-policy)\n\
#### Prohibited Uses\nWe want everyone to use Llama 3.1 safely and responsibly.\
\ You agree you will not use, or allow others to use, Llama 3.1 to:\n 1. Violate\
\ the law or others’ rights, including to:\n 1. Engage in, promote, generate,\
\ contribute to, encourage, plan, incite, or further illegal or unlawful activity\
\ or content, such as:\n 1. Violence or terrorism\n 2. Exploitation\
\ or harm to children, including the solicitation, creation, acquisition, or dissemination\
\ of child exploitative content or failure to report Child Sexual Abuse Material\n\
\ 3. Human trafficking, exploitation, and sexual violence\n 4. The\
\ illegal distribution of information or materials to minors, including obscene\
\ materials, or failure to employ legally required age-gating in connection with\
\ such information or materials.\n 5. Sexual solicitation\n 6. Any\
\ other criminal activity\n 3. Engage in, promote, incite, or facilitate the\
\ harassment, abuse, threatening, or bullying of individuals or groups of individuals\n\
\ 4. Engage in, promote, incite, or facilitate discrimination or other unlawful\
\ or harmful conduct in the provision of employment, employment benefits, credit,\
\ housing, other economic benefits, or other essential goods and services\n 5.\
\ Engage in the unauthorized or unlicensed practice of any profession including,\
\ but not limited to, financial, legal, medical/health, or related professional\
\ practices\n 6. Collect, process, disclose, generate, or infer health, demographic,\
\ or other sensitive personal or private information about individuals without rights\
\ and consents required by applicable laws\n 7. Engage in or facilitate any action\
\ or generate any content that infringes, misappropriates, or otherwise violates\
\ any third-party rights, including the outputs or results of any products or services\
\ using the Llama Materials\n 8. Create, generate, or facilitate the creation\
\ of malicious code, malware, computer viruses or do anything else that could disable,\
\ overburden, interfere with or impair the proper working, integrity, operation\
\ or appearance of a website or computer system\n2. Engage in, promote, incite,\
\ facilitate, or assist in the planning or development of activities that present\
\ a risk of death or bodily harm to individuals, including use of Llama 3.1 related\
\ to the following:\n 1. Military, warfare, nuclear industries or applications,\
\ espionage, use for materials or activities that are subject to the International\
\ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
\ State\n 2. Guns and illegal weapons (including weapon development)\n 3.\
\ Illegal drugs and regulated/controlled substances\n 4. Operation of critical\
\ infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm\
\ or harm to others, including suicide, cutting, and eating disorders\n 6. Any\
\ content intended to incite or promote violence, abuse, or any infliction of bodily\
\ harm to an individual\n3. Intentionally deceive or mislead others, including use\
\ of Llama 3.1 related to the following:\n 1. Generating, promoting, or furthering\
\ fraud or the creation or promotion of disinformation\n 2. Generating, promoting,\
\ or furthering defamatory content, including the creation of defamatory statements,\
\ images, or other content\n 3. Generating, promoting, or further distributing\
\ spam\n 4. Impersonating another individual without consent, authorization,\
\ or legal right\n 5. Representing that the use of Llama 3.1 or outputs are human-generated\n\
\ 6. Generating or facilitating false online engagement, including fake reviews\
\ and other means of fake online engagement\n4. Fail to appropriately disclose to\
\ end users any known dangers of your AI system\nPlease report any violation of\
\ this Policy, software “bug,” or other problems that could lead to a violation\
\ of this Policy through one of the following means:\n * Reporting issues with\
\ the model: [https://github.com/meta-llama/llama-models/issues](https://github.com/meta-llama/llama-models/issues)\n\
\ * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n\
\ * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting\
\ violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]"
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
- Reporter
- Other
geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
with the Meta Privacy Policy
: checkbox
extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
# Quantized Version of meta-llama/Llama-3.1-8B-Instruct
This model is a quantized variant of the meta-llama/Llama-3.1-8B-Instruct model, optimized for use with Jlama, a Java-based inference engine. The quantization process reduces the model's size and improves inference speed, while maintaining high accuracy for efficient deployment in production environments.
For more information on Jlama, visit the [Jlama GitHub repository](https://github.com/tjake/jlama).
---
## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
**Model developer**: Meta
**Model Architecture:** Llama 3.1 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Input modalities</strong>
</td>
<td><strong>Output modalities</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="3" >Llama 3.1 (text only)
</td>
<td rowspan="3" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
<td rowspan="3" >15T+
</td>
<td rowspan="3" >December 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
<tr>
<td>405B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
</table>
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** July 23, 2024.
**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
## How to use
This repository contains two versions of Meta-Llama-3.1-8B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3.1-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Tool use with transformers
LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).
Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers.
Here is a quick example showing a single simple tool:
```python
# First, define a tool
def get_current_temperature(location: str) -> float:
"""
Get the current temperature at a location.
Args:
location: The location to get the temperature for, in the format "City, Country"
Returns:
The current temperature at the specified location in the specified units, as a float.
"""
return 22. # A real function should probably actually get the temperature!
# Next, create a chat and apply the chat template
messages = [
{"role": "system", "content": "You are a bot that responds to weather queries."},
{"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]
inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
```
You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
```python
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
```
and then call the tool and append the result, with the `tool` role, like so:
```python
messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
```
After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3.1-8B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-8B-Instruct
```
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training utilized a cumulative of** 39.3M GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions** Estimated total location-based greenhouse gas emissions were **11,390** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy, therefore the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
<table>
<tr>
<td>
</td>
<td><strong>Training Time (GPU hours)</strong>
</td>
<td><strong>Training Power Consumption (W)</strong>
</td>
<td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
<td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3.1 8B
</td>
<td>1.46M
</td>
<td>700
</td>
<td>420
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 70B
</td>
<td>7.0M
</td>
<td>700
</td>
<td>2,040
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 405B
</td>
<td>30.84M
</td>
<td>700
</td>
<td>8,930
</td>
<td>0
</td>
</tr>
<tr>
<td>Total
</td>
<td>39.3M
<td>
<ul>
</ul>
</td>
<td>11,390
</td>
<td>0
</td>
</tr>
</table>
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023.
## Benchmark scores
In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="7" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>66.7
</td>
<td>66.7
</td>
<td>79.5
</td>
<td>79.3
</td>
<td>85.2
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>36.2
</td>
<td>37.1
</td>
<td>55.0
</td>
<td>53.8
</td>
<td>61.6
</td>
</tr>
<tr>
<td>AGIEval English
</td>
<td>3-5
</td>
<td>average/acc_char
</td>
<td>47.1
</td>
<td>47.8
</td>
<td>63.0
</td>
<td>64.6
</td>
<td>71.6
</td>
</tr>
<tr>
<td>CommonSenseQA
</td>
<td>7
</td>
<td>acc_char
</td>
<td>72.6
</td>
<td>75.0
</td>
<td>83.8
</td>
<td>84.1
</td>
<td>85.8
</td>
</tr>
<tr>
<td>Winogrande
</td>
<td>5
</td>
<td>acc_char
</td>
<td>-
</td>
<td>60.5
</td>
<td>-
</td>
<td>83.3
</td>
<td>86.7
</td>
</tr>
<tr>
<td>BIG-Bench Hard (CoT)
</td>
<td>3
</td>
<td>average/em
</td>
<td>61.1
</td>
<td>64.2
</td>
<td>81.3
</td>
<td>81.6
</td>
<td>85.9
</td>
</tr>
<tr>
<td>ARC-Challenge
</td>
<td>25
</td>
<td>acc_char
</td>
<td>79.4
</td>
<td>79.7
</td>
<td>93.1
</td>
<td>92.9
</td>
<td>96.1
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki
</td>
<td>5
</td>
<td>em
</td>
<td>78.5
</td>
<td>77.6
</td>
<td>89.7
</td>
<td>89.8
</td>
<td>91.8
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD
</td>
<td>1
</td>
<td>em
</td>
<td>76.4
</td>
<td>77.0
</td>
<td>85.6
</td>
<td>81.8
</td>
<td>89.3
</td>
</tr>
<tr>
<td>QuAC (F1)
</td>
<td>1
</td>
<td>f1
</td>
<td>44.4
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>51.1
</td>
<td>53.6
</td>
</tr>
<tr>
<td>BoolQ
</td>
<td>0
</td>
<td>acc_char
</td>
<td>75.7
</td>
<td>75.0
</td>
<td>79.0
</td>
<td>79.4
</td>
<td>80.0
</td>
</tr>
<tr>
<td>DROP (F1)
</td>
<td>3
</td>
<td>f1
</td>
<td>58.4
</td>
<td>59.5
</td>
<td>79.7
</td>
<td>79.6
</td>
<td>84.8
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B Instruct</strong>
</td>
<td><strong>Llama 3.1 8B Instruct</strong>
</td>
<td><strong>Llama 3 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 405B Instruct</strong>
</td>
</tr>
<tr>
<td rowspan="4" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc
</td>
<td>68.5
</td>
<td>69.4
</td>
<td>82.0
</td>
<td>83.6
</td>
<td>87.3
</td>
</tr>
<tr>
<td>MMLU (CoT)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>65.3
</td>
<td>73.0
</td>
<td>80.9
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>micro_avg/acc_char
</td>
<td>45.5
</td>
<td>48.3
</td>
<td>63.4
</td>
<td>66.4
</td>
<td>73.3
</td>
</tr>
<tr>
<td>IFEval
</td>
<td>
</td>
<td>
</td>
<td>76.8
</td>
<td>80.4
</td>
<td>82.9
</td>
<td>87.5
</td>
<td>88.6
</td>
</tr>
<tr>
<td rowspan="2" >Reasoning
</td>
<td>ARC-C
</td>
<td>0
</td>
<td>acc
</td>
<td>82.4
</td>
<td>83.4
</td>
<td>94.4
</td>
<td>94.8
</td>
<td>96.9
</td>
</tr>
<tr>
<td>GPQA
</td>
<td>0
</td>
<td>em
</td>
<td>34.6
</td>
<td>30.4
</td>
<td>39.5
</td>
<td>46.7
</td>
<td>50.7
</td>
</tr>
<tr>
<td rowspan="4" >Code
</td>
<td>HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>60.4
</td>
<td>72.6
</td>
<td>81.7
</td>
<td>80.5
</td>
<td>89.0
</td>
</tr>
<tr>
<td>MBPP ++ base version
</td>
<td>0
</td>
<td>pass@1
</td>
<td>70.6
</td>
<td>72.8
</td>
<td>82.5
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>Multipl-E HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>50.8
</td>
<td>-
</td>
<td>65.5
</td>
<td>75.2
</td>
</tr>
<tr>
<td>Multipl-E MBPP
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>52.4
</td>
<td>-
</td>
<td>62.0
</td>
<td>65.7
</td>
</tr>
<tr>
<td rowspan="2" >Math
</td>
<td>GSM-8K (CoT)
</td>
<td>8
</td>
<td>em_maj1@1
</td>
<td>80.6
</td>
<td>84.5
</td>
<td>93.0
</td>
<td>95.1
</td>
<td>96.8
</td>
</tr>
<tr>
<td>MATH (CoT)
</td>
<td>0
</td>
<td>final_em
</td>
<td>29.1
</td>
<td>51.9
</td>
<td>51.0
</td>
<td>68.0
</td>
<td>73.8
</td>
</tr>
<tr>
<td rowspan="4" >Tool Use
</td>
<td>API-Bank
</td>
<td>0
</td>
<td>acc
</td>
<td>48.3
</td>
<td>82.6
</td>
<td>85.1
</td>
<td>90.0
</td>
<td>92.0
</td>
</tr>
<tr>
<td>BFCL
</td>
<td>0
</td>
<td>acc
</td>
<td>60.3
</td>
<td>76.1
</td>
<td>83.0
</td>
<td>84.8
</td>
<td>88.5
</td>
</tr>
<tr>
<td>Gorilla Benchmark API Bench
</td>
<td>0
</td>
<td>acc
</td>
<td>1.7
</td>
<td>8.2
</td>
<td>14.7
</td>
<td>29.7
</td>
<td>35.3
</td>
</tr>
<tr>
<td>Nexus (0-shot)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>18.1
</td>
<td>38.5
</td>
<td>47.8
</td>
<td>56.7
</td>
<td>58.7
</td>
</tr>
<tr>
<td>Multilingual
</td>
<td>Multilingual MGSM (CoT)
</td>
<td>0
</td>
<td>em
</td>
<td>-
</td>
<td>68.9
</td>
<td>-
</td>
<td>86.9
</td>
<td>91.6
</td>
</tr>
</table>
#### Multilingual benchmarks
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Language</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="9" ><strong>General</strong>
</td>
<td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
</td>
<td>Portuguese
</td>
<td>62.12
</td>
<td>80.13
</td>
<td>84.95
</td>
</tr>
<tr>
<td>Spanish
</td>
<td>62.45
</td>
<td>80.05
</td>
<td>85.08
</td>
</tr>
<tr>
<td>Italian
</td>
<td>61.63
</td>
<td>80.4
</td>
<td>85.04
</td>
</tr>
<tr>
<td>German
</td>
<td>60.59
</td>
<td>79.27
</td>
<td>84.36
</td>
</tr>
<tr>
<td>French
</td>
<td>62.34
</td>
<td>79.82
</td>
<td>84.66
</td>
</tr>
<tr>
<td>Hindi
</td>
<td>50.88
</td>
<td>74.52
</td>
<td>80.31
</td>
</tr>
<tr>
<td>Thai
</td>
<td>50.32
</td>
<td>72.95
</td>
<td>78.21
</td>
</tr>
</table>
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
### Responsible deployment
Llama is a foundational technology designed to be used in a variety of use cases, examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models enabling the world to benefit from the technology power, by aligning our model safety for the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.
#### Llama 3.1 instruct
Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. For more details on the safety mitigations implemented please read the Llama 3 paper.
**Fine-tuning data**
We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone**
Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.1 systems
**Large language models, including Llama 3.1, are not designed to be deployed in isolation but instead should be deployed as part of an overall AI system with additional safety guardrails as required.** Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools.
As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard 3, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
#### New capabilities
Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.
**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
**Red teaming**
For both scenarios, we conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets.
We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical and other risks
We specifically focused our efforts on mitigating the following critical risk areas:
**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
To assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons.
**2. Child Safety**
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3. Cyber attack enablement**
Our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention.
Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3.1 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.1 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3.1 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.1’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.1 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
yvajaya/gtp2-geopo3 | yvajaya | 2024-10-19T19:57:40Z | 141 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T19:57:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mekjr1/ai-detect-2 | mekjr1 | 2024-10-19T19:55:36Z | 90 | 0 | transformers | [
"transformers",
"safetensors",
"longformer",
"text-classification",
"generated_from_trainer",
"base_model:allenai/longformer-base-4096",
"base_model:finetune:allenai/longformer-base-4096",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T13:45:50Z | ---
library_name: transformers
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ai-detect-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-detect-2
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6213
- Accuracy: 0.9234
- Precision: 0.8980
- Recall: 0.9901
- F1: 0.9418
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.1356 | 1.0 | 6250 | 0.2998 | 0.9344 | 0.9132 | 0.9891 | 0.9497 |
| 0.0932 | 2.0 | 12500 | 0.5421 | 0.9052 | 0.8764 | 0.9879 | 0.9288 |
| 0.0019 | 3.0 | 18750 | 0.3828 | 0.9338 | 0.9118 | 0.9899 | 0.9493 |
| 0.0678 | 4.0 | 25000 | 0.2624 | 0.953 | 0.9384 | 0.9899 | 0.9635 |
| 0.0006 | 5.0 | 31250 | 0.5998 | 0.9083 | 0.8760 | 0.9942 | 0.9314 |
| 0.0002 | 6.0 | 37500 | 0.4959 | 0.9384 | 0.9183 | 0.9896 | 0.9526 |
| 0.0371 | 7.0 | 43750 | 0.6213 | 0.9234 | 0.8980 | 0.9901 | 0.9418 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
tdnathmlenthusiast/speecht5_finetuned_voice_dataset_bn_v_2 | tdnathmlenthusiast | 2024-10-19T19:54:07Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2024-10-18T17:29:25Z | ---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_voice_dataset_bn_v_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_voice_dataset_bn_v_2
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5100
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 125
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5988 | 0.3487 | 250 | 0.5447 |
| 0.569 | 0.6975 | 500 | 0.5202 |
| 0.5604 | 1.0462 | 750 | 0.5109 |
| 0.5569 | 1.3949 | 1000 | 0.5100 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
Hhblvjgvg/myiaxd | Hhblvjgvg | 2024-10-19T19:32:37Z | 120 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T19:21:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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] |
riddhi01/phi-2-MedBot-2 | riddhi01 | 2024-10-19T19:30:42Z | 48 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T18:05:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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] |
AndreyRzhaksinskiy/CDS-starcoder2-Ins-7b-E2E-20241019 | AndreyRzhaksinskiy | 2024-10-19T19:21:20Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T17:57:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Jeevanwork/gemma-Instruct-Finetune-test-good | Jeevanwork | 2024-10-19T19:20:02Z | 138 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T19:07:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
enver/lisan3.2_3b_freshtokenizer | enver | 2024-10-19T19:00:21Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-19T18:58:05Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** enver
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
GGorman/WizardCoder-33B-V1.1-Q8-mlx | GGorman | 2024-10-19T18:45:31Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"mlx",
"base_model:WizardLMTeam/WizardCoder-33B-V1.1",
"base_model:quantized:WizardLMTeam/WizardCoder-33B-V1.1",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] | text-generation | 2024-10-19T18:43:31Z | ---
metrics:
- code_eval
library_name: transformers
tags:
- code
- mlx
base_model: WizardLMTeam/WizardCoder-33B-V1.1
model-index:
- name: WizardCoder
results:
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: pass@1
value: 0.799
name: pass@1
verified: false
---
# GGorman/WizardCoder-33B-V1.1-Q8-mlx
The Model [GGorman/WizardCoder-33B-V1.1-Q8-mlx](https://huggingface.co/GGorman/WizardCoder-33B-V1.1-Q8-mlx) was converted to MLX format from [WizardLMTeam/WizardCoder-33B-V1.1](https://huggingface.co/WizardLMTeam/WizardCoder-33B-V1.1) using mlx-lm version **0.19.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("GGorman/WizardCoder-33B-V1.1-Q8-mlx")
prompt="hello"
if hasattr(tokenizer, "apply_chat_template") and tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
imanoop7/bert-phishing-detector | imanoop7 | 2024-10-19T18:34:55Z | 249 | 1 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"phishing",
"url-classification",
"en",
"dataset:imanoop7/phishing_url_classification",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-19T18:30:00Z | ---
language: en
license: mit
tags:
- phishing
- url-classification
- bert
datasets:
- imanoop7/phishing_url_classification
metrics:
- accuracy
- auc
base_model:
- google-bert/bert-base-uncased
library_name: transformers
---
# BERT Phishing URL Detector
This model is a fine-tuned version of `bert-base-uncased` on a phishing URL classification dataset. It can be used to detect potentially malicious URLs.
## Model description
The model is based on BERT (Bidirectional Encoder Representations from Transformers) and has been fine-tuned for binary classification of URLs as safe or potentially phishing.
## Intended uses & limitations
This model is intended for use in detecting potentially malicious URLs. However, it should not be used as the sole method of protection against phishing attacks. Always use multiple layers of security and exercise caution when dealing with suspicious URLs.
## Training data
The model was trained on a custom dataset of URLs labeled as safe or unsafe. The dataset is available at `imanoop7/phishing_url_classification` on the Hugging Face Hub.
## Training procedure
The model was fine-tuned using the Hugging Face Transformers library. Only the pooling layers were fine-tuned while the base BERT layers were frozen. |
Harshatheeswar/gpt2-scratch | Harshatheeswar | 2024-10-19T18:30:05Z | 41 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:Harshatheeswar/gpt2-scratch",
"base_model:finetune:Harshatheeswar/gpt2-scratch",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-11T21:36:34Z | ---
library_name: transformers
base_model: Harshatheeswar/gpt2-scratch
tags:
- generated_from_trainer
model-index:
- name: gpt2-scratch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-scratch
This model is a fine-tuned version of [Harshatheeswar/gpt2-scratch](https://huggingface.co/Harshatheeswar/gpt2-scratch) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1380
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.2094 | 1.0 | 1390 | 4.1380 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
AhmedBou/Arabic-Medicine-Meta-Llama-3.2-3B-GGUF | AhmedBou | 2024-10-19T18:23:18Z | 234 | 2 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-3B-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-19T18:21:04Z | ---
base_model: unsloth/Llama-3.2-3B-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** AhmedBou
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
wedobetterit/trained | wedobetterit | 2024-10-19T18:17:55Z | 140 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-11T22:48:03Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5b
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: trained
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# trained
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5b](https://huggingface.co/Qwen/Qwen2.5-0.5b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
meandyou200175/vn_bi_encoder_med | meandyou200175 | 2024-10-19T18:09:24Z | 8 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:43804",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:bkai-foundation-models/vietnamese-bi-encoder",
"base_model:finetune:bkai-foundation-models/vietnamese-bi-encoder",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-10-17T18:12:57Z | ---
base_model: bkai-foundation-models/vietnamese-bi-encoder
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:43804
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: Nhờ bác sĩ cho biết việc lựa chọn đóng đinh nội tủy và nẹp vít
để kết hợp xương đòn dựa trên cơ sở nào ạ? Ca phẫu thuật thường kéo dài trong
bao lâu? Bệnh nhân nằm viện mấy ngày?
sentences:
- ' Chào em, là bệnh mãn tính phải điều trị suốt đời, phải kiên nhẫn và kiên trì
nên đôi khi lượng đường trong cơ thể không ổn định. Lúc đi khám xét nghiệm thì
ổn do bản thân biết mai đi khám nên sẽ kiêng ăn, ăn ít... còn bệnh lâu dài nên
trong ngày đôi khi thèm chút này hay thích ăn chút kia, quên uống thuốc, suy
nghĩ, mất ngủ cũng làm đường không ổn định. Đường trong cơ thể lúc lên lúc xuống
dễ đưa đến biến chứng. Em hay thấy bệnh nhân tiểu đường tháo khớp ngón chân, ngón
tay, đôi khi tháo khớp gối, khớp háng, đây là do tê liệt hệ thần kinh nên khi
va chạm bệnh nhân không phát hiện. Đến khi phát hiện thì đã nhiễm trùng nặng phải
tháo khớp. Theo BS mẹ em có khả năng do biến chứng tiểu đường vì mẹ em bị bệnh
khá lâu nên ít nhiều ảnh hưởng thần kinh bị tê liệt gây đau. Em nên nhớ dặn mẹ
đi tái khám và điều trị cho thật ổn định nhé! Thân mến!'
- ' Để lựa chọn phương pháp đóng đinh nội tủy hay nẹp vít cho bệnh nhân cần dựa
vào nhiều yếu tố. Trong lòng tủy xương có một cái ống, nếu lòng tủy bệnh nhân
nhỏ mà đường gãy không bị gãy thành nhiều mảnh thì nên lựa chọn phương pháp đóng
đinh. Phương pháp này có nhược điểm dễ bị lộ phần đinh khi đinh vừa đóng, chưa
chắc vào xương. Tuy nhiên, ưu điểm là khi đóng đinh, đường mổ sẽ nhỏ, đơn giản.
Đối với nẹp vít, đường mổ dài hơn nhưng phần nắn chỉnh sẽ tuyệt đối, vững chắc
hơn. Nhìn chung, giữa 2 phương pháp thời gian mổ không khác biệt nhau nhiều, từ
30-45 phút sẽ hoàn thành cuộc phẫu thuật kết hợp xương. Tại bệnh viện Nhân dân
115, sau khi bệnh nhân được làm phẫu thuật có thể xuất viện rất sớm trong vòng
khoảng 3-5 ngày, tùy theo đường mổ lớn hay nhỏ. Giữa việc lựa chọn phẫu thuật
hay bảo tồn, đinh nội tủy hay nẹp vít phụ thuộc vào lòng tủy của bệnh nhân và
thói quen, sự đánh giá của phẫu thuật viên. Cá nhân tôi thường lựa chọn phương
pháp phẫu thuật nẹp vít sẽ cho kết quả nắn chỉnh tốt, chắc hơn và bệnh nhân không
bị biến chứng trồi đinh về sau. Thân mến.'
- Chào em, Tình trạng người mệt mỏi, khó thở, tim đập nhanh xảy ra khi không gắng
sức có thể do nhiều nguyên nhân, gồm tim mạch, hô hấp, thần kinh cơ, tiêu hóa
(chủ yếu là ống tiêu hóa trên), tâm lý, bệnh lý nội tiết tố… Viêm dạ dày trào
ngược có thể gây các triệu chứng này do dịch acid trào ngược từ dạ dày lên thực
quản kích thích thần kinh tim. Mặt khác bệnh dạ dày là bệnh có thể tái phát, điều
trị hết bệnh rồi thì bệnh vẫn có thể tái lại. Do đó, nếu em đã khám tim mạch và
hô hấp bình thường, để biết có phải mình mệt mỏi do bệnh dạ dày gây ra hay không
thì tốt nhất là em khám chuyên khoa nội tiêu hóa và điều trị trào ngược dạ dày
thực quản thử, nếu triệu chứng cải thiện nhanh chóng thì chính hắn là nguyên nhân,
em nhé.
- source_sentence: Tôi bị tình trạng nuốt nước miếng có cảm giác bị vướng ở cổ, không
đau rát, không ho sốt, ăn uống bình thường đã 1 ngày nay. Chỉ có nuốt nước miếng
là có cảm giác vướng thôi, lỗ tai bên trái thì cảm giác ngứa nhẹ. Xin hỏi là bệnh
gì vậy ạ?
sentences:
- "Em Lan thân mến, Hiện nay, xét nghiệm được xem là một xét nghiệm\r\nthường quy,\
\ nên thai kỳ của em cũng rất cần được làm những xét nghiệm này mặc\r\ndù gia\
\ đình em không có bệnh lý bất thường. Tuy nhiên, thai kỳ của em đã qua thời gian\
\ làm xét nghiệm Double test, bây\r\ngiờ em phải chờ đến lúc thai được 16 – 18\
\ tuần tuổi, làm xét nghiệm Triple test\r\nem nhé! Chúc em và bé khỏe mạnh!"
- 'Trường hợp thoái hóa cột sống thắt lưng gây đau mỏi liên tục dù đã dùng thuốc
giảm đau liều cao Chào em, Thoái hóa khớp, thoái hóa cột sống là tiến trình lão
hóa không thể tránh khỏi của con người, đặc biệt có thể xảy ra sớm và nhanh hơn
ở người nữ sau mãn kinh, sinh nở nhiều, suy dinh dưỡng hay ăn uống thiếu chất
khoáng, lao động vất vả lúc còn trẻ. Trường hợp thoái hóa cột sống thắt lưng gây
đau mỏi liên tục dù đã dùng thuốc giảm đau liều cao, đặc biệt là đau lan xuống
hai chân, tê yếu hai chân thì cần chụp MRI cột sống để tầm soát thoát vị đĩa đệm
chèn ép tủy sống. Trường hợp của em, mới phát hiện thoái hóa cột sống thắt lưng
gần đây, cũng mới uống thuốc 1 tuần và không duy trì nữa, việc đau lưng vẫn còn
âm ỉ nhưng không lan xuống hai chân thì chưa đến mức cần chụp MRI cột sống thắt
lưng. Nhưng mà, em cần tích cực điều trị để bệnh thoái hóa cột sống thắt lưng
không tiến triển nặng hơn. Bệnh này trị khỏi hoàn toàn là không thể, vì sinh lão
bệnh tử không thể cải hoàn, nhưng mà việc điều trị tích cực sẽ giúp khống chế
được bệnh, giảm đau và giảm tốc độ tiến triển của bệnh. Về việc sử dụng thuốc,
dù là thuốc Tây hay thuốc Đông y, em cũng cần phải thăm khám bs ck cơ xương khớp
(Tây y) hay ck y học cổ truyền (Đông y) để được kê thuốc phù hợp. các thuốc thường
dùng là giảm đau, giãn cơ, bổ sung vi khoáng chất (canxi, vitamin D3, magie...).
Bên cạnh đó, về phương pháp giảm đau hỗ trợ không dùng thuốc, em nên chú ý: -
Chú ý thay đổi tư thế trong quá trình làm việc, không giữ mãi một tư thế trong
nhiều giờ liền. Ngồi làm việc đúng tư thế để tránh các bệnh cột sống. - Vận động
đúng cách, khi vác vật nặng không vặn cột sống. - Thường xuyên tập thể dục rèn
luyện để cột sống vững chắc, cơ thể dẻo dai, bơi cũng được mà yoga là tốt nhất.
- Ăn uống khoa học, xây dựng chế độ dinh dưỡng hợp lý, tăng cường nhóm thực phẩm
giàu canxi, vitamin D, omega 3… giúp nâng cao độ chắc khỏe của đĩa đệm cũng như
xương khớp. - Duy trì cân nặng bình thường, tránh để tăng cân quá mức. - Tư thế
ngủ: nằm ngửa trên ván cứng hay nệm bông ép chặt, tránh nệm lò xo hay nệm cao
su quá mềm, có thể đệm ở vùng khoeo làm co nhẹ khớp gối và khớp háng, nên nằm
đầu thấp không gối sẽ tốt cho cột sống cổ. - Có thể thực hiện điều trị vật lý
và các liệu pháp phản xạ: bao gồm phương pháp nhiệt như chườm nóng (túi nước,
muối rang, cám rang, lá lốt, lá ngải cứu nóng); dùng các dòng điện tại khoa vật
lý trị liệu, điều trị bằng laser; châm cứu, kéo cơ để hỗ trợ giảm đau cơ cạnh
sống. Trân trọng!'
- Chào bạn, Nuốt vướng ở cổ thường gặp trong một số bệnh lý viêm nhiễm hầu họng
như viêm họng, viêm amidan mạn, trào ngược dạ dày thực quản, hội chứng chảy mũi
sau… Đây là có thể là triệu chứng đầu tiên báo hiệu một đợt bùng phát cấp tính
của viêm nhiễm hô hấp trên do triệu chứng mới chỉ xuất hiện 1 ngày. Bạn nên khám
bác sĩ Tai mũi họng để thăm khám trực tiếp, đánh giá và kê toa điều trị bạn nhé!
Thân mến.
