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
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# BioChat Model
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## Model Details
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### Model Description
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# BioChat Model
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Source Paper: [BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains](https://arxiv.org/abs/2402.10373)
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BioChat is a language model fine-tuned using the ChatDoctor dataset from [ChatDoctor-5k](https://huggingface.co/datasets/LinhDuong/chatdoctor-5k). Specifically designed for medical conversations, BioChat enables users to engage in interactive discussions with a virtual doctor. Whether you are seeking advice about symptoms you are experiencing, exploring possible health conditions, or looking for general medical insights, BioChat is built to assist in a reliable and informative manner.
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In addition to its ability to act as a virtual medical consultant, we are still in the early stages of exploring the generation capabilities and limitations of this model. 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 promote best practices for the responsible training and deployment of domain-specific language models. Ensuring reliability, accuracy, and explainability remains a top priority for us.
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## Model Details
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### Model Description
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