Instructions to use aaditya/Llama3-OpenBioLLM-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aaditya/Llama3-OpenBioLLM-8B with Transformers:

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

pipe = pipeline("text-generation", model="aaditya/Llama3-OpenBioLLM-8B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("aaditya/Llama3-OpenBioLLM-8B")
model = AutoModelForCausalLM.from_pretrained("aaditya/Llama3-OpenBioLLM-8B", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use aaditya/Llama3-OpenBioLLM-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aaditya/Llama3-OpenBioLLM-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aaditya/Llama3-OpenBioLLM-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/aaditya/Llama3-OpenBioLLM-8B

SGLang

How to use aaditya/Llama3-OpenBioLLM-8B 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 "aaditya/Llama3-OpenBioLLM-8B" \
    --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": "aaditya/Llama3-OpenBioLLM-8B",
		"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 "aaditya/Llama3-OpenBioLLM-8B" \
        --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": "aaditya/Llama3-OpenBioLLM-8B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use aaditya/Llama3-OpenBioLLM-8B with Docker Model Runner:
```
docker model run hf.co/aaditya/Llama3-OpenBioLLM-8B
```

aaditya commited on Apr 26, 2024

Commit

cef1ebb

verified ·

1 Parent(s): df4ab83

Update README.md

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README.md +4 -3

README.md CHANGED Viewed

@@ -222,7 +222,7 @@ print(outputs[0]["generated_text"][len(prompt):])
 |                    | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg   |
 |--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------|
-| **OpenBioLLM-70B** | **92.93**       | **93.197**           | **83.904**  | **93.75**       | **93.827**          | **85.749**           | 78.162       | 78.97    | **74.014**  | **86.05588** |
 | Med-PaLM-2             | 88.3        | 90               | 77.8    | **95.2**         | 94.4            | 80.9             | **79.7**         | **79.2**     | 71.3    | 84.08 |
 | **GPT-4**              | 86.04       | 91               | 80      | 93.01        | **95.14**           | 76.88            | 78.87        | 75.2     | 69.52   | 82.85 |
 | Med-PaLM-1 (Flan-PaLM) | 80.4        | 75               | 63.7    | 83.8         | 88.9            | 76.3             | 67.6         | 79       | 57.6    | 74.7  |
@@ -238,8 +238,9 @@ print(outputs[0]["generated_text"][len(prompt):])
 | AlpaCare-llama2-7b     | 49.81       | 49               | 45.92   | 33.82        | 50              | 43.35            | 29.77        | 72.2     | 34.42   | 45.36 |
 | ClinicalGPT            | 30.56       | 27               | 30.37   | 19.48        | 25              | 24.27            | 26.08        | 63.8     | 28.18   | 30.52 |
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png)
 ## Detailed Medical Subjectwise accuracy

 |                    | Clinical KG | Medical Genetics | Anatomy | Pro Medicine | College Biology | College Medicine | MedQA 4 opts | PubMedQA | MedMCQA | Avg   |
 |--------------------|-------------|------------------|---------|--------------|-----------------|------------------|--------------|----------|---------|-------|
+| **OpenBioLLM-70B** | **92.93**       | **93.197**           | **83.904**  | 93.75       | **93.827**          | **85.749**           | 78.162       | 78.97    | **74.014**  | **86.05588** |
 | Med-PaLM-2             | 88.3        | 90               | 77.8    | **95.2**         | 94.4            | 80.9             | **79.7**         | **79.2**     | 71.3    | 84.08 |
 | **GPT-4**              | 86.04       | 91               | 80      | 93.01        | **95.14**           | 76.88            | 78.87        | 75.2     | 69.52   | 82.85 |
 | Med-PaLM-1 (Flan-PaLM) | 80.4        | 75               | 63.7    | 83.8         | 88.9            | 76.3             | 67.6         | 79       | 57.6    | 74.7  |
 | AlpaCare-llama2-7b     | 49.81       | 49               | 45.92   | 33.82        | 50              | 43.35            | 29.77        | 72.2     | 34.42   | 45.36 |
 | ClinicalGPT            | 30.56       | 27               | 30.37   | 19.48        | 25              | 24.27            | 26.08        | 63.8     | 28.18   | 30.52 |
+<div align="center">
+<img width="1600px" src="https://cdn-uploads.huggingface.co/production/uploads/5f3fe13d79c1ba4c353d0c19/_SzdcJSBjZyo8RS1bTEkP.png">
+</div>
 ## Detailed Medical Subjectwise accuracy