Instructions to use Heng666/StableMedZephyr-Merged-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Heng666/StableMedZephyr-Merged-3b with Transformers:

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

pipe = pipeline("text-generation", model="Heng666/StableMedZephyr-Merged-3b", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Heng666/StableMedZephyr-Merged-3b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Heng666/StableMedZephyr-Merged-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Heng666/StableMedZephyr-Merged-3b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Heng666/StableMedZephyr-Merged-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Heng666/StableMedZephyr-Merged-3b

SGLang

How to use Heng666/StableMedZephyr-Merged-3b 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 "Heng666/StableMedZephyr-Merged-3b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Heng666/StableMedZephyr-Merged-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Heng666/StableMedZephyr-Merged-3b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Heng666/StableMedZephyr-Merged-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Heng666/StableMedZephyr-Merged-3b with Docker Model Runner:
```
docker model run hf.co/Heng666/StableMedZephyr-Merged-3b
```

FOR EXPERIMENT

Description

stabilityai/stablelm-zephyr-3b, StableMed-3b merged with a new, experimental implementation of "dare ties" via mergekit. See:

Language Models are Super Mario: Absorbing Abilities from Homologous Models as a Free Lunch

https://github.com/cg123/mergekit/tree/dare

Usage

StableLM Zephyr 3B uses the following instruction format:

<|user|>
List 3 synonyms for the word "tiny"<|endoftext|>
<|assistant|>
1. Dwarf
2. Little
3. Petite<|endoftext|>

Testing Notes

Merged in mergekit with the following config, and the tokenizer from chargoddard's Yi-Llama:

models:
  - model: stabilityai/stablelm-zephyr-3b
    # no parameters necessary for base model
  - model: cxllin/StableMed-3b
    parameters:
      weight: 0.08
      density: 0.5
merge_method: dare_ties
base_model: stabilityai/stablelm-zephyr-3b
parameters:
  int8_mask: true
dtype: bfloat16

Model Details

License: StabilityAI Non-Commercial Research Community License

Downloads last month: 4

Safetensors

Model size

3B params

Tensor type

BF16