Instructions to use SpassMedAI/stage_one_ft_eng_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SpassMedAI/stage_one_ft_eng_test with Transformers:

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

pipe = pipeline("text-generation", model="SpassMedAI/stage_one_ft_eng_test")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("SpassMedAI/stage_one_ft_eng_test")
model = AutoModelForCausalLM.from_pretrained("SpassMedAI/stage_one_ft_eng_test")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SpassMedAI/stage_one_ft_eng_test with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/SpassMedAI/stage_one_ft_eng_test

SGLang

How to use SpassMedAI/stage_one_ft_eng_test 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 "SpassMedAI/stage_one_ft_eng_test" \
    --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": "SpassMedAI/stage_one_ft_eng_test",
		"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 "SpassMedAI/stage_one_ft_eng_test" \
        --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": "SpassMedAI/stage_one_ft_eng_test",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SpassMedAI/stage_one_ft_eng_test with Docker Model Runner:
```
docker model run hf.co/SpassMedAI/stage_one_ft_eng_test
```

simonZzz86 commited on Sep 13, 2024

Commit

6341e75

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1 Parent(s): 75eca14

Update README.md

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@@ -15,7 +15,7 @@ Stage 1 - General Medical Knowledge Injected Llama3 8B model (Merged)
 <!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
 - **Developed by:** Meng Zhou, Surajsinh Parmar, Anubhav Bhatti, SpassMed Inc.
 - **Funded by [optional]:** [More Information Needed]

 <!-- Provide a longer summary of what this model is. -->
+This is the English-tuned Llama 3 8B model for Stage 1 using the [MMed-IFT-En](https://huggingface.co/datasets/SpassMedAI/MMed-IFT/viewer/English) dataset
 - **Developed by:** Meng Zhou, Surajsinh Parmar, Anubhav Bhatti, SpassMed Inc.
 - **Funded by [optional]:** [More Information Needed]