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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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
```

Johnyquest7 commited on Oct 5, 2023

Commit

9beff1e

1 Parent(s): 229b4ce

Update README.md

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@@ -1,3 +1,51 @@
 ---
 license: mit
 ---

 ---
 license: mit
 ---
+language:
+  - en
+pipeline_tag: text-generation
+tags:
+  - endocrinology
+  - medical
+  - llama
+  - thyroid
+  - diabetes
+  - pituitary
+  - adrenal
+widget:
+  - text: |
+      ### Question: What is hypothyroidism?
+      ### Input:
+      ### Response:
+inference:
+  parameters:
+    temperature: 0.1
+    max_new_tokens: 1024
+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!