- source_sentence: Chào bác sĩ, em bị gãy xương gót, đã đóng đinh đến nay được gần
5 tuần. Vậy 6 tuần em tháo đinh được chưa ạ?
sentences:
- ' Chào em, gồm 2 trị số, trị số lớn nhất gọi là huyết áp tâm thu, bình thường
< 140 và > 90 mmHg; trị số thấp nhất gọi là huyết áp tâm trương, bình thường <
90 và > 60 mmHg. Huyết áp có thể tăng khi căng thẳng, do lo lắng, do hội chứng
áo choàng trắng (khi vào bv, khi gặp bác sĩ thì huyết áp cao), bệnh lý viêm nhiễm,
do cafe, khi khó thở... nhìn chung là các stress đối với cơ thể. Như vậy, huyết
áp ghi nhận ở những lúc cơ thể đang lo lắng, bồn chồn, có bệnh thì sẽ không phản
ánh chính xác được huyết áp dao động bình thường của người bệnh. Do vậy em nên
khám chuyên khoa tim mạch, bác sĩ sẽ thăm khám và làm xét nghiệm kiểm tra xem
em có các dấu chứng của tăng huyết áp hay không (như dày thành tim, tiểu đạm,
đo huyết áp 24 giờ...) để xác định em có tăng huyết áp hay không và điều trị thích
hợp. Những triệu chứng hoa mắt, chóng mặt, đau đầu, đau 1 bên mắt, tiểu nhiều
có thể là do bệnh tăng huyết áp gây ra (ảnh hưởng lên mạch máu não, lên thận...)
hoặc là 1 bệnh lý khác như thiếu máu, rối loạn tiền đình, viêm nhiễm hệ thống,
viêm mũi xoang, bệnh lý mạch máu não... (và tăng huyết áp chỉ là phản ứng của
cơ thể khi có stress). Để tìm ra bệnh và giải quyết nỗi lo về bệnh, em nên đến
bệnh viện để kiểm tra sức khỏe em nhé. Thân mến! '
- ' Chào em, Thời điểm 6 tuần là quá sớm để rút đinh cố định xương gót (trừ trường
hợp khung cố định xương bên ngoài). Tháo đinh vít kim loại chỉ bắt buộc thực hiện
sớm trong những trường hợp bất thường như gãy vít, nhiễm trùng, khớp giả... gây
ra các triệu chứng bất thường với bệnh nhân mà thôi. Em nên tái khám tại chuyên
khoa Chấn thương Chỉnh hình để bác sĩ kiểm tra lại việc lành xương của em tốt
chưa và dặn em lịch trình rút đinh phù hợp, em nhé. Thân mến.'
- K dạ dày không điều trị tiên lượng sống khá ngắn Chào em, K dạ dày là ung thư
dạ dày. Bệnh ung thư dạ dày là bệnh lý ác tính và có chỉ định phẫu thuật cắt khối
u – cắt dạ dày khi còn có thể cắt được. Nếu đã phát hiện ung thư dạ dày mà không
điều trị phẫu thuật thì thời gian sống của bệnh nhân trung bình là 6 tháng đến
1 năm tùy loại ung thư dạ dày, khi ung thư tiến triển di căn có thể gây nhiều
đau đớn hơn. Hiện tại chị em đang bị suy nhược cơ thể nhiều, không ăn uống được,
đau nhiều do ung thư dạ dày là có chỉ định vào bệnh viện nằm điều trị luôn rồi,
chứ không thể nào lấy thuốc mà không tới phòng khám được đâu. Vô bệnh viện chị
em sẽ được truyền dịch, chích thuốc, nâng thể trạng lên rồi mới tính đến chuyện
điều trị khối ung thư kia. Em đưa chị em đến bệnh viện càng sớm càng tốt, tốt
nhất là bệnh viện Ung bướu, em nhé.
- source_sentence: "Thưa bác sĩ,\r\n\r\nEm bị đục thủy tinh thể do chấn thương và\
\ vừa mổ mắt về và em cũng bị cận thị. Thời gian khoảng 1 tuần em thấy mắt mình\
\ nhìn chỉ rõ hơn được 1 phần nào. Nhìn xa thì vẫn thấy nhưng vẫn mờ mờ. Bác sĩ\
\ cho em lời khuyên nên làm cách nào và mắt em có thể sáng lại như bình thường\
\ được không ạ?\r\n\r\nEm xin chân thành cảm ơn! (Minh Tiến - Bình Định)"
sentences:
- Bạn Minh Tiến thân mến, Hiện nay phẫu thuật đục thủy tinh thể đã được y học nói
chung và ngành Nhãn khoa Việt Nam thực hiện hoàn chỉnh đến mức tuyệt vời. Phẫu
thuật này được xem như một cuộc cách mạng rất đáng tự hào của ngành nhãn khoa.
Hàng ngày có thể tới hàng ngàn ca phẫu thuật đem lại ánh sáng cho người mù lòa
đục thể thủy tinh tại Việt Nam. Nói như vậy để giúp cho bạn hiểu rõ phẫu thuật
này các bác sĩ Việt Nam thực hiện rất thường xuyên và rất tốt. Tuy nhiên, với
mắt đục thủy tinh thể do chấn thương của bạn là ca phẫu thuật tương đối không
đơn giản. Thêm vào đó ngoài đục thủy tinh thể do chấn thương, mắt bạn cũng có
thể kèm theo tổn thương ở các bộ phận khác của mắt mà trước mổ bác sĩ khó có thể
chẩn đoán được. Với hai lý do nêu trên, nên đôi khi mắt mổ khó có thể tốt theo
ý muốn của cả bệnh nhân lẫn thầy thuốc. Bạn cần có thời gian theo dõi và điều
trị tiếp sau mổ. Sau thời gian ổn định khoảng 1 tháng, bạn cần đo thử kính xem
có cải thiện thị lực thêm không? Chúc bạn may mắn!
- Chào em, Bình thường các hạch trong cơ thể không sưng to lên đến mức có thể sờ
chạm hay nhận biết được. Vì thế, hạch sưng lên, hay thường gọi là nổi hạch, là
một triệu chứng bất thường của cơ thể. Cho nên, em lo lắng là đúng khi phát hiện
hạch ở vùng cổ. Hạch bạch huyết đóng vai trò quan trọng đối với hoạt động của
hệ miễn dịch. Chúng chứa các tế bào miễn dịch như lympho bào, đại thực bào...
có chức năng miễn dịch chống lại các yếu tố lạ như vi khuẩn, virus, kí sinh trùng...
xâm nhập vào cơ thể. Trong quá trình đó các hạch có thể bị viêm và sưng lên. Một
số trường hợp hạch sưng có thể là hạch ung thư hoặc di căn. Đặc điểm của hạch
viêm là nhỏ, số lượng ít, bờ tròn đều, không phát triển theo thời gian, không
xâm lấn da xung quanh. Thông thường đối với hạch viêm thì nguồn viêm có thể tấn
công tại hạch, cũng có khi là hạch viêm phản ứng với ổ viêm nhiễm cạnh đó, điều
trị hết viêm thì hạch sẽ lặn dần, có thể lặn chậm hơn vài tuần đến vài tháng,
có một số loại hạch cũng là hạch viêm nhưng mà chỉ giảm kích thước rồi cứ "lì"
vậy luôn - không lặn hẳn nhưng không còn sưng như trước và vẫn giữ hình ảnh của
hạch viêm, cũng có loại hạch viêm sau lại chuyển sang xơ chai hóa như sẹo cũ và
không lặn. Như vậy, em có 1 hạch vùng cổ đã được xác định là hạch viêm thông qua
sinh thiết hạch cách đây 10 năm. Trong vòng 10 năm nay, hạch cổ đó không có triệu
chứng bất thường. Gần đây, hạch cổ đó có biểu hiện viêm trở lại, mặc dù em uống
thuốc (tự mua) thì hạch hết sưng đau, nhưng em cũng cần khám lại bên chuyên khoa
ung bướu để kiểm tra tổng quát lại 1 lần, tìm nguyên nhân gây kích thích hạch
viêm này tái hoạt động, xem là nguyên nhân lành tính hay tiềm ẩn nguyên nhân khác
(vì lần kiểm tra trước đã cách đây 10 năm rồi), em nhé.
- ' Chào em, Trường hợp em mô tả là những bất thường của hệ hô hấp có thể là bệnh
lý tai mũi họng hay hô hấp dưới như viêm phổi, viêm phế quản, em cần đến các cơ
sở y tế chuyên sâu tai mũi họng hay hô hấp để khám thêm. Những biểu hiện đó hoàn
toàn không có cơ sở nghĩ . Thân mến!'
- source_sentence: Bác sĩ cho em hỏi, em bị rạn nứt xương gót chân bên phải. Em bị
hơn 1 tháng nay rồi. Em bỏ thuốc lá. Em muốn hỏi bác sĩ thông thường bó bột hơn
hay thuốc lá hơn? Như của em khoảng bao lâu thì khỏi? Và giờ em vẫn chưa đi được
bác sĩ ạ. Em cảm ơn.
sentences:
- 'Câu hỏi của em rất chân thành. Tự ý thức quyết tâm cai nghiệm là điều đáng quý.
Nếu em tiếp tục sử dụng thì tình trạng sẽ tồi tệ hơn rất nhiều. Ba yếu tố quan
trọng nhất và tiến hành đồng thời để cai nghiện thành công, đó là: 1. Ý chí 2.
Sự hiểu biết thấu đáo 3. Môi trường thân thiện. Các Trung tâm cai nghiện sẽ giúp
em phần 2 và phần 3, từ đó sẽ củng cố phần 1 của em. Trường hợp ở nhà mà em tự
cai, thực hành mỗi ngày với 3 điều kiện trên, em sẽ thành công như nhiều bạn khác.
Không nên nôn nóng, sốt ruột. Trước tiên em phải thuộc lòng và thực hành những
quy tắc này thành thói quen và áp dụng suốt đời. Nhiều trường hợp cai được vài
năm vẫn tái nghiện. Do đó, nên tránh xa những "nguồn" khiến em tái nghiện, tránh
xa bạn bè nghiện ngập em nhé. Chúc em quyết tâm và đem lại niềm vui cho bố mẹ.'
- Chào em, Thứ nhất, bắt buộc phải có phim Xquang để biết em có thực sự nứt xương
gót hay bị gãy phức tạp hơn, vì nhiều trường hợp tưởng chỉ nứt xương thôi nhưng
thật ra là vỡ phức tạp, phải phẫu thuật mới nhanh ổn được. Thứ hai, theo nguyên
tắc điều trị nứt gãy xương là phải cố định tốt để can xương mọc ra, chỗ nứt gãy
mới được nối liền. Do đó, nếu bó bột thì chân sẽ được cố định liên tục trong 4-6
tuần, còn bó lá thì phải thay thường xuyên, mỗi lần thay là 1 lần xê dịch nên
xương khó lành. Tốt hơn hết em nên đến Bệnh viện Chấn thương Chỉnh hình để được
kiểm tra và điều trị thích hợp, em nhé. Thân mến.
- Chào bạn, Qua hình ảnh sang thương và mô tả triệu chứng, bệnh lý của bạn có khả
năng là chàm hay còn gọi là viêm da dị ứng với đặc điểm là viêm và nổi mụn nhỏ,
ngứa ngáy. Nguyên nhân của chàm hiện nay chưa rõ nhưng có thể do cơ địa dị ứng
(người mắc hen, viêm mũi dị ứng có nguy cơ cao mắc chàm), do kích thích của hóa
chất như nước rửa chén, bột giặt, cao su, kim loại, chất liệu giày dép (chàm tiếp
xúc),... Thời tiết lạnh, stress, đổ mồ hôi nhiều và phấn hoa... cũng là những
nguyên nhân có thể khiến da bị chàm. Chàm cũng có thể gặp ở người bị suy van tĩnh
mạch, giãn tĩnh mạch chân khiến tình trạng bệnh dai dẳng, kém đáp ứng điều trị.
Điều trị chàm thường phải sử dụng một số loại thuốc bôi da kéo dài, có thể để
lại tác dụng phụ, do đó bạn nên khám BS Da liễu để kê toa loại thuốc phù hợp.
Ngoài ra, bạn nên chú ý xem có yếu tố nào thường kích thích khởi phát chàm để
tránh cho bệnh tái phát bạn nhé! Thân mến.
