Text Generation
Transformers
TensorBoard
Safetensors
English
mistral
text-to-text
natural-language-processing
text2text-generation
text-generation-inference
Instructions to use Indah1/BioChat10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Indah1/BioChat10 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Indah1/BioChat10")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Indah1/BioChat10") model = AutoModelForCausalLM.from_pretrained("Indah1/BioChat10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Indah1/BioChat10 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Indah1/BioChat10" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Indah1/BioChat10", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Indah1/BioChat10
- SGLang
How to use Indah1/BioChat10 with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "Indah1/BioChat10" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Indah1/BioChat10", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "Indah1/BioChat10" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Indah1/BioChat10", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Indah1/BioChat10 with Docker Model Runner:
docker model run hf.co/Indah1/BioChat10
Update README.md
Browse files
README.md
CHANGED
|
@@ -50,13 +50,16 @@ The fine-tuning data used for BioChat is derived from the [ChatDoctor-5k](https:
|
|
| 50 |
| Optimizer | AdamW_8Bit |
|
| 51 |
| Warm Up Ratio | 0.03 |
|
| 52 |
| Scheduler | Cosine |
|
| 53 |
-
| Number of Epoch | 10 |
|
| 54 |
|
| 55 |
## Evaluation
|
| 56 |
|
| 57 |
-
To determine the best model for fine-tuning, I used *perplexity* as a metric to evaluate performance and select the most optimal version. By leveraging the model's capabilities, I aim to evaluate its behavior and responses using tools like the *Word Embedding Association Test (WEAT)*. It is important to emphasize that its text generation features are intended solely for research purposes and are not yet suitable for production use. By releasing this model, we aim to drive advancements in biomedical NLP applications and contribute to best practices for the responsible development of domain-specific language models. Ensuring reliability, fairness, accuracy, and explainability remains a top priority for us.
|
| 58 |
-
|
| 59 |
-
|
|
|
|
|
|
|
|
|
|
| 60 |
### Framework versions
|
| 61 |
|
| 62 |
- PEFT 0.11.1
|
|
|
|
| 50 |
| Optimizer | AdamW_8Bit |
|
| 51 |
| Warm Up Ratio | 0.03 |
|
| 52 |
| Scheduler | Cosine |
|
| 53 |
+
| Number of Epoch | 5, 10, 15 |
|
| 54 |
|
| 55 |
## Evaluation
|
| 56 |
|
| 57 |
+
To determine the best model for fine-tuning, I used ***perplexity*** as a metric to evaluate performance and select the most optimal version. By leveraging the model's capabilities, I aim to evaluate its behavior and responses using tools like the ***Word Embedding Association Test (WEAT)***. Below are the WEAT scores and perplexity values for the model at epochs 5, 10, and 15, which helped in determining the best-performing version. It is important to emphasize that its text generation features are intended solely for research purposes and are not yet suitable for production use. By releasing this model, we aim to drive advancements in biomedical NLP applications and contribute to best practices for the responsible development of domain-specific language models. Ensuring reliability, fairness, accuracy, and explainability remains a top priority for us.
|
| 58 |
+
| Model Name | Perplexity Score | WEAT Score | Effect Size |
|
| 59 |
+
|:-------------------:|:----------------------------------:|:----------------------------------:|:----------------------------------:|
|
| 60 |
+
| **[BioChat5](https://huggingface.co/Indah1/BioChat5)** | **4.5799** | **-0.00652** | **-0.4059** |
|
| 61 |
+
| **[BioChat10](https://huggingface.co/Indah1/BioChat10)** | **4.5873** | **0.002351** | **0.06176** |
|
| 62 |
+
| **[BioChat15](https://huggingface.co/Indah1/BioChat15)** | **4.8864** | **0.00859** | **0.43890** |
|
| 63 |
### Framework versions
|
| 64 |
|
| 65 |
- PEFT 0.11.1
|