Instructions to use johnsnowlabs/JSL-MedLlama-3-8B-v2.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use johnsnowlabs/JSL-MedLlama-3-8B-v2.0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="johnsnowlabs/JSL-MedLlama-3-8B-v2.0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("johnsnowlabs/JSL-MedLlama-3-8B-v2.0") model = AutoModelForCausalLM.from_pretrained("johnsnowlabs/JSL-MedLlama-3-8B-v2.0") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use johnsnowlabs/JSL-MedLlama-3-8B-v2.0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "johnsnowlabs/JSL-MedLlama-3-8B-v2.0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "johnsnowlabs/JSL-MedLlama-3-8B-v2.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/johnsnowlabs/JSL-MedLlama-3-8B-v2.0
- SGLang
How to use johnsnowlabs/JSL-MedLlama-3-8B-v2.0 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 "johnsnowlabs/JSL-MedLlama-3-8B-v2.0" \ --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": "johnsnowlabs/JSL-MedLlama-3-8B-v2.0", "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 "johnsnowlabs/JSL-MedLlama-3-8B-v2.0" \ --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": "johnsnowlabs/JSL-MedLlama-3-8B-v2.0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use johnsnowlabs/JSL-MedLlama-3-8B-v2.0 with Docker Model Runner:
docker model run hf.co/johnsnowlabs/JSL-MedLlama-3-8B-v2.0
JSL-MedLlama-3-8B-v2.0
This model is developed by John Snow Labs.
This model is available under a CC-BY-NC-ND license and must also conform to this Acceptable Use Policy. If you need to license this model for commercial use, please contact us at info@johnsnowlabs.com.
π» Usage
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/JSL-MedLlama-3-8B-v2.0"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
π Evaluation
| Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| stem | N/A | none | 0 | acc | 0.6466 | Β± | 0.0056 |
| none | 0 | acc_norm | 0.6124 | Β± | 0.0066 | ||
| - medmcqa | Yaml | none | 0 | acc | 0.6118 | Β± | 0.0075 |
| none | 0 | acc_norm | 0.6118 | Β± | 0.0075 | ||
| - medqa_4options | Yaml | none | 0 | acc | 0.6143 | Β± | 0.0136 |
| none | 0 | acc_norm | 0.6143 | Β± | 0.0136 | ||
| - anatomy (mmlu) | 0 | none | 0 | acc | 0.7185 | Β± | 0.0389 |
| - clinical_knowledge (mmlu) | 0 | none | 0 | acc | 0.7811 | Β± | 0.0254 |
| - college_biology (mmlu) | 0 | none | 0 | acc | 0.8264 | Β± | 0.0317 |
| - college_medicine (mmlu) | 0 | none | 0 | acc | 0.7110 | Β± | 0.0346 |
| - medical_genetics (mmlu) | 0 | none | 0 | acc | 0.8300 | Β± | 0.0378 |
| - professional_medicine (mmlu) | 0 | none | 0 | acc | 0.7868 | Β± | 0.0249 |
| - pubmedqa | 1 | none | 0 | acc | 0.7420 | Β± | 0.0196 |
| Groups | Version | Filter | n-shot | Metric | Value | Stderr | |
|---|---|---|---|---|---|---|---|
| stem | N/A | none | 0 | acc | 0.6466 | Β± | 0.0056 |
| none | 0 | acc_norm | 0.6124 | Β± | 0.0066 |
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