---
# SentenceTransformer based on bkai-foundation-models/vietnamese-bi-encoder
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [bkai-foundation-models/vietnamese-bi-encoder](https://huggingface.co/bkai-foundation-models/vietnamese-bi-encoder). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [bkai-foundation-models/vietnamese-bi-encoder](https://huggingface.co/bkai-foundation-models/vietnamese-bi-encoder) <!-- at revision 84f9d9ada0d1a3c37557398b9ae9fcedcdf40be0 -->
- **Maximum Sequence Length:** 256 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("meandyou200175/vn_bi_encoder_med")
# Run inference
sentences = [
'Bác sĩ cho em hỏi, em bị rạn nứt xương gót chân bên phải. Em bị hơn 1 tháng nay rồi. Em bỏ thuốc lá. Em muốn hỏi bác sĩ thông thường bó bột hơn hay thuốc lá hơn? Như của em khoảng bao lâu thì khỏi? Và giờ em vẫn chưa đi được bác sĩ ạ. Em cảm ơn.',
'Chào em, Thứ nhất, bắt buộc phải có phim Xquang để biết em có thực sự nứt xương gót hay bị gãy phức tạp hơn, vì nhiều trường hợp tưởng chỉ nứt xương thôi nhưng thật ra là vỡ phức tạp, phải phẫu thuật mới nhanh ổn được. Thứ hai, theo nguyên tắc điều trị nứt gãy xương là phải cố định tốt để can xương mọc ra, chỗ nứt gãy mới được nối liền. Do đó, nếu bó bột thì chân sẽ được cố định liên tục trong 4-6 tuần, còn bó lá thì phải thay thường xuyên, mỗi lần thay là 1 lần xê dịch nên xương khó lành. Tốt hơn hết em nên đến Bệnh viện Chấn thương Chỉnh hình để được kiểm tra và điều trị thích hợp, em nhé. Thân mến.',
'Chào bạn, Qua hình ảnh sang thương và mô tả triệu chứng, bệnh lý của bạn có khả năng là chàm hay còn gọi là viêm da dị ứng với đặc điểm là viêm và nổi mụn nhỏ, ngứa ngáy. Nguyên nhân của chàm hiện nay chưa rõ nhưng có thể do cơ địa dị ứng (người mắc hen, viêm mũi dị ứng có nguy cơ cao mắc chàm), do kích thích của hóa chất như nước rửa chén, bột giặt, cao su, kim loại, chất liệu giày dép (chàm tiếp xúc),... Thời tiết lạnh, stress, đổ mồ hôi nhiều và phấn hoa... cũng là những nguyên nhân có thể khiến da bị chàm. Chàm cũng có thể gặp ở người bị suy van tĩnh mạch, giãn tĩnh mạch chân khiến tình trạng bệnh dai dẳng, kém đáp ứng điều trị. Điều trị chàm thường phải sử dụng một số loại thuốc bôi da kéo dài, có thể để lại tác dụng phụ, do đó bạn nên khám BS Da liễu để kê toa loại thuốc phù hợp. Ngoài ra, bạn nên chú ý xem có yếu tố nào thường kích thích khởi phát chàm để tránh cho bệnh tái phát bạn nhé! Thân mến.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:-----:|:-------------:|:---------------:|
| 0.0365 | 100 | 0.4427 | 0.3117 |
| 0.0730 | 200 | 0.3028 | 0.2373 |
| 0.1096 | 300 | 0.2468 | 0.2063 |
| 0.1461 | 400 | 0.2434 | 0.1857 |
| 0.1826 | 500 | 0.2075 | 0.1724 |
| 0.2191 | 600 | 0.185 | 0.1612 |
| 0.2557 | 700 | 0.183 | 0.1509 |
| 0.2922 | 800 | 0.1823 | 0.1445 |
| 0.3287 | 900 | 0.1663 | 0.1370 |
| 0.3652 | 1000 | 0.1622 | 0.1311 |
| 0.4018 | 1100 | 0.1361 | 0.1312 |
| 0.4383 | 1200 | 0.1834 | 0.1247 |
| 0.4748 | 1300 | 0.1558 | 0.1320 |
| 0.5113 | 1400 | 0.1289 | 0.1207 |
| 0.5478 | 1500 | 0.1424 | 0.1192 |
| 0.5844 | 1600 | 0.1412 | 0.1224 |
| 0.6209 | 1700 | 0.1349 | 0.1084 |
| 0.6574 | 1800 | 0.1275 | 0.1051 |
| 0.6939 | 1900 | 0.1266 | 0.1013 |
| 0.7305 | 2000 | 0.1245 | 0.1047 |
| 0.7670 | 2100 | 0.114 | 0.0931 |
| 0.8035 | 2200 | 0.1164 | 0.0925 |
| 0.8400 | 2300 | 0.1136 | 0.0990 |
| 0.8766 | 2400 | 0.1012 | 0.0907 |
| 0.9131 | 2500 | 0.1273 | 0.0889 |
| 0.9496 | 2600 | 0.1374 | 0.0918 |
| 0.9861 | 2700 | 0.1135 | 0.0881 |
| 1.0226 | 2800 | 0.0925 | 0.0875 |
| 1.0592 | 2900 | 0.0854 | 0.0891 |
| 1.0957 | 3000 | 0.0953 | 0.0847 |
| 1.1322 | 3100 | 0.075 | 0.0798 |
| 1.1687 | 3200 | 0.0811 | 0.0776 |
| 1.2053 | 3300 | 0.0729 | 0.0768 |
| 1.2418 | 3400 | 0.0626 | 0.0784 |
| 1.2783 | 3500 | 0.0639 | 0.0784 |
| 1.3148 | 3600 | 0.0658 | 0.0801 |
| 1.3514 | 3700 | 0.0516 | 0.0741 |
| 1.3879 | 3800 | 0.0327 | 0.0760 |
| 1.4244 | 3900 | 0.064 | 0.0753 |
| 1.4609 | 4000 | 0.0366 | 0.0738 |
| 1.4974 | 4100 | 0.035 | 0.0755 |
| 1.5340 | 4200 | 0.0327 | 0.0754 |
| 1.5705 | 4300 | 0.0301 | 0.0759 |
| 1.6070 | 4400 | 0.0304 | 0.0736 |
| 1.6435 | 4500 | 0.02 | 0.0773 |
| 1.6801 | 4600 | 0.0319 | 0.0720 |
| 1.7166 | 4700 | 0.026 | 0.0693 |
| 1.7531 | 4800 | 0.0267 | 0.0756 |
| 1.7896 | 4900 | 0.0252 | 0.0686 |
| 1.8262 | 5000 | 0.0236 | 0.0721 |
| 1.8627 | 5100 | 0.0217 | 0.0752 |
| 1.8992 | 5200 | 0.0259 | 0.0696 |
| 1.9357 | 5300 | 0.0391 | 0.0713 |
| 1.9722 | 5400 | 0.0309 | 0.0778 |
| 2.0088 | 5500 | 0.0246 | 0.0733 |
| 2.0453 | 5600 | 0.0214 | 0.0674 |
| 2.0818 | 5700 | 0.0217 | 0.0706 |
| 2.1183 | 5800 | 0.0174 | 0.0722 |
| 2.1549 | 5900 | 0.02 | 0.0682 |
| 2.1914 | 6000 | 0.0174 | 0.0705 |
| 2.2279 | 6100 | 0.0191 | 0.0695 |
| 2.2644 | 6200 | 0.0165 | 0.0727 |
| 2.3009 | 6300 | 0.0174 | 0.0698 |
| 2.3375 | 6400 | 0.0188 | 0.0667 |
| 2.3740 | 6500 | 0.0095 | 0.0673 |
| 2.4105 | 6600 | 0.0138 | 0.0679 |
| 2.4470 | 6700 | 0.0126 | 0.0659 |
| 2.4836 | 6800 | 0.0093 | 0.0674 |
| 2.5201 | 6900 | 0.0065 | 0.0696 |
| 2.5566 | 7000 | 0.0089 | 0.0684 |
| 2.5931 | 7100 | 0.0068 | 0.0670 |
| 2.6297 | 7200 | 0.007 | 0.0689 |
| 2.6662 | 7300 | 0.0095 | 0.0676 |
| 2.7027 | 7400 | 0.0068 | 0.0667 |
| 2.7392 | 7500 | 0.0079 | 0.0666 |
| 2.7757 | 7600 | 0.008 | 0.0653 |
| 2.8123 | 7700 | 0.0071 | 0.0670 |
| 2.8488 | 7800 | 0.007 | 0.0677 |
| 2.8853 | 7900 | 0.0087 | 0.0670 |
| 2.9218 | 8000 | 0.0104 | 0.0660 |
| 2.9584 | 8100 | 0.0086 | 0.0665 |
| 2.9949 | 8200 | 0.0078 | 0.0654 |
| 3.0314 | 8300 | 0.0071 | 0.0688 |
| 3.0679 | 8400 | 0.0054 | 0.0679 |
| 3.1045 | 8500 | 0.0059 | 0.0657 |
| 3.1410 | 8600 | 0.0049 | 0.0629 |
| 3.1775 | 8700 | 0.0043 | 0.0625 |
| 3.2140 | 8800 | 0.0057 | 0.0634 |
| 3.2505 | 8900 | 0.0062 | 0.0646 |
| 3.2871 | 9000 | 0.0051 | 0.0658 |
| 3.3236 | 9100 | 0.0044 | 0.0652 |
| 3.3601 | 9200 | 0.0054 | 0.0649 |
| 3.3966 | 9300 | 0.0032 | 0.0647 |
| 3.4332 | 9400 | 0.0045 | 0.0651 |
| 3.4697 | 9500 | 0.0036 | 0.0634 |
| 3.5062 | 9600 | 0.0036 | 0.0629 |
| 3.5427 | 9700 | 0.0037 | 0.0625 |
| 3.5793 | 9800 | 0.0026 | 0.0624 |
| 3.6158 | 9900 | 0.0021 | 0.0628 |
| 3.6523 | 10000 | 0.0028 | 0.0621 |
| 3.6888 | 10100 | 0.0028 | 0.0622 |
| 3.7253 | 10200 | 0.0027 | 0.0616 |
| 3.7619 | 10300 | 0.0037 | 0.0647 |
| 3.7984 | 10400 | 0.0026 | 0.0621 |
| 3.8349 | 10500 | 0.0029 | 0.0623 |
| 3.8714 | 10600 | 0.0027 | 0.0649 |
| 3.9080 | 10700 | 0.0028 | 0.0631 |
| 3.9445 | 10800 | 0.0031 | 0.0630 |
| 3.9810 | 10900 | 0.0027 | 0.0638 |
| 4.0175 | 11000 | 0.0025 | 0.0636 |
| 4.0541 | 11100 | 0.0021 | 0.0623 |
| 4.0906 | 11200 | 0.0027 | 0.0637 |
| 4.1271 | 11300 | 0.0022 | 0.0636 |
| 4.1636 | 11400 | 0.0023 | 0.0620 |
| 4.2001 | 11500 | 0.0021 | 0.0609 |
| 4.2367 | 11600 | 0.0029 | 0.0608 |
| 4.2732 | 11700 | 0.0021 | 0.0621 |
| 4.3097 | 11800 | 0.0022 | 0.0619 |
| 4.3462 | 11900 | 0.0018 | 0.0621 |
| 4.3828 | 12000 | 0.0015 | 0.0615 |
| 4.4193 | 12100 | 0.0018 | 0.0632 |
| 4.4558 | 12200 | 0.002 | 0.0634 |
| 4.4923 | 12300 | 0.0018 | 0.0621 |
| 4.5289 | 12400 | 0.0014 | 0.0623 |
| 4.5654 | 12500 | 0.0016 | 0.0623 |
| 4.6019 | 12600 | 0.0013 | 0.0616 |
| 4.6384 | 12700 | 0.0013 | 0.0620 |
| 4.6749 | 12800 | 0.0016 | 0.0627 |
| 4.7115 | 12900 | 0.0014 | 0.0615 |
| 4.7480 | 13000 | 0.0016 | 0.0612 |
| 4.7845 | 13100 | 0.0015 | 0.0615 |
| 4.8210 | 13200 | 0.0012 | 0.0614 |
| 4.8576 | 13300 | 0.002 | 0.0615 |
| 4.8941 | 13400 | 0.0015 | 0.0617 |
| 4.9306 | 13500 | 0.0016 | 0.0613 |
| 4.9671 | 13600 | 0.0016 | 0.0614 |
</details>
### Framework Versions
- Python: 3.10.14
- Sentence Transformers: 3.2.0
- Transformers: 4.45.1
- PyTorch: 2.4.0
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
yvajaya/gtp2-geopo | yvajaya | 2024-10-19T17:27:31Z | 168 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T17:27:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
generalbakudan/gemma-2b-instruct-ft-medical-qa | generalbakudan | 2024-10-19T17:18:14Z | 141 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T17:10:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
falanaja/helmgojekid | falanaja | 2024-10-19T17:11:50Z | 7 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-19T16:27:54Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: helmgojekid
---
# Helmgojekid
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `helmgojekid` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('falanaja/helmgojekid', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
khalednabawi11/fine_tuned_gpt-2 | khalednabawi11 | 2024-10-19T16:49:22Z | 187 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T16:48:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
g-assismoraes/llama-music_fold_9 | g-assismoraes | 2024-10-19T16:48:28Z | 6 | 0 | null | [
"safetensors",
"llama",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2024-10-19T16:31:30Z | ---
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- generated_from_trainer
model-index:
- name: llama-music_fold_9
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-music_fold_9
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4073
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0736 | 1.0 | 184 | 2.4509 |
| 1.9814 | 2.0 | 368 | 2.4946 |
| 2.3385 | 3.0 | 552 | 2.4073 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1
|
Ram20307/bart-medtranscription | Ram20307 | 2024-10-19T16:48:20Z | 359 | 1 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"medical",
"summarization",
"en",
"dataset:ccdv/pubmed-summarization",
"base_model:facebook/bart-large-cnn",
"base_model:finetune:facebook/bart-large-cnn",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2024-10-19T16:35:05Z | ---
license: apache-2.0
datasets:
- ccdv/pubmed-summarization
language:
- en
metrics:
- accuracy
base_model:
- facebook/bart-large-cnn
new_version: facebook/bart-large-cnn
pipeline_tag: summarization
library_name: transformers
tags:
- medical
--- |
jdavit/roberta-large-QA | jdavit | 2024-10-19T16:44:43Z | 127 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"question-answering",
"general-porpuse",
"es",
"dataset:PlanTL-GOB-ES/SQAC",
"base_model:PlanTL-GOB-ES/roberta-large-bne-sqac",
"base_model:finetune:PlanTL-GOB-ES/roberta-large-bne-sqac",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-10-19T02:12:09Z | ---
license: apache-2.0
datasets:
- PlanTL-GOB-ES/SQAC
language:
- es
base_model:
- PlanTL-GOB-ES/roberta-large-bne-sqac
pipeline_tag: question-answering
library_name: transformers
tags:
- general-porpuse
--- |
Akg-247/speecht5_finetuned_emirhan_tr | Akg-247 | 2024-10-19T16:43:11Z | 76 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2024-10-19T16:13:02Z | ---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_emirhan_tr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_emirhan_tr
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5234
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9197 | 0.3366 | 100 | 0.9228 |
| 0.6853 | 0.6731 | 200 | 0.6622 |
| 0.6089 | 1.0097 | 300 | 0.5585 |
| 0.5779 | 1.3462 | 400 | 0.5332 |
| 0.5638 | 1.6828 | 500 | 0.5234 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
Nicktheblade/amateurphoto | Nicktheblade | 2024-10-19T16:42:47Z | 9 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unknown",
"region:us"
] | text-to-image | 2024-10-19T16:42:27Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
Hack Forums scrapped posted to WhatsApp r/me_irl Shot on iPhone photo of
five friends posing together in a lively kitchen during a costume party. On
the far left is a man with a medium build, light skin, and short brown hair,
wearing glasses, a brown henley shirt, and a lanyard with a badge around his
neck. He holds a can of beer in his right hand and smiles widely, his
expression cheerful. Next to him is a man with a tall, slim build, fair
skin, and short dark hair, dressed in a black Raiders jacket and sunglasses.
He has a serious expression with his lips pursed, standing confidently with
his hands in his pockets. In the center, slightly behind the group, is
another man with a medium build, light skin, and short blonde hair, wearing
a black suit, white shirt, black tie, and black sunglasses. He smiles
warmly, holding a bottle of beer in his right hand. To his right, a woman
with a medium build, light skin, and shoulder-length dark brown hair stands
close, wearing a black t-shirt with a tattoo design on her right arm. She
smiles at the camera, holding a beer bottle in her left hand. In the front,
a woman with fair skin, red hair, and a painted face resembling a zombie
nurse is crouched down, smiling brightly with her makeup giving her a
ghoulish appearance <lora:amateurphoto-version4-final-9555:0.4>
output:
url: images/00084-4042615827.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Hack Forums scrapped posted to WhatsApp r/me_irl Shot on iPhone photo of
license: unknown
---
# AmateurPhoto Flux Lora
<Gallery />
## Model description
Amateur photo gen
## Trigger words
You should use `Hack Forums scrapped posted to WhatsApp r/me_irl Shot on iPhone photo of` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Nicktheblade/amateurphoto/tree/main) them in the Files & versions tab.
|
glif-loradex-trainer/kklors_flux_dev_data_moshingV2 | glif-loradex-trainer | 2024-10-19T16:42:12Z | 10 | 1 | diffusers | [
"diffusers",
"text-to-image",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us",
"flux",
"lora",
"base_model:adapter:black-forest-labs/FLUX.1-dev"
] | text-to-image | 2024-10-19T16:41:45Z | ---
tags:
- diffusers
- text-to-image
- template:sd-lora
- base_model:black-forest-labs/FLUX.1-dev
- base_model:finetune:black-forest-labs/FLUX.1-dev
- license:other
- region:us
- flux
- lora
widget:
- output:
url: samples/1729355972746__000003000_0.jpg
text: close up of a shoe MOSH
- output:
url: samples/1729355995622__000003000_1.jpg
text: red mercedes in a city MOSH
- output:
url: samples/1729356018493__000003000_2.jpg
text: a busy city road MOSH
- output:
url: samples/1729356041365__000003000_3.jpg
text: playing poker at a table MOSH
- output:
url: samples/1729356064243__000003000_4.jpg
text: egypt pyraminds MOSH
- output:
url: samples/1729356087119__000003000_5.jpg
text: kids playing in a backyard MOSH
base_model: black-forest-labs/FLUX.1-dev
trigger: MOSH
instance_prompt: MOSH
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# flux_dev_data_moshingV2
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `kklors`.
<Gallery />
## Trigger words
You should use `MOSH` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/glif-loradex-trainer/kklors_flux_dev_data_moshingV2/tree/main) them in the Files & versions tab.
## License
This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
mekjr1/ai-detect-6 | mekjr1 | 2024-10-19T16:41:19Z | 5 | 0 | null | [
"safetensors",
"roberta",
"generated_from_trainer",
"base_model:Hello-SimpleAI/chatgpt-detector-roberta",
"base_model:finetune:Hello-SimpleAI/chatgpt-detector-roberta",
"region:us"
] | null | 2024-10-19T14:49:40Z | ---
base_model: Hello-SimpleAI/chatgpt-detector-roberta
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: ai-detect-6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ai-detect-6
This model is a fine-tuned version of [Hello-SimpleAI/chatgpt-detector-roberta](https://huggingface.co/Hello-SimpleAI/chatgpt-detector-roberta) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4830
- Accuracy: 0.9396
- Precision: 0.9223
- Recall: 0.9866
- F1: 0.9534
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.216 | 1.0 | 6250 | 0.4401 | 0.8916 | 0.8572 | 0.9920 | 0.9197 |
| 0.1277 | 2.0 | 12500 | 0.2963 | 0.928 | 0.9093 | 0.9831 | 0.9447 |
| 0.0858 | 3.0 | 18750 | 0.3475 | 0.9236 | 0.8993 | 0.9887 | 0.9418 |
| 0.0647 | 4.0 | 25000 | 0.2927 | 0.9404 | 0.9252 | 0.9843 | 0.9539 |
| 0.0798 | 5.0 | 31250 | 0.3095 | 0.9424 | 0.9282 | 0.9840 | 0.9553 |
| 0.0392 | 6.0 | 37500 | 0.4386 | 0.9401 | 0.9214 | 0.9887 | 0.9538 |
| 0.0433 | 7.0 | 43750 | 0.4830 | 0.9396 | 0.9223 | 0.9866 | 0.9534 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 3.0.1
- Tokenizers 0.15.2
|
g-assismoraes/llama-music_fold_8 | g-assismoraes | 2024-10-19T16:31:28Z | 7 | 0 | null | [
"safetensors",
"llama",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2024-10-19T16:15:04Z | ---
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- generated_from_trainer
model-index:
- name: llama-music_fold_8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-music_fold_8
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3512
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3488 | 1.0 | 184 | 1.3760 |
| 2.2389 | 2.0 | 368 | 1.3512 |
| 2.7486 | 3.0 | 552 | 1.3760 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1
|
richardchai/plp_action_clr_tinybert | richardchai | 2024-10-19T16:26:28Z | 5 | 0 | null | [
"safetensors",
"bert",
"en",
"license:mit",
"region:us"
] | null | 2024-10-19T16:25:51Z | ---
language: en
license: mit
---
# Model Card
Bank ACTION Classifier - tinyBERT
Developed by: Richard Chai, https://www.linkedin.com/in/richardchai/
This model has been fine-tuned for Bank User Action/Intent Identification.
