Instructions to use SNOWTEAM/MedicoLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SNOWTEAM/MedicoLLM with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SNOWTEAM/MedicoLLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SNOWTEAM/MedicoLLM")
model = AutoModelForCausalLM.from_pretrained("SNOWTEAM/MedicoLLM")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use SNOWTEAM/MedicoLLM with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SNOWTEAM/MedicoLLM"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SNOWTEAM/MedicoLLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SNOWTEAM/MedicoLLM

SGLang

How to use SNOWTEAM/MedicoLLM 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/MedicoLLM" \
    --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": "SNOWTEAM/MedicoLLM",
		"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 "SNOWTEAM/MedicoLLM" \
        --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": "SNOWTEAM/MedicoLLM",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use SNOWTEAM/MedicoLLM with Docker Model Runner:
```
docker model run hf.co/SNOWTEAM/MedicoLLM
```

MedicoLLM / README.md

SNOWTEAM2023

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	---
	# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
	# Doc / guide: https://huggingface.co/docs/hub/model-cards
	{}
	---

	# Overview

	SNOWTEAM/medico-mistral is a specialized language model designed for medical applications. This transformer-based decoder-only language model is based on the Mistral 8x7B model and has been fine-tuned through global parameter adjustments, leveraging a comprehensive dataset that includes 4.8 million research papers and 10,000 medical books.

	### Model Description

	<!-- Provide a longer summary of what this model is. -->



	- Base Model: [Mistral 8x7B model- Instruct](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1)
	- Model type: Transformer-based decoder-only language model
	- Language(s) (NLP): English


	## Training Dataset
	- Dataset Size: 4.8 million research papers and 10,000 medical books.
	- Data Diversity: Includes a wide range of medical fields, ensuring comprehensive coverage of medical knowledge.
	- Preprocessing:
	- Books: We collected 10,000 textbooks from various sources such as the open-library, university libraries, and reputable publishers, covering a wide range of medical specialties. For preprocessing, we extracted text content from PDF files, then performed data cleaning through de-duplication and content filtering. This involved removing extraneous elements such as URLs, author lists, superfluous information, document contents, references, and citations.
	- Papers: Academic papers are a valuable knowledge resource due to their high-quality, cutting-edge medical information. We started with the S2ORC (Lo et al. 2020) dataset, which contains 81.1 million English-language academic papers. From this, we selected biomedical-related papers based on the presence of corresponding PubMed Central (PMC) IDs. This resulted in approximately 4.8 million biomedical papers, totaling over 75 billion tokens.

	### Model Sources [optional]

	<!-- Provide the basic links for the model. -->

	- Repository: https://huggingface.co/SNOWTEAM/medico-mistral
	- Paper [optional]:
	- Demo [optional]:

	## How to Get Started with the Model
	```python
	import transformers
	import torch

	model_path = "SNOWTEAM/medico-mistral"
	model = AutoModelForCausalLM.from_pretrained(
	model_path,device_map="auto",
	max_memory=max_memory_mapping,
	torch_dtype=torch.float16,
	)
	tokenizer = AutoTokenizer.from_pretrained("SNOWTEAM/medico-mistral")
	input_text = ""
	input_ids = tokenizer(input_text, return_tensors="pt").input_ids
	output_ids = model.generate(input_ids=input_ids.cuda(),
	max_new_tokens=300,
	pad_token_id=tokenizer.eos_token_id,)
	output_text = tokenizer.batch_decode(output_ids[:, input_ids.shape[1]:],skip_special_tokens=True)[0]
	print(output_text)
	```

	## Training Details



	#### 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. -->

	## Evaluation


	### 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


	## 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]

	## Model Card Authors [optional]

	[More Information Needed]

	## Model Card Contact

	[More Information Needed]