Instructions to use Amrender/Medical_Chatbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Amrender/Medical_Chatbot with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.2") model = PeftModel.from_pretrained(base_model, "Amrender/Medical_Chatbot") - Transformers
How to use Amrender/Medical_Chatbot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Amrender/Medical_Chatbot") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Amrender/Medical_Chatbot", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Amrender/Medical_Chatbot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Amrender/Medical_Chatbot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Amrender/Medical_Chatbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Amrender/Medical_Chatbot
- SGLang
How to use Amrender/Medical_Chatbot 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 "Amrender/Medical_Chatbot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Amrender/Medical_Chatbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Amrender/Medical_Chatbot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Amrender/Medical_Chatbot", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Amrender/Medical_Chatbot with Docker Model Runner:
docker model run hf.co/Amrender/Medical_Chatbot
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9cc7d05 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | {%- if messages[0]['role'] == 'system' %}
{%- set system_message = messages[0]['content'] %}
{%- set loop_messages = messages[1:] %}
{%- else %}
{%- set loop_messages = messages %}
{%- endif %}
{{- bos_token }}
{%- for message in loop_messages %}
{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}
{{- raise_exception('After the optional system message, conversation roles must alternate user/assistant/user/assistant/...') }}
{%- endif %}
{%- if message['role'] == 'user' %}
{%- if loop.first and system_message is defined %}
{{- ' [INST] ' + system_message + '\n\n' + message['content'] + ' [/INST]' }}
{%- else %}
{{- ' [INST] ' + message['content'] + ' [/INST]' }}
{%- endif %}
{%- elif message['role'] == 'assistant' %}
{{- ' ' + message['content'] + eos_token}}
{%- else %}
{{- raise_exception('Only user and assistant roles are supported, with the exception of an initial optional system message!') }}
{%- endif %}
{%- endfor %}
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