Instructions to use Johnyquest7/thyroid_open_llama_3b_v2b_full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Johnyquest7/thyroid_open_llama_3b_v2b_full with Transformers:

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

pipe = pipeline("text-generation", model="Johnyquest7/thyroid_open_llama_3b_v2b_full")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Johnyquest7/thyroid_open_llama_3b_v2b_full")
model = AutoModelForCausalLM.from_pretrained("Johnyquest7/thyroid_open_llama_3b_v2b_full")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Johnyquest7/thyroid_open_llama_3b_v2b_full with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Johnyquest7/thyroid_open_llama_3b_v2b_full"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Johnyquest7/thyroid_open_llama_3b_v2b_full",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Johnyquest7/thyroid_open_llama_3b_v2b_full

SGLang

How to use Johnyquest7/thyroid_open_llama_3b_v2b_full 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 "Johnyquest7/thyroid_open_llama_3b_v2b_full" \
    --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": "Johnyquest7/thyroid_open_llama_3b_v2b_full",
		"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 "Johnyquest7/thyroid_open_llama_3b_v2b_full" \
        --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": "Johnyquest7/thyroid_open_llama_3b_v2b_full",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Johnyquest7/thyroid_open_llama_3b_v2b_full with Docker Model Runner:
```
docker model run hf.co/Johnyquest7/thyroid_open_llama_3b_v2b_full
```

Document created on September 7 2023

Endo_Llama: Thyroid Edition (v1) Model Description

What is it?

Endo_Llama is a large language model (LLama 2) finetuned to answer questions related to endocrinology, with a focus on thyroid-related issues in its first version. This model aims to assist healthcare professionals and researchers in obtaining better information about thyroid conditions, treatments, and best practices.

Input Models input text only.

Output Models generate text only.

Model Architecture Model Type: Llama 2 Parameters: ~7B Training Data: Curated dataset of endocrinology questions and answers, with a focus on thyroid issues for Version 1.

Intended Use Clinical decision support Medical research

Limitations Not a substitute for professional medical advice May not cover all nuances of a complex medical condition Version 1 is primarily focused on thyroid-related issues

Training Data The model is trained on a curated dataset of endocrinology questions and answers, with an emphasis on thyroid conditions.

Usage coming soon!

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