Currently, it identifies the following actions:
['access',
'activate',
'apply',
'block',
'cancel',
'close',
'deposit',
'dispute',
'earn',
'exchange',
'find',
'inquire',
'link',
'open',
'pay',
'receive',
'redeem',
'refund',
'renew',
'report',
'reset',
'retrieve',
'schedule',
'select',
'transfer',
'unblock',
'unknown',
'unlink',
'update',
'verify',
'withdraw']
## Model Details
- **Model type**: Transformer-based (e.g., BERT, DistilBERT, etc.): tinyBERT
- **Dataset**: Stanford Sentiment Treebank SST-5 or another sentiment dataset
- **Fine-tuning**: The model was fine-tuned for X epochs using a learning rate of Y on a dataset with Z samples.
## Usage
You can use this model to classify text sentiment as follows:
```python
from transformers import pipeline
# Check if GPU is available
device = 0 if torch.cuda.is_available() else -1
model_checkpt = "richardchai/plp_action_clr_tinybert"
clf = pipeline('text-classification', model="model_trained/tinybert", device=device)
result = clf(f"['please tell me more about your fixed deposit.', 'I want to deposit money into my savings account.']")
print(result)
```
|
richardchai/plp_action_clr_distilbert | richardchai | 2024-10-19T16:18:57Z | 6 | 0 | null | [
"safetensors",
"distilbert",
"en",
"license:mit",
"region:us"
] | null | 2024-10-19T16:17:28Z | ---
language: en
license: mit
---
# Model Card
Bank ACTION Classifier - DistilBERT
Developed by: Richard Chai, https://www.linkedin.com/in/richardchai/
This model has been fine-tuned for Bank User Action/Intent Identification.
Currently, it identifies the following actions:
['access',
'activate',
'apply',
'block',
'cancel',
'close',
'deposit',
'dispute',
'earn',
'exchange',
'find',
'inquire',
'link',
'open',
'pay',
'receive',
'redeem',
'refund',
'renew',
'report',
'reset',
'retrieve',
'schedule',
'select',
'transfer',
'unblock',
'unknown',
'unlink',
'update',
'verify',
'withdraw']
## Model Details
- **Model type**: Transformer-based (e.g., BERT, DistilBERT, etc.): DistilBERT
- **Dataset**: Stanford Sentiment Treebank SST-5 or another sentiment dataset
- **Fine-tuning**: The model was fine-tuned for X epochs using a learning rate of Y on a dataset with Z samples.
## Usage
You can use this model to classify text sentiment as follows:
```python
from transformers import pipeline
# Check if GPU is available
device = 0 if torch.cuda.is_available() else -1
model_checkpt = "richardchai/plp_action_clr_distilbert"
clf = pipeline('text-classification', model="model_trained/distilbert", device=device)
result = clf(f"['please tell me more about your fixed deposit.', 'I want to deposit money into my savings account.']")
print(result)
```
|
g-assismoraes/llama-music_fold_7 | g-assismoraes | 2024-10-19T16:14:57Z | 5 | 0 | null | [
"safetensors",
"llama",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2024-10-19T15:58:56Z | ---
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- generated_from_trainer
model-index:
- name: llama-music_fold_7
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-music_fold_7
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2071
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9457 | 1.0 | 184 | 3.3194 |
| 2.0437 | 2.0 | 368 | 3.2071 |
| 2.4208 | 3.0 | 552 | 3.2071 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1
|
g-assismoraes/llama-music_fold_6 | g-assismoraes | 2024-10-19T15:58:54Z | 5 | 0 | null | [
"safetensors",
"llama",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2024-10-19T15:46:12Z | ---
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- generated_from_trainer
model-index:
- name: llama-music_fold_6
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-music_fold_6
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4509
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.9132 | 1.0 | 184 | 2.6692 |
| 2.0088 | 2.0 | 368 | 2.5382 |
| 2.5484 | 3.0 | 552 | 2.4509 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1
|
bbansal/model1 | bbansal | 2024-10-19T15:56:44Z | 171 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T15:55:33Z | ---
base_model: unsloth/Llama-3.2-3B-Instruct
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** bbansal
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
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)
|
KuanP/continual-pretrain-a100_large_epoch-lr2e-5-cw10.0-lg0.5.new_2024-10-19_fold_5 | KuanP | 2024-10-19T15:55:45Z | 34 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-19T15:55:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
g-assismoraes/llama-music_fold_5 | g-assismoraes | 2024-10-19T15:46:09Z | 5 | 0 | null | [
"safetensors",
"llama",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2024-10-19T15:30:30Z | ---
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- generated_from_trainer
model-index:
- name: llama-music_fold_5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-music_fold_5
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4509
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8655 | 1.0 | 184 | 2.7129 |
| 2.2086 | 2.0 | 368 | 2.4509 |
| 2.5658 | 3.0 | 552 | 2.4946 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1
|
KuanP/continual-pretrain-a100_large_epoch-lr2e-5-cw10.0-lg0.5.new_2024-10-19_fold_4 | KuanP | 2024-10-19T15:40:43Z | 34 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-19T15:40:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ycli68/ddpm-celebahq-finetuned-butterflies-2epochs | ycli68 | 2024-10-19T15:37:16Z | 44 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2024-10-19T15:36:51Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('ycli68/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
|
allknowingroger/Qwenslerp3-14B | allknowingroger | 2024-10-19T15:36:34Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:allknowingroger/Qwen2.5-slerp-14B",
"base_model:merge:allknowingroger/Qwen2.5-slerp-14B",
"base_model:rombodawg/Rombos-LLM-V2.6-Qwen-14b",
"base_model:merge:rombodawg/Rombos-LLM-V2.6-Qwen-14b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T15:26:06Z | ---
base_model:
- allknowingroger/Qwen2.5-slerp-14B
- rombodawg/Rombos-LLM-V2.6-Qwen-14b
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
# 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:
* [allknowingroger/Qwen2.5-slerp-14B](https://huggingface.co/allknowingroger/Qwen2.5-slerp-14B)
* [rombodawg/Rombos-LLM-V2.6-Qwen-14b](https://huggingface.co/rombodawg/Rombos-LLM-V2.6-Qwen-14b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: rombodawg/Rombos-LLM-V2.6-Qwen-14b
merge_method: slerp
base_model: allknowingroger/Qwen2.5-slerp-14B
parameters:
t:
- value: [0, 0, 0.3, 0.4, 0.5, 0.6, 0.5, 0.4, 0.3, 0, 0]
dtype: bfloat16
``` |
awels/maximusLLM-3b-128k-gguf | awels | 2024-10-19T15:32:04Z | 14 | 1 | adapters | [
"adapters",
"gguf",
"awels",
"maximo",
"en",
"dataset:awels/maximo_admin_dataset",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"base_model:quantized:microsoft/Phi-3-mini-128k-instruct",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-07-08T19:48:58Z | ---
license: mit
base_model: microsoft/Phi-3-mini-128k-instruct
library_name: adapters
datasets:
- awels/maximo_admin_dataset
language:
- en
widget:
- text: Who are you, Maximus ?
tags:
- awels
- maximo
---
# Maximus Model Card
## Model Details
**Model Name:** Maximus
**Model Type:** Transformer-based leveraging Microsoft Phi 3b 128k tokens
**Publisher:** Awels Engineering
**License:** MIT
**Model Description:**
Maximus is a sophisticated model designed to help as an AI agent focusing on Maximo Application Suite. It leverages advanced machine learning techniques to provide efficient and accurate solutions. It has been trained on the full docments corpus of MAS 8.5.
## Dataset
**Dataset Name:** [awels/maximo_admin_dataset](https://huggingface.co/datasets/awels/maximo_admin_dataset)
**Dataset Source:** Hugging Face Datasets
**Dataset License:** MIT
**Dataset Description:**
The dataset used to train Maximus consists of all the public documents available on Maximo application suite. This dataset is curated to ensure a comprehensive representation of typical administrative scenarios encountered in Maximo.
## Training Details
**Training Data:**
The training data includes 67,000 Questions and Answers generated by the [Bonito LLM](https://github.com/BatsResearch/bonito). The dataset is split into 3 sets of data (training, test and validation) to ensure robust model performance.
**Training Procedure:**
Maximus was trained using supervised learning with cross-entropy loss and the Adam optimizer. The training involved 1 epoch, a batch size of 4, a learning rate of 5.0e-06, and a cosine learning rate scheduler with gradient checkpointing for memory efficiency.
**Hardware:**
The model was trained on a single NVIDIA RTX 4090 graphic card.
**Framework:**
The training was conducted using PyTorch.
## Evaluation
**Evaluation Metrics:**
Maximus was evaluated on the training dataset:
> epoch = 1.0
total_flos = 64046138GF
train_loss = 2.8079
train_runtime = 0:37:48.33
train_samples_per_second = 21.066
train_steps_per_second = 5.267
**Performance:**
The model achieved the following results on the evaluation dataset:
> epoch = 1.0
eval_loss = 2.288
eval_runtime = 0:02:05.48
eval_samples = 10773
eval_samples_per_second = 95.338
eval_steps_per_second = 23.836
## Intended Use
**Primary Use Case:**
Maximus is intended to be used locally in an agent swarm to colleborate together to solve Maximo Application Suite related problems.
**Limitations:**
While Maximus is highly effective, it may have limitations due to the model size. An 8b model based on Llama 3 is used internally at Awels Engineering. |
KuanP/continual-pretrain-a100_large_epoch-lr2e-5-cw10.0-lg0.5.new_2024-10-19_fold_3 | KuanP | 2024-10-19T15:25:35Z | 34 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-19T15:25:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
g-assismoraes/llama-music_fold_3 | g-assismoraes | 2024-10-19T15:17:55Z | 5 | 0 | null | [
"safetensors",
"llama",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2024-10-19T15:03:24Z | ---
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- generated_from_trainer
model-index:
- name: llama-music_fold_3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-music_fold_3
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0187
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.836 | 1.0 | 183 | 1.2858 |
| 2.3334 | 2.0 | 366 | 1.0187 |
| 2.9706 | 3.0 | 549 | 1.0364 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1
|
myxlmynx/cyberrealistic_classic40 | myxlmynx | 2024-10-19T15:16:59Z | 198 | 2 | diffusers | [
"diffusers",
"safetensors",
"Base Model",
"Realism",
"Photorealistic",
"Person",
"Portrait",
"diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-10-13T22:17:04Z | ---
license: creativeml-openrail-m
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Base Model
- Realism
- Photorealistic
- Person
- Portrait
- diffusers
- diffusion
- stable-diffusion-diffusers
- text-to-image
inference:
parameters:
height: 768
width: 512
negative_prompt: nsfw
widget:
- text: young woman
output:
url: images/example_h83m0fz0p.png
- text: >-
A tenacious female journalist uncovering a high-stakes conspiracy in a
bustling metropolis, weaving through crowded streets and dark alleyways,
close up, Detailed clothes, piercing gaze, flowing hair, determined
expression, shiny glossy skin, subsurface scattering, (sharp:0.7), amazing
fine detail, Nikon D850 film stock photograph Kodak Portra 400 camera f1.6
lens, rich colors, lifelike texture, dramatic lighting, urban environment,
skyscrapers, neon signs, street vendors, dynamic composition, unreal engine,
trending on ArtStation, cinestill 800 tungsten
output:
url: images/example_zs3qc60ms.png
---
Converted from [https://civitai.com/models/71185/cyberrealistic-classic](https://civitai.com/models/71185/cyberrealistic-classic).
