Instructions to use SNOWTEAM/DoctorLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SNOWTEAM/DoctorLLM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SNOWTEAM/DoctorLLM")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SNOWTEAM/DoctorLLM") model = AutoModelForCausalLM.from_pretrained("SNOWTEAM/DoctorLLM") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SNOWTEAM/DoctorLLM with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SNOWTEAM/DoctorLLM" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SNOWTEAM/DoctorLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SNOWTEAM/DoctorLLM
- SGLang
How to use SNOWTEAM/DoctorLLM 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 "SNOWTEAM/DoctorLLM" \ --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": "SNOWTEAM/DoctorLLM", "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 "SNOWTEAM/DoctorLLM" \ --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": "SNOWTEAM/DoctorLLM", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SNOWTEAM/DoctorLLM with Docker Model Runner:
docker model run hf.co/SNOWTEAM/DoctorLLM
Update README.md
Browse files
README.md
CHANGED
|
@@ -7,7 +7,7 @@ summary: "A specialized language model for medical applications, refined through
|
|
| 7 |
|
| 8 |
## Overview
|
| 9 |
|
| 10 |
-
SNOWTEAM/sft_medico-mistral is a specialized language model designed for medical applications, further refined through instruction tuning to enhance its ability to respond to various medical-related instructions. This tuning leverages the embedded medical knowledge within the
|
| 11 |
|
| 12 |
## Model Description
|
| 13 |
|
|
@@ -39,7 +39,7 @@ Using open source instruction tuning datasets are composed of three main parts:
|
|
| 39 |
|
| 40 |
### Medical-Specific Instruction Tuning
|
| 41 |
|
| 42 |
-
By combining the above three parts, we form a large-scale, high-quality, medical-specific instruction tuning dataset,
|
| 43 |
|
| 44 |
## Training Details
|
| 45 |
|
|
|
|
| 7 |
|
| 8 |
## Overview
|
| 9 |
|
| 10 |
+
SNOWTEAM/sft_medico-mistral is a specialized language model designed for medical applications, further refined through instruction tuning to enhance its ability to respond to various medical-related instructions. This tuning leverages the embedded medical knowledge within the Medico-mistral model, focusing on medical consulting conversations, medical rationale QA, and medical knowledge graph prompting.
|
| 11 |
|
| 12 |
## Model Description
|
| 13 |
|
|
|
|
| 39 |
|
| 40 |
### Medical-Specific Instruction Tuning
|
| 41 |
|
| 42 |
+
By combining the above three parts, we form a large-scale, high-quality, medical-specific instruction tuning dataset, consisting of 202M tokens. We further tune Medico-mistral on this dataset, resulting in sft_medico-mistral.
|
| 43 |
|
| 44 |
## Training Details
|
| 45 |
|