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

File size: 4,089 Bytes

---
# 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]