* [a beautiful sunset](?text=a+beautiful+sunset)
* [a majestic tree](?text=a+majestic+tree)
* [a pilot on an airplane cabin](?text=a+pilot+on+an+airplane+cabin)
* [a delicious meal](?text=a+delicious+meal)
* [intense city traffic](?text=intense+city+traffic)
|
KuanP/continual-pretrain-a100_large_epoch-lr2e-5-cw10.0-lg0.5.new_2024-10-19_fold_2 | KuanP | 2024-10-19T15:10:31Z | 33 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-19T15:10:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
1MK26/BART_hydroGENERATION | 1MK26 | 2024-10-19T15:10:20Z | 171 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-19T15:08:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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] |
youssefkhalil320/all-MiniLM-L12-v2-pairscore | youssefkhalil320 | 2024-10-19T15:08:59Z | 11 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:510287",
"loss:CoSENTLoss",
"en",
"arxiv:1908.10084",
"base_model:sentence-transformers/all-MiniLM-L12-v2",
"base_model:finetune:sentence-transformers/all-MiniLM-L12-v2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-10-19T15:07:03Z | ---
base_model: sentence-transformers/all-MiniLM-L12-v2
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:510287
- loss:CoSENTLoss
widget:
- source_sentence: bag
sentences:
- bag
- summer colors bag
- carry all bag
- source_sentence: bean bag
sentences:
- bag
- havan bag
- black yellow shoes
- source_sentence: pyramid shaped cushion mattress
sentences:
- dress
- silver bag
- women shoes
- source_sentence: handcrafted rug
sentences:
- amaga cross bag - white
- handcrafted boots
- polyester top
- source_sentence: bean bag
sentences:
- bag
- v-neck dress
- bag
model-index:
- name: all-MiniLM-L12-v2-pair_score
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts dev
type: sts-dev
metrics:
- type: pearson_cosine
value: -0.10403022864037037
name: Pearson Cosine
- type: spearman_cosine
value: -0.1437799564130218
name: Spearman Cosine
- type: pearson_manhattan
value: -0.10847915569723102
name: Pearson Manhattan
- type: spearman_manhattan
value: -0.14274368509273366
name: Spearman Manhattan
- type: pearson_euclidean
value: -0.11064121359722408
name: Pearson Euclidean
- type: spearman_euclidean
value: -0.14377964610318103
name: Spearman Euclidean
- type: pearson_dot
value: -0.10403015819885228
name: Pearson Dot
- type: spearman_dot
value: -0.14377961300118045
name: Spearman Dot
- type: pearson_max
value: -0.10403015819885228
name: Pearson Max
- type: spearman_max
value: -0.14274368509273366
name: Spearman Max
---
# all-MiniLM-L12-v2-pair_score
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) <!-- at revision 30ce63ae64e71b9199b3d2eae9de99f64a26eedc -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'bean bag',
'bag',
'v-neck dress',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-dev`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:------------|
| pearson_cosine | -0.104 |
| **spearman_cosine** | **-0.1438** |
| pearson_manhattan | -0.1085 |
| spearman_manhattan | -0.1427 |
| pearson_euclidean | -0.1106 |
| spearman_euclidean | -0.1438 |
| pearson_dot | -0.104 |
| spearman_dot | -0.1438 |
| pearson_max | -0.104 |
| spearman_max | -0.1427 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `warmup_ratio`: 0.1
- `fp16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | loss | sts-dev_spearman_cosine |
|:------:|:-----:|:-------------:|:------:|:-----------------------:|
| 0 | 0 | - | - | -0.1438 |
| 0.0063 | 100 | 11.9171 | - | - |
| 0.0125 | 200 | 11.0074 | - | - |
| 0.0188 | 300 | 10.1073 | - | - |
| 0.0251 | 400 | 8.6232 | - | - |
| 0.0314 | 500 | 7.5947 | 7.2720 | - |
| 0.0376 | 600 | 6.3883 | - | - |
| 0.0439 | 700 | 5.6165 | - | - |
| 0.0502 | 800 | 4.8254 | - | - |
| 0.0564 | 900 | 4.5595 | - | - |
| 0.0627 | 1000 | 4.2965 | 4.1720 | - |
| 0.0690 | 1100 | 4.063 | - | - |
| 0.0752 | 1200 | 4.0861 | - | - |
| 0.0815 | 1300 | 3.9703 | - | - |
| 0.0878 | 1400 | 3.8222 | - | - |
| 0.0941 | 1500 | 3.927 | 3.6404 | - |
| 0.1003 | 1600 | 3.6892 | - | - |
| 0.1066 | 1700 | 3.9166 | - | - |
| 0.1129 | 1800 | 3.7162 | - | - |
| 0.1191 | 1900 | 3.4866 | - | - |
| 0.1254 | 2000 | 3.5202 | 3.4226 | - |
| 0.1317 | 2100 | 3.6876 | - | - |
| 0.1380 | 2200 | 3.4884 | - | - |
| 0.1442 | 2300 | 3.4407 | - | - |
| 0.1505 | 2400 | 3.2658 | - | - |
| 0.1568 | 2500 | 3.2973 | 3.0777 | - |
| 0.1630 | 2600 | 3.2087 | - | - |
| 0.1693 | 2700 | 3.4316 | - | - |
| 0.1756 | 2800 | 3.3372 | - | - |
| 0.1819 | 2900 | 3.161 | - | - |
| 0.1881 | 3000 | 3.0232 | 2.8805 | - |
| 0.1944 | 3100 | 3.2897 | - | - |
| 0.2007 | 3200 | 3.2576 | - | - |
| 0.2069 | 3300 | 2.7636 | - | - |
| 0.2132 | 3400 | 3.1788 | - | - |
| 0.2195 | 3500 | 2.6269 | 2.6237 | - |
| 0.2257 | 3600 | 2.9352 | - | - |
| 0.2320 | 3700 | 2.847 | - | - |
| 0.2383 | 3800 | 2.8001 | - | - |
| 0.2446 | 3900 | 2.6048 | - | - |
| 0.2508 | 4000 | 2.5976 | 2.5250 | - |
| 0.2571 | 4100 | 2.5211 | - | - |
| 0.2634 | 4200 | 2.7812 | - | - |
| 0.2696 | 4300 | 2.6822 | - | - |
| 0.2759 | 4400 | 2.4779 | - | - |
| 0.2822 | 4500 | 2.6242 | 2.6365 | - |
| 0.2885 | 4600 | 2.5655 | - | - |
| 0.2947 | 4700 | 2.9998 | - | - |
| 0.3010 | 4800 | 2.679 | - | - |
| 0.3073 | 4900 | 2.5719 | - | - |
| 0.3135 | 5000 | 2.6913 | 2.6934 | - |
| 0.3198 | 5100 | 2.8346 | - | - |
| 0.3261 | 5200 | 2.7453 | - | - |
| 0.3324 | 5300 | 2.4492 | - | - |
| 0.3386 | 5400 | 2.9389 | - | - |
| 0.3449 | 5500 | 2.6002 | 2.6182 | - |
| 0.3512 | 5600 | 2.2592 | - | - |
| 0.3574 | 5700 | 2.3822 | - | - |
| 0.3637 | 5800 | 2.4771 | - | - |
| 0.3700 | 5900 | 3.5914 | - | - |
| 0.3762 | 6000 | 2.3525 | 2.5605 | - |
| 0.3825 | 6100 | 2.2667 | - | - |
| 0.3888 | 6200 | 2.4671 | - | - |
| 0.3951 | 6300 | 2.6816 | - | - |
| 0.4013 | 6400 | 2.2303 | - | - |
| 0.4076 | 6500 | 2.3153 | 2.4245 | - |
| 0.4139 | 6600 | 2.7969 | - | - |
| 0.4201 | 6700 | 2.61 | - | - |
| 0.4264 | 6800 | 2.5267 | - | - |
| 0.4327 | 6900 | 2.532 | - | - |
| 0.4390 | 7000 | 2.6088 | 2.4666 | - |
| 0.4452 | 7100 | 1.848 | - | - |
| 0.4515 | 7200 | 2.1369 | - | - |
| 0.4578 | 7300 | 2.185 | - | - |
| 0.4640 | 7400 | 2.0279 | - | - |
| 0.4703 | 7500 | 2.5593 | 2.3958 | - |
| 0.4766 | 7600 | 2.339 | - | - |
| 0.4828 | 7700 | 2.2122 | - | - |
| 0.4891 | 7800 | 2.7878 | - | - |
| 0.4954 | 7900 | 2.3005 | - | - |
| 0.5017 | 8000 | 2.2922 | 2.5408 | - |
| 0.5079 | 8100 | 2.3731 | - | - |
| 0.5142 | 8200 | 2.1879 | - | - |
| 0.5205 | 8300 | 2.1598 | - | - |
| 0.5267 | 8400 | 2.2292 | - | - |
| 0.5330 | 8500 | 1.958 | 2.0935 | - |
| 0.5393 | 8600 | 2.1152 | - | - |
| 0.5456 | 8700 | 1.9725 | - | - |
| 0.5518 | 8800 | 2.1106 | - | - |
| 0.5581 | 8900 | 2.06 | - | - |
| 0.5644 | 9000 | 1.7624 | 2.1509 | - |
| 0.5706 | 9100 | 2.3793 | - | - |
| 0.5769 | 9200 | 1.9322 | - | - |
| 0.5832 | 9300 | 1.8355 | - | - |
| 0.5895 | 9400 | 2.1425 | - | - |
| 0.5957 | 9500 | 2.2191 | 1.9984 | - |
| 0.6020 | 9600 | 2.3245 | - | - |
| 0.6083 | 9700 | 2.1206 | - | - |
| 0.6145 | 9800 | 2.0957 | - | - |
| 0.6208 | 9900 | 2.5276 | - | - |
| 0.6271 | 10000 | 1.5383 | 1.9509 | - |
| 0.6333 | 10100 | 2.111 | - | - |
| 0.6396 | 10200 | 1.893 | - | - |
| 0.6459 | 10300 | 1.8961 | - | - |
| 0.6522 | 10400 | 1.6599 | - | - |
| 0.6584 | 10500 | 2.3409 | 1.8286 | - |
| 0.6647 | 10600 | 1.9741 | - | - |
| 0.6710 | 10700 | 2.0438 | - | - |
| 0.6772 | 10800 | 1.814 | - | - |
| 0.6835 | 10900 | 2.1819 | - | - |
| 0.6898 | 11000 | 1.8547 | 1.9461 | - |
| 0.6961 | 11100 | 2.5979 | - | - |
| 0.7023 | 11200 | 1.9309 | - | - |
| 0.7086 | 11300 | 1.6247 | - | - |
| 0.7149 | 11400 | 2.1107 | - | - |
| 0.7211 | 11500 | 2.1264 | 1.8004 | - |
| 0.7274 | 11600 | 1.7397 | - | - |
| 0.7337 | 11700 | 1.9569 | - | - |
| 0.7400 | 11800 | 1.4769 | - | - |
| 0.7462 | 11900 | 1.6222 | - | - |
| 0.7525 | 12000 | 1.5354 | 1.6811 | - |
| 0.7588 | 12100 | 2.2645 | - | - |
| 0.7650 | 12200 | 1.8662 | - | - |
| 0.7713 | 12300 | 1.5327 | - | - |
| 0.7776 | 12400 | 1.9501 | - | - |
| 0.7838 | 12500 | 2.0923 | 1.6134 | - |
| 0.7901 | 12600 | 1.8887 | - | - |
| 0.7964 | 12700 | 1.7207 | - | - |
| 0.8027 | 12800 | 1.8589 | - | - |
| 0.8089 | 12900 | 1.7602 | - | - |
| 0.8152 | 13000 | 2.2405 | 1.5030 | - |
| 0.8215 | 13100 | 1.6249 | - | - |
| 0.8277 | 13200 | 1.6814 | - | - |
| 0.8340 | 13300 | 1.4072 | - | - |
| 0.8403 | 13400 | 1.6286 | - | - |
| 0.8466 | 13500 | 2.2081 | 1.6078 | - |
| 0.8528 | 13600 | 1.7387 | - | - |
| 0.8591 | 13700 | 1.5268 | - | - |
| 0.8654 | 13800 | 1.5693 | - | - |
| 0.8716 | 13900 | 1.2473 | - | - |
| 0.8779 | 14000 | 1.361 | 1.7168 | - |
| 0.8842 | 14100 | 1.5246 | - | - |
| 0.8904 | 14200 | 1.7266 | - | - |
| 0.8967 | 14300 | 0.9221 | - | - |
| 0.9030 | 14400 | 1.6397 | - | - |
| 0.9093 | 14500 | 1.3139 | 1.5492 | - |
| 0.9155 | 14600 | 1.7942 | - | - |
| 0.9218 | 14700 | 1.5206 | - | - |
| 0.9281 | 14800 | 1.5868 | - | - |
| 0.9343 | 14900 | 1.2131 | - | - |
| 0.9406 | 15000 | 1.8765 | 1.4192 | - |
| 0.9469 | 15100 | 1.624 | - | - |
| 0.9532 | 15200 | 1.4692 | - | - |
| 0.9594 | 15300 | 1.5426 | - | - |
| 0.9657 | 15400 | 1.3668 | - | - |
| 0.9720 | 15500 | 1.3951 | 1.6835 | - |
| 0.9782 | 15600 | 1.1567 | - | - |
| 0.9845 | 15700 | 1.8634 | - | - |
| 0.9908 | 15800 | 1.641 | - | - |
| 0.9971 | 15900 | 1.6458 | - | - |
| 1.0033 | 16000 | 1.1369 | 1.5746 | - |
| 1.0096 | 16100 | 1.1913 | - | - |
| 1.0159 | 16200 | 1.5563 | - | - |
| 1.0221 | 16300 | 1.4081 | - | - |
| 1.0284 | 16400 | 1.8157 | - | - |
| 1.0347 | 16500 | 1.6405 | 1.5235 | - |
| 1.0409 | 16600 | 0.9207 | - | - |
| 1.0472 | 16700 | 1.4301 | - | - |
| 1.0535 | 16800 | 1.4566 | - | - |
| 1.0598 | 16900 | 1.5397 | - | - |
| 1.0660 | 17000 | 1.3417 | 1.3883 | - |
| 1.0723 | 17100 | 0.9769 | - | - |
| 1.0786 | 17200 | 1.3734 | - | - |
| 1.0848 | 17300 | 1.0874 | - | - |
| 1.0911 | 17400 | 1.2601 | - | - |
| 1.0974 | 17500 | 1.4799 | 1.4361 | - |
| 1.1037 | 17600 | 1.1086 | - | - |
| 1.1099 | 17700 | 1.3731 | - | - |
| 1.1162 | 17800 | 1.0515 | - | - |
| 1.1225 | 17900 | 1.7916 | - | - |
| 1.1287 | 18000 | 1.7606 | 1.3792 | - |
| 1.1350 | 18100 | 1.3844 | - | - |
| 1.1413 | 18200 | 1.3567 | - | - |
| 1.1476 | 18300 | 1.4322 | - | - |
| 1.1538 | 18400 | 1.9509 | - | - |
| 1.1601 | 18500 | 1.0303 | 1.3425 | - |
| 1.1664 | 18600 | 1.6484 | - | - |
| 1.1726 | 18700 | 1.1177 | - | - |
| 1.1789 | 18800 | 1.0295 | - | - |
| 1.1852 | 18900 | 1.4364 | - | - |
| 1.1914 | 19000 | 1.1954 | 1.3385 | - |
| 1.1977 | 19100 | 1.1944 | - | - |
| 1.2040 | 19200 | 0.9109 | - | - |
| 1.2103 | 19300 | 1.4191 | - | - |
| 1.2165 | 19400 | 1.5755 | - | - |
| 1.2228 | 19500 | 1.0958 | 1.2872 | - |
| 1.2291 | 19600 | 0.9054 | - | - |
| 1.2353 | 19700 | 1.0892 | - | - |
| 1.2416 | 19800 | 1.4455 | - | - |
| 1.2479 | 19900 | 1.3273 | - | - |
| 1.2542 | 20000 | 1.6442 | 1.2880 | - |
| 1.2604 | 20100 | 1.1901 | - | - |
| 1.2667 | 20200 | 0.9871 | - | - |
| 1.2730 | 20300 | 1.6448 | - | - |
| 1.2792 | 20400 | 1.1899 | - | - |
| 1.2855 | 20500 | 1.3454 | 1.3303 | - |
| 1.2918 | 20600 | 1.4376 | - | - |
| 1.2980 | 20700 | 1.0356 | - | - |
| 1.3043 | 20800 | 1.7588 | - | - |
| 1.3106 | 20900 | 1.0993 | - | - |
| 1.3169 | 21000 | 1.3673 | 1.2607 | - |
| 1.3231 | 21100 | 1.3326 | - | - |
| 1.3294 | 21200 | 1.3618 | - | - |
| 1.3357 | 21300 | 1.3123 | - | - |
| 1.3419 | 21400 | 0.9771 | - | - |
| 1.3482 | 21500 | 1.1626 | 1.2873 | - |
| 1.3545 | 21600 | 1.41 | - | - |
| 1.3608 | 21700 | 1.6998 | - | - |
| 1.3670 | 21800 | 0.8335 | - | - |
| 1.3733 | 21900 | 1.579 | - | - |
| 1.3796 | 22000 | 1.6073 | 1.2164 | - |
| 1.3858 | 22100 | 1.0534 | - | - |
| 1.3921 | 22200 | 1.0045 | - | - |
| 1.3984 | 22300 | 1.4195 | - | - |
