Instructions to use Echelon-AI/Med-Qwen2-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Echelon-AI/Med-Qwen2-7B with Transformers:

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

pipe = pipeline("text-generation", model="Echelon-AI/Med-Qwen2-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Echelon-AI/Med-Qwen2-7B")
model = AutoModelForCausalLM.from_pretrained("Echelon-AI/Med-Qwen2-7B")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Echelon-AI/Med-Qwen2-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Echelon-AI/Med-Qwen2-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Echelon-AI/Med-Qwen2-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Echelon-AI/Med-Qwen2-7B

SGLang

How to use Echelon-AI/Med-Qwen2-7B 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 "Echelon-AI/Med-Qwen2-7B" \
    --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": "Echelon-AI/Med-Qwen2-7B",
		"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 "Echelon-AI/Med-Qwen2-7B" \
        --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": "Echelon-AI/Med-Qwen2-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Echelon-AI/Med-Qwen2-7B with Docker Model Runner:
```
docker model run hf.co/Echelon-AI/Med-Qwen2-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen2 7B, after finetuning on a medical dataset, demonstrates enhanced performance in medical text understanding and generation

Model Description

The model shows improved accuracy in diagnosing medical conditions, generating specialized medical texts, and responding to medical queries with contextually relevant information. This adaptation equips Med-Qwen2 to support advanced applications in healthcare, offering nuanced insights and precise language processing tailored for medical professionals and patients alike

Finetuned from model : https://huggingface.co/Qwen/Qwen2-7B-Instruct

GGUF

Uses

Diagnosing medical conditions with improved accuracy.
Generating specialized medical texts and reports.
Providing contextually relevant responses to medical queries.
Supporting advanced applications in healthcare with precise language processing.

Downloads last month: 76

Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Echelon-AI/Med-Qwen2-7B

Quantizations

3 models

Echelon-AI
/

Med-Qwen2-7B

Model Description

Uses

Model tree for Echelon-AI/Med-Qwen2-7B

Dataset used to train Echelon-AI/Med-Qwen2-7B