Instructions to use wanglab/ClinicalCamel-70B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use wanglab/ClinicalCamel-70B with Transformers:

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

pipe = pipeline("text-generation", model="wanglab/ClinicalCamel-70B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("wanglab/ClinicalCamel-70B")
model = AutoModelForCausalLM.from_pretrained("wanglab/ClinicalCamel-70B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use wanglab/ClinicalCamel-70B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wanglab/ClinicalCamel-70B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wanglab/ClinicalCamel-70B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/wanglab/ClinicalCamel-70B

SGLang

How to use wanglab/ClinicalCamel-70B 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 "wanglab/ClinicalCamel-70B" \
    --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": "wanglab/ClinicalCamel-70B",
		"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 "wanglab/ClinicalCamel-70B" \
        --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": "wanglab/ClinicalCamel-70B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use wanglab/ClinicalCamel-70B with Docker Model Runner:
```
docker model run hf.co/wanglab/ClinicalCamel-70B
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

You agree not to use the model for healthcare decision-making or commercial use

Clinical Camel

Model Description

Clinical Camel is an open large language model (LLM), fine-tuned on the LLaMA-2 70B architecture using QLoRA. It is tailored for the medical and clinical research, capable of processing and generating relevant content.

Review our pre-print for more details: Clinical Camel - Pre-print

Performance

Clinical Camel demonstrates competitive performance on medical benchmarks.

Table: Five-Shot Performance of Clinical Camel-70B (C70), GPT3.5, GPT4, and Med-PaLM 2 on Various Medical Datasets

Dataset	ClinicalCamel-70B	GPT3.5	GPT4	Med-PaLM 2
MMLU Anatomy	65.2	60.7	80.0	77.8
MMLU Clinical Knowledge	72.8	68.7	86.4	88.3
MMLU College Biology	81.2	72.9	93.8	94.4
MMLU College Medicine	68.2	63.6	76.3	80.9
MMLU Medical Genetics	69.0	68.0	92.0	90.0
MMLU Professional Medicine	75.0	69.8	93.8	95.2
MedMCQA	54.2	51.0	72.4	71.3
MedQA (USMLE)	60.7	53.6	81.4	79.7
PubMedQA	77.9	60.2	74.4	79.2
USMLE Sample Exam	64.3	58.5	86.6	-

Evaluation Datasets:

The performance of Clinical Camel was benchmarked across several datasets, including:

Evaluation Reproduction:

To reproduce the evaluations with lm-evaluation-harness see the 'TaskFiles' folder

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Spaces using wanglab/ClinicalCamel-70B 3

Paper for wanglab/ClinicalCamel-70B

Clinical Camel: An Open-Source Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding

Paper • 2305.12031 • Published May 19, 2023 • 5