| 1.4047 | 22400 | 1.4409 | - | - |
| 1.4109 | 22500 | 1.3942 | 1.2018 | - |
| 1.4172 | 22600 | 1.6013 | - | - |
| 1.4235 | 22700 | 1.139 | - | - |
| 1.4297 | 22800 | 0.7062 | - | - |
| 1.4360 | 22900 | 1.1948 | - | - |
| 1.4423 | 23000 | 1.6784 | 1.1736 | - |
| 1.4485 | 23100 | 1.1618 | - | - |
| 1.4548 | 23200 | 0.827 | - | - |
| 1.4611 | 23300 | 1.0041 | - | - |
| 1.4674 | 23400 | 0.7447 | - | - |
| 1.4736 | 23500 | 1.1531 | 1.0797 | - |
| 1.4799 | 23600 | 1.0904 | - | - |
| 1.4862 | 23700 | 1.0648 | - | - |
| 1.4924 | 23800 | 1.1863 | - | - |
| 1.4987 | 23900 | 0.893 | - | - |
| 1.5050 | 24000 | 1.2528 | 1.0737 | - |
| 1.5113 | 24100 | 0.9333 | - | - |
| 1.5175 | 24200 | 1.3404 | - | - |
| 1.5238 | 24300 | 0.8959 | - | - |
| 1.5301 | 24400 | 0.6898 | - | - |
| 1.5363 | 24500 | 0.9896 | 1.1813 | - |
| 1.5426 | 24600 | 0.7928 | - | - |
| 1.5489 | 24700 | 1.4153 | - | - |
| 1.5552 | 24800 | 1.2393 | - | - |
| 1.5614 | 24900 | 0.744 | - | - |
| 1.5677 | 25000 | 0.7545 | 1.0823 | - |
| 1.5740 | 25100 | 1.1936 | - | - |
| 1.5802 | 25200 | 0.8755 | - | - |
| 1.5865 | 25300 | 1.063 | - | - |
| 1.5928 | 25400 | 0.8634 | - | - |
| 1.5990 | 25500 | 1.2905 | 1.0718 | - |
| 1.6053 | 25600 | 1.0906 | - | - |
| 1.6116 | 25700 | 1.1594 | - | - |
| 1.6179 | 25800 | 1.108 | - | - |
| 1.6241 | 25900 | 1.2538 | - | - |
| 1.6304 | 26000 | 1.3377 | 1.1370 | - |
| 1.6367 | 26100 | 0.8156 | - | - |
| 1.6429 | 26200 | 0.9753 | - | - |
| 1.6492 | 26300 | 1.0909 | - | - |
| 1.6555 | 26400 | 1.0029 | - | - |
| 1.6618 | 26500 | 0.6841 | 1.0385 | - |
| 1.6680 | 26600 | 1.1673 | - | - |
| 1.6743 | 26700 | 1.3606 | - | - |
| 1.6806 | 26800 | 0.4306 | - | - |
| 1.6868 | 26900 | 1.0989 | - | - |
| 1.6931 | 27000 | 1.3283 | 1.0136 | - |
| 1.6994 | 27100 | 1.0206 | - | - |
| 1.7056 | 27200 | 0.6866 | - | - |
| 1.7119 | 27300 | 0.9168 | - | - |
| 1.7182 | 27400 | 0.9472 | - | - |
| 1.7245 | 27500 | 0.7866 | 1.0890 | - |
| 1.7307 | 27600 | 1.481 | - | - |
| 1.7370 | 27700 | 1.0311 | - | - |
| 1.7433 | 27800 | 1.3346 | - | - |
| 1.7495 | 27900 | 0.8331 | - | - |
| 1.7558 | 28000 | 1.3056 | 0.9919 | - |
| 1.7621 | 28100 | 0.9692 | - | - |
| 1.7684 | 28200 | 0.9337 | - | - |
| 1.7746 | 28300 | 1.1588 | - | - |
| 1.7809 | 28400 | 1.0859 | - | - |
| 1.7872 | 28500 | 0.9939 | 1.0109 | - |
| 1.7934 | 28600 | 1.4019 | - | - |
| 1.7997 | 28700 | 0.9404 | - | - |
| 1.8060 | 28800 | 0.7085 | - | - |
| 1.8123 | 28900 | 1.1423 | - | - |
| 1.8185 | 29000 | 0.8389 | 0.9510 | - |
| 1.8248 | 29100 | 1.3947 | - | - |
| 1.8311 | 29200 | 0.8909 | - | - |
| 1.8373 | 29300 | 1.3824 | - | - |
| 1.8436 | 29400 | 0.6364 | - | - |
| 1.8499 | 29500 | 1.2197 | 0.9501 | - |
| 1.8561 | 29600 | 0.6353 | - | - |
| 1.8624 | 29700 | 1.3453 | - | - |
| 1.8687 | 29800 | 1.1069 | - | - |
| 1.8750 | 29900 | 0.9873 | - | - |
| 1.8812 | 30000 | 0.9291 | 1.0391 | - |
| 1.8875 | 30100 | 1.3971 | - | - |
| 1.8938 | 30200 | 1.0569 | - | - |
| 1.9000 | 30300 | 0.6731 | - | - |
| 1.9063 | 30400 | 1.0216 | - | - |
| 1.9126 | 30500 | 1.295 | 0.9819 | - |
| 1.9189 | 30600 | 1.1641 | - | - |
| 1.9251 | 30700 | 0.9199 | - | - |
| 1.9314 | 30800 | 0.9774 | - | - |
| 1.9377 | 30900 | 0.8242 | - | - |
| 1.9439 | 31000 | 1.4039 | 0.9666 | - |
| 1.9502 | 31100 | 0.7112 | - | - |
| 1.9565 | 31200 | 0.846 | - | - |
| 1.9628 | 31300 | 1.0952 | - | - |
| 1.9690 | 31400 | 1.0372 | - | - |
| 1.9753 | 31500 | 0.9585 | 0.8983 | - |
| 1.9816 | 31600 | 1.1527 | - | - |
| 1.9878 | 31700 | 0.7675 | - | - |
| 1.9941 | 31800 | 0.8359 | - | - |
| 2.0004 | 31900 | 1.1224 | - | - |
| 2.0066 | 32000 | 1.3421 | 0.9575 | - |
| 2.0129 | 32100 | 0.9171 | - | - |
| 2.0192 | 32200 | 0.5865 | - | - |
| 2.0255 | 32300 | 0.9239 | - | - |
| 2.0317 | 32400 | 0.7426 | - | - |
| 2.0380 | 32500 | 0.8965 | 0.9158 | - |
| 2.0443 | 32600 | 0.6605 | - | - |
| 2.0505 | 32700 | 0.8507 | - | - |
| 2.0568 | 32800 | 0.7288 | - | - |
| 2.0631 | 32900 | 0.6888 | - | - |
| 2.0694 | 33000 | 0.8745 | 0.9568 | - |
| 2.0756 | 33100 | 0.7972 | - | - |
| 2.0819 | 33200 | 0.6211 | - | - |
| 2.0882 | 33300 | 1.0126 | - | - |
| 2.0944 | 33400 | 0.8268 | - | - |
| 2.1007 | 33500 | 0.9723 | 0.8551 | - |
| 2.1070 | 33600 | 0.6366 | - | - |
| 2.1133 | 33700 | 0.6773 | - | - |
| 2.1195 | 33800 | 0.7676 | - | - |
| 2.1258 | 33900 | 0.9192 | - | - |
| 2.1321 | 34000 | 0.7054 | 0.8941 | - |
| 2.1383 | 34100 | 0.7349 | - | - |
| 2.1446 | 34200 | 0.6288 | - | - |
| 2.1509 | 34300 | 0.799 | - | - |
| 2.1571 | 34400 | 0.7492 | - | - |
| 2.1634 | 34500 | 1.0967 | 0.8746 | - |
| 2.1697 | 34600 | 0.7628 | - | - |
| 2.1760 | 34700 | 0.7697 | - | - |
| 2.1822 | 34800 | 0.7458 | - | - |
| 2.1885 | 34900 | 0.7868 | - | - |
| 2.1948 | 35000 | 0.9526 | 0.8620 | - |
| 2.2010 | 35100 | 0.6087 | - | - |
| 2.2073 | 35200 | 0.8602 | - | - |
| 2.2136 | 35300 | 0.8906 | - | - |
| 2.2199 | 35400 | 0.6012 | - | - |
| 2.2261 | 35500 | 0.9625 | 0.9094 | - |
| 2.2324 | 35600 | 0.8622 | - | - |
| 2.2387 | 35700 | 0.9015 | - | - |
| 2.2449 | 35800 | 1.0395 | - | - |
| 2.2512 | 35900 | 0.5582 | - | - |
| 2.2575 | 36000 | 0.7266 | 0.8666 | - |
| 2.2637 | 36100 | 0.6806 | - | - |
| 2.2700 | 36200 | 0.9246 | - | - |
| 2.2763 | 36300 | 0.7452 | - | - |
| 2.2826 | 36400 | 0.7886 | - | - |
| 2.2888 | 36500 | 0.9288 | 0.8529 | - |
| 2.2951 | 36600 | 1.2166 | - | - |
| 2.3014 | 36700 | 0.9566 | - | - |
| 2.3076 | 36800 | 0.7842 | - | - |
| 2.3139 | 36900 | 0.6815 | - | - |
| 2.3202 | 37000 | 0.78 | 0.8212 | - |
| 2.3265 | 37100 | 0.8306 | - | - |
| 2.3327 | 37200 | 0.8073 | - | - |
| 2.3390 | 37300 | 0.7565 | - | - |
| 2.3453 | 37400 | 0.8478 | - | - |
| 2.3515 | 37500 | 1.0159 | 0.8735 | - |
| 2.3578 | 37600 | 0.8126 | - | - |
| 2.3641 | 37700 | 0.751 | - | - |
| 2.3704 | 37800 | 0.7185 | - | - |
| 2.3766 | 37900 | 0.7429 | - | - |
| 2.3829 | 38000 | 0.7149 | 0.7997 | - |
| 2.3892 | 38100 | 0.6867 | - | - |
| 2.3954 | 38200 | 0.608 | - | - |
| 2.4017 | 38300 | 0.5687 | - | - |
| 2.4080 | 38400 | 0.6623 | - | - |
| 2.4142 | 38500 | 0.7751 | 0.7834 | - |
| 2.4205 | 38600 | 0.6537 | - | - |
| 2.4268 | 38700 | 0.7121 | - | - |
| 2.4331 | 38800 | 0.7864 | - | - |
| 2.4393 | 38900 | 0.296 | - | - |
| 2.4456 | 39000 | 0.4544 | 0.8051 | - |
| 2.4519 | 39100 | 0.4543 | - | - |
| 2.4581 | 39200 | 0.9965 | - | - |
| 2.4644 | 39300 | 0.4595 | - | - |
| 2.4707 | 39400 | 0.7557 | - | - |
| 2.4770 | 39500 | 0.6006 | 0.8437 | - |
| 2.4832 | 39600 | 0.695 | - | - |
| 2.4895 | 39700 | 0.6292 | - | - |
| 2.4958 | 39800 | 0.7392 | - | - |
| 2.5020 | 39900 | 0.6547 | - | - |
| 2.5083 | 40000 | 0.739 | 0.8443 | - |
| 2.5146 | 40100 | 0.5618 | - | - |
| 2.5209 | 40200 | 0.861 | - | - |
| 2.5271 | 40300 | 0.7318 | - | - |
| 2.5334 | 40400 | 0.9021 | - | - |
| 2.5397 | 40500 | 0.7329 | 0.8595 | - |
| 2.5459 | 40600 | 0.9691 | - | - |
| 2.5522 | 40700 | 1.0524 | - | - |
| 2.5585 | 40800 | 0.4546 | - | - |
| 2.5647 | 40900 | 0.8917 | - | - |
| 2.5710 | 41000 | 0.6644 | 0.8664 | - |
| 2.5773 | 41100 | 0.5167 | - | - |
| 2.5836 | 41200 | 0.6499 | - | - |
| 2.5898 | 41300 | 0.8096 | - | - |
| 2.5961 | 41400 | 0.7269 | - | - |
| 2.6024 | 41500 | 0.8561 | 0.8173 | - |
| 2.6086 | 41600 | 0.761 | - | - |
| 2.6149 | 41700 | 1.0167 | - | - |
| 2.6212 | 41800 | 0.763 | - | - |
| 2.6275 | 41900 | 0.6659 | - | - |
| 2.6337 | 42000 | 0.7299 | 0.8343 | - |
| 2.6400 | 42100 | 0.7045 | - | - |
| 2.6463 | 42200 | 0.9054 | - | - |
| 2.6525 | 42300 | 0.3002 | - | - |
| 2.6588 | 42400 | 0.7728 | - | - |
| 2.6651 | 42500 | 0.8214 | 0.8112 | - |
| 2.6713 | 42600 | 0.6762 | - | - |
| 2.6776 | 42700 | 0.8863 | - | - |
| 2.6839 | 42800 | 0.7438 | - | - |
| 2.6902 | 42900 | 0.5968 | - | - |
| 2.6964 | 43000 | 0.5292 | 0.7920 | - |
| 2.7027 | 43100 | 0.429 | - | - |
| 2.7090 | 43200 | 0.6001 | - | - |
| 2.7152 | 43300 | 0.7253 | - | - |
| 2.7215 | 43400 | 0.9268 | - | - |
| 2.7278 | 43500 | 0.9536 | 0.8434 | - |
| 2.7341 | 43600 | 0.6164 | - | - |
| 2.7403 | 43700 | 0.8411 | - | - |
| 2.7466 | 43800 | 1.0441 | - | - |
| 2.7529 | 43900 | 0.6473 | - | - |
| 2.7591 | 44000 | 0.8697 | 0.8089 | - |
| 2.7654 | 44100 | 0.7743 | - | - |
| 2.7717 | 44200 | 0.9118 | - | - |
| 2.7780 | 44300 | 0.7464 | - | - |
| 2.7842 | 44400 | 0.7195 | - | - |
| 2.7905 | 44500 | 0.9814 | 0.8122 | - |
| 2.7968 | 44600 | 0.5812 | - | - |
| 2.8030 | 44700 | 0.5095 | - | - |
| 2.8093 | 44800 | 0.7771 | - | - |
| 2.8156 | 44900 | 0.6714 | - | - |
| 2.8218 | 45000 | 0.5836 | 0.7786 | - |
| 2.8281 | 45100 | 1.0708 | - | - |
| 2.8344 | 45200 | 0.576 | - | - |
| 2.8407 | 45300 | 0.9657 | - | - |
| 2.8469 | 45400 | 0.8103 | - | - |
| 2.8532 | 45500 | 0.4644 | 0.7895 | - |
| 2.8595 | 45600 | 0.7485 | - | - |
| 2.8657 | 45700 | 0.9843 | - | - |
| 2.8720 | 45800 | 0.8462 | - | - |
| 2.8783 | 45900 | 0.9025 | - | - |
| 2.8846 | 46000 | 0.7014 | 0.8031 | - |
| 2.8908 | 46100 | 0.5638 | - | - |
| 2.8971 | 46200 | 0.6016 | - | - |
| 2.9034 | 46300 | 0.7257 | - | - |
| 2.9096 | 46400 | 1.1182 | - | - |
| 2.9159 | 46500 | 1.0352 | 0.8031 | - |
| 2.9222 | 46600 | 0.8413 | - | - |
| 2.9285 | 46700 | 0.7341 | - | - |
| 2.9347 | 46800 | 0.7115 | - | - |
| 2.9410 | 46900 | 0.9124 | - | - |
| 2.9473 | 47000 | 0.7988 | 0.7591 | - |
| 2.9535 | 47100 | 0.8373 | - | - |
| 2.9598 | 47200 | 0.8587 | - | - |
| 2.9661 | 47300 | 0.4961 | - | - |
| 2.9723 | 47400 | 0.7349 | - | - |
| 2.9786 | 47500 | 0.5285 | 0.7255 | - |
| 2.9849 | 47600 | 0.3715 | - | - |
| 2.9912 | 47700 | 0.811 | - | - |
| 2.9974 | 47800 | 0.6716 | - | - |
| 3.0037 | 47900 | 0.4408 | - | - |
| 3.0100 | 48000 | 0.7449 | 0.7503 | - |
| 3.0162 | 48100 | 0.4491 | - | - |
| 3.0225 | 48200 | 0.5995 | - | - |
| 3.0288 | 48300 | 0.6073 | - | - |
| 3.0351 | 48400 | 0.5753 | - | - |
| 3.0413 | 48500 | 0.6204 | 0.7650 | - |
| 3.0476 | 48600 | 0.9864 | - | - |
| 3.0539 | 48700 | 0.6648 | - | - |
| 3.0601 | 48800 | 0.4662 | - | - |
| 3.0664 | 48900 | 0.5638 | - | - |
| 3.0727 | 49000 | 0.6692 | 0.7381 | - |
| 3.0789 | 49100 | 0.6403 | - | - |
| 3.0852 | 49200 | 0.5042 | - | - |
| 3.0915 | 49300 | 0.4447 | - | - |
| 3.0978 | 49400 | 0.5983 | - | - |
| 3.1040 | 49500 | 0.6961 | 0.7289 | - |
| 3.1103 | 49600 | 0.8092 | - | - |
| 3.1166 | 49700 | 0.4172 | - | - |
| 3.1228 | 49800 | 0.6542 | - | - |
| 3.1291 | 49900 | 0.8016 | - | - |
| 3.1354 | 50000 | 0.3927 | 0.7370 | - |
| 3.1417 | 50100 | 0.4724 | - | - |
| 3.1479 | 50200 | 0.46 | - | - |
| 3.1542 | 50300 | 0.4258 | - | - |
| 3.1605 | 50400 | 0.5053 | - | - |
| 3.1667 | 50500 | 0.3406 | 0.7210 | - |
| 3.1730 | 50600 | 0.6276 | - | - |
| 3.1793 | 50700 | 0.5913 | - | - |
| 3.1856 | 50800 | 0.3902 | - | - |
| 3.1918 | 50900 | 0.5063 | - | - |
| 3.1981 | 51000 | 0.7909 | 0.7442 | - |
| 3.2044 | 51100 | 0.5071 | - | - |
| 3.2106 | 51200 | 0.5611 | - | - |
| 3.2169 | 51300 | 0.545 | - | - |
| 3.2232 | 51400 | 0.4359 | - | - |
| 3.2294 | 51500 | 0.5249 | 0.7148 | - |
| 3.2357 | 51600 | 0.6759 | - | - |
| 3.2420 | 51700 | 0.5458 | - | - |
| 3.2483 | 51800 | 0.5195 | - | - |
| 3.2545 | 51900 | 0.292 | - | - |
| 3.2608 | 52000 | 0.4826 | 0.7129 | - |
| 3.2671 | 52100 | 0.2496 | - | - |
| 3.2733 | 52200 | 0.6702 | - | - |
| 3.2796 | 52300 | 0.3192 | - | - |
| 3.2859 | 52400 | 0.66 | - | - |
| 3.2922 | 52500 | 0.6472 | 0.7146 | - |
| 3.2984 | 52600 | 0.4482 | - | - |
| 3.3047 | 52700 | 0.6618 | - | - |
| 3.3110 | 52800 | 0.4424 | - | - |
| 3.3172 | 52900 | 0.6157 | - | - |
| 3.3235 | 53000 | 0.5087 | 0.7036 | - |
| 3.3298 | 53100 | 0.5148 | - | - |
| 3.3361 | 53200 | 0.386 | - | - |
| 3.3423 | 53300 | 0.3552 | - | - |
| 3.3486 | 53400 | 0.5609 | - | - |
| 3.3549 | 53500 | 0.3549 | 0.7148 | - |
| 3.3611 | 53600 | 0.3099 | - | - |
| 3.3674 | 53700 | 0.2903 | - | - |
| 3.3737 | 53800 | 0.7385 | - | - |
| 3.3799 | 53900 | 0.7025 | - | - |
| 3.3862 | 54000 | 0.5625 | 0.7014 | - |
| 3.3925 | 54100 | 0.7545 | - | - |
| 3.3988 | 54200 | 0.4371 | - | - |
| 3.4050 | 54300 | 0.4588 | - | - |
| 3.4113 | 54400 | 0.4973 | - | - |
| 3.4176 | 54500 | 0.4534 | 0.7010 | - |
| 3.4238 | 54600 | 0.6761 | - | - |
| 3.4301 | 54700 | 0.6559 | - | - |
| 3.4364 | 54800 | 0.6087 | - | - |
| 3.4427 | 54900 | 0.601 | - | - |
| 3.4489 | 55000 | 0.4894 | 0.6706 | - |
| 3.4552 | 55100 | 0.6524 | - | - |
| 3.4615 | 55200 | 0.8268 | - | - |
| 3.4677 | 55300 | 0.1795 | - | - |
| 3.4740 | 55400 | 0.5667 | - | - |
| 3.4803 | 55500 | 0.4185 | 0.6823 | - |
| 3.4865 | 55600 | 0.615 | - | - |
| 3.4928 | 55700 | 0.6231 | - | - |
| 3.4991 | 55800 | 0.3809 | - | - |
| 3.5054 | 55900 | 0.6747 | - | - |
| 3.5116 | 56000 | 0.6484 | 0.6736 | - |
| 3.5179 | 56100 | 0.6208 | - | - |
| 3.5242 | 56200 | 0.2345 | - | - |
| 3.5304 | 56300 | 0.4494 | - | - |
| 3.5367 | 56400 | 0.327 | - | - |
| 3.5430 | 56500 | 0.5614 | 0.6762 | - |
| 3.5493 | 56600 | 0.8796 | - | - |
| 3.5555 | 56700 | 0.6068 | - | - |
| 3.5618 | 56800 | 0.4918 | - | - |
| 3.5681 | 56900 | 0.7352 | - | - |
| 3.5743 | 57000 | 0.4149 | 0.6881 | - |
| 3.5806 | 57100 | 0.3746 | - | - |
| 3.5869 | 57200 | 0.7055 | - | - |
| 3.5932 | 57300 | 0.5557 | - | - |
| 3.5994 | 57400 | 0.7734 | - | - |
| 3.6057 | 57500 | 0.5263 | 0.6800 | - |
| 3.6120 | 57600 | 0.4527 | - | - |
| 3.6182 | 57700 | 0.8339 | - | - |
| 3.6245 | 57800 | 0.7004 | - | - |
| 3.6308 | 57900 | 0.5068 | - | - |
| 3.6370 | 58000 | 0.6601 | 0.6667 | - |
| 3.6433 | 58100 | 0.8452 | - | - |
| 3.6496 | 58200 | 0.2345 | - | - |
| 3.6559 | 58300 | 0.6034 | - | - |
| 3.6621 | 58400 | 0.8962 | - | - |
| 3.6684 | 58500 | 0.5844 | 0.6755 | - |
| 3.6747 | 58600 | 0.6827 | - | - |
| 3.6809 | 58700 | 0.4087 | - | - |
| 3.6872 | 58800 | 0.6221 | - | - |
| 3.6935 | 58900 | 0.777 | - | - |
| 3.6998 | 59000 | 0.572 | 0.6737 | - |
| 3.7060 | 59100 | 0.5479 | - | - |
| 3.7123 | 59200 | 0.5078 | - | - |
| 3.7186 | 59300 | 0.6982 | - | - |
| 3.7248 | 59400 | 0.2223 | - | - |
| 3.7311 | 59500 | 0.5361 | 0.6709 | - |
| 3.7374 | 59600 | 0.6072 | - | - |
| 3.7437 | 59700 | 0.35 | - | - |
| 3.7499 | 59800 | 0.8802 | - | - |
| 3.7562 | 59900 | 0.6216 | - | - |
| 3.7625 | 60000 | 0.2514 | 0.6836 | - |
| 3.7687 | 60100 | 0.6285 | - | - |
| 3.7750 | 60200 | 0.9845 | - | - |
| 3.7813 | 60300 | 0.5355 | - | - |
| 3.7875 | 60400 | 0.495 | - | - |
| 3.7938 | 60500 | 0.6905 | 0.6725 | - |
| 3.8001 | 60600 | 0.563 | - | - |
| 3.8064 | 60700 | 0.6067 | - | - |
| 3.8126 | 60800 | 0.7585 | - | - |
| 3.8189 | 60900 | 0.4283 | - | - |
| 3.8252 | 61000 | 0.4758 | 0.6600 | - |
| 3.8314 | 61100 | 0.5462 | - | - |
| 3.8377 | 61200 | 0.649 | - | - |
| 3.8440 | 61300 | 0.5576 | - | - |
| 3.8503 | 61400 | 0.6717 | - | - |
| 3.8565 | 61500 | 0.2951 | 0.6613 | - |
| 3.8628 | 61600 | 0.457 | - | - |
| 3.8691 | 61700 | 0.473 | - | - |
| 3.8753 | 61800 | 0.5181 | - | - |
| 3.8816 | 61900 | 0.4581 | - | - |
| 3.8879 | 62000 | 0.6875 | 0.6669 | - |
| 3.8941 | 62100 | 0.3821 | - | - |
| 3.9004 | 62200 | 0.5039 | - | - |
| 3.9067 | 62300 | 0.6809 | - | - |
| 3.9130 | 62400 | 0.3591 | - | - |
| 3.9192 | 62500 | 0.6695 | 0.6654 | - |
| 3.9255 | 62600 | 0.5352 | - | - |
| 3.9318 | 62700 | 0.8635 | - | - |
| 3.9380 | 62800 | 0.73 | - | - |
| 3.9443 | 62900 | 0.4138 | - | - |
| 3.9506 | 63000 | 0.3704 | 0.6620 | - |
| 3.9569 | 63100 | 0.4831 | - | - |
| 3.9631 | 63200 | 0.5405 | - | - |
| 3.9694 | 63300 | 0.6123 | - | - |
| 3.9757 | 63400 | 0.5167 | - | - |
| 3.9819 | 63500 | 0.6967 | 0.6613 | - |
| 3.9882 | 63600 | 0.338 | - | - |
| 3.9945 | 63700 | 0.515 | - | - |
</details>
### Framework Versions
- Python: 3.8.10
- Sentence Transformers: 3.1.1
- Transformers: 4.45.1
- PyTorch: 2.4.0+cu121
- Accelerate: 0.34.2
- Datasets: 3.0.1
- Tokenizers: 0.20.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CoSENTLoss
```bibtex
@online{kexuefm-8847,
title={CoSENT: A more efficient sentence vector scheme than Sentence-BERT},
author={Su Jianlin},
year={2022},
month={Jan},
url={https://kexue.fm/archives/8847},
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
noelabu/gemma-2b-instruct-ft-mental-health-conv_v2 | noelabu | 2024-10-19T14:53:42Z | 137 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T14:49:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
g-assismoraes/llama-music_fold_1 | g-assismoraes | 2024-10-19T14:47:42Z | 7 | 0 | null | [
"safetensors",
"llama",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"license:llama3.2",
"region:us"
] | null | 2024-10-19T14:33:23Z | ---
license: llama3.2
base_model: meta-llama/Llama-3.2-3B-Instruct
tags:
- generated_from_trainer
model-index:
- name: llama-music_fold_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-music_fold_1
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7626
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6459 | 1.0 | 183 | 3.1907 |
| 1.9707 | 2.0 | 366 | 2.7626 |
| 2.293 | 3.0 | 549 | 2.9053 |
### Framework versions
- Transformers 4.43.1
- Pytorch 2.3.1
- Datasets 2.21.0
- Tokenizers 0.19.1
|
minh132/bge-tuned | minh132 | 2024-10-19T14:45:06Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-10-19T08:47:29Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.10.15
- Sentence Transformers: 3.2.0
- Transformers: 4.45.2
- PyTorch: 2.5.0+cu124
- Accelerate: 1.0.1
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citation
### BibTeX
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
glif-loradex-trainer/kklors_flux_dev_data_moshing | glif-loradex-trainer | 2024-10-19T14:43:32Z | 9 | 1 | diffusers | [
"diffusers",
"text-to-image",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:finetune:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us",
"flux",
"lora",
"base_model:adapter:black-forest-labs/FLUX.1-dev"
] | text-to-image | 2024-10-19T14:43:04Z | ---
tags:
- diffusers
- text-to-image
- template:sd-lora
- base_model:black-forest-labs/FLUX.1-dev
- base_model:finetune:black-forest-labs/FLUX.1-dev
- license:other
- region:us
- flux
- lora
widget:
- output:
url: samples/1729348858967__000003000_0.jpg
text: cars on a street in a city MOSH
- output:
url: samples/1729348881696__000003000_1.jpg
text: red mercedesMOSH
- output:
url: samples/1729348904424__000003000_2.jpg
text: colorful patterns MOSH
- output:
url: samples/1729348927153__000003000_3.jpg
text: close up of a nike sneaker, shoes MOSH
- output:
url: samples/1729348949876__000003000_4.jpg
text: young girl in a red sweater MOSH
- output:
url: samples/1729348972599__000003000_5.jpg
text: crowded restaurant MOSH
base_model: black-forest-labs/FLUX.1-dev
trigger: MOSH
instance_prompt: MOSH
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# flux_dev_data_moshing
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit) under the [Glif Loradex program](https://huggingface.co/glif-loradex-trainer) by [Glif](https://glif.app) user `kklors`.
<Gallery />
## Trigger words
You should use `MOSH` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/glif-loradex-trainer/kklors_flux_dev_data_moshing/tree/main) them in the Files & versions tab.
## License
This model is licensed under the [flux-1-dev-non-commercial-license](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
|
iTroned/hate_speech_lyddis1 | iTroned | 2024-10-19T14:40:11Z | 139 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-18T10:10:14Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: hate_speech_lyddis1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/mzd9blsd)
# hate_speech_lyddis1
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.45.2
- Pytorch 2.3.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1
|
nguyenminh4099/custom-resnet | nguyenminh4099 | 2024-10-19T14:23:58Z | 195 | 0 | transformers | [
"transformers",
"safetensors",
"custom-resnet",
"image-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | image-classification | 2024-10-19T14:23:28Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
TheGreatVAPpy/ppo-Huggy | TheGreatVAPpy | 2024-10-19T14:17:34Z | 24 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2024-10-19T14:17:29Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: TheGreatVAPpy/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jeje01/clinical_text | jeje01 | 2024-10-19T14:12:43Z | 61 | 0 | transformers | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-18T22:55:08Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: jeje01/clinical_text
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# jeje01/clinical_text
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Train Accuracy: 0.0458
- Validation Loss: nan
- Validation Accuracy: 0.0250
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'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': True, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| nan | 0.0448 | nan | 0.0250 | 0 |
| nan | 0.0458 | nan | 0.0250 | 1 |
| nan | 0.0458 | nan | 0.0250 | 2 |
| nan | 0.0458 | nan | 0.0250 | 3 |
| nan | 0.0458 | nan | 0.0250 | 4 |
### Framework versions
- Transformers 4.45.1
- TensorFlow 2.16.1
- Datasets 3.0.1
- Tokenizers 0.20.0
|
steffygreypaul/Experiment40 | steffygreypaul | 2024-10-19T13:57:32Z | 137 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T13:56:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
vhab10/Llama-3.2-3B-Instruct_Nepali_gguf_q4_k_m | vhab10 | 2024-10-19T13:42:07Z | 21 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-18T10:11:56Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
library_name: transformers
---
# Uploaded model
- **Developed by:** vhab10
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
swayamg21/kvasir-classifier | swayamg21 | 2024-10-19T13:41:33Z | 194 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-10-19T13:40:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
steffygreypaul/Experiment39 | steffygreypaul | 2024-10-19T13:35:08Z | 137 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T13:33:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **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] |
jbdoj/rare-puppers | jbdoj | 2024-10-19T13:34:23Z | 6 | 0 | null | [
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"region:us"
] | image-classification | 2024-10-19T13:34:16Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9253731369972229
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### corgi
#### samoyed
#### shiba inu
 |
MHGanainy/gpt2-xl-lora-multi-512-1-top | MHGanainy | 2024-10-19T13:27:13Z | 9 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:openai-community/gpt2-xl",
"base_model:adapter:openai-community/gpt2-xl",
"license:mit",
"region:us"
] | null | 2024-10-19T12:25:59Z | ---
library_name: peft
license: mit
base_model: openai-community/gpt2-xl
tags:
- generated_from_trainer
model-index:
- name: gpt2-xl-lora-multi-512-1-top
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-xl-lora-multi-512-1-top
This model is a fine-tuned version of [openai-community/gpt2-xl](https://huggingface.co/openai-community/gpt2-xl) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2176
## 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: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 8735
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.45.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ecdev/gemma-2b-instruct-ft-mental-health-counseling-conversations-2 | ecdev | 2024-10-19T13:25:23Z | 137 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-19T13:19:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Subsets and Splits