Instructions to use meetkai/functionary-medium-v3.0-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use meetkai/functionary-medium-v3.0-AWQ with Transformers:

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

pipe = pipeline("text-generation", model="meetkai/functionary-medium-v3.0-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("meetkai/functionary-medium-v3.0-AWQ")
model = AutoModelForCausalLM.from_pretrained("meetkai/functionary-medium-v3.0-AWQ")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use meetkai/functionary-medium-v3.0-AWQ with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "meetkai/functionary-medium-v3.0-AWQ"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "meetkai/functionary-medium-v3.0-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/meetkai/functionary-medium-v3.0-AWQ

SGLang

How to use meetkai/functionary-medium-v3.0-AWQ 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 "meetkai/functionary-medium-v3.0-AWQ" \
    --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": "meetkai/functionary-medium-v3.0-AWQ",
		"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 "meetkai/functionary-medium-v3.0-AWQ" \
        --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": "meetkai/functionary-medium-v3.0-AWQ",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use meetkai/functionary-medium-v3.0-AWQ with Docker Model Runner:
```
docker model run hf.co/meetkai/functionary-medium-v3.0-AWQ
```

Model Size

by hsjoo2442 - opened Jun 19, 2024

Discussion

hsjoo2442

Jun 19, 2024

Hello,
I'd like to ask why the model size is stated as 11.3B in the model card.
From what I'm aware of, functionary-medium-v3.0 is based on llama 3, which has 70B parameters.
Does the parameter size shrink with 4bit quantization?
Any help would be appreciated.

jeffreymeetkai

Jul 1, 2024

Hi, I am unsure why the quantized model has 11.3B only but looking at the following models quantized by the original author and major contributors of AutoAWQ, it seems like 4-bit quantization by AWQ reduces the model size of Llama-3 70B by approximately 6.2x.

https://huggingface.co/casperhansen/llama-3-70b-instruct-awq
https://huggingface.co/TechxGenus/Meta-Llama-3-70B-Instruct-AWQ

We may need to read the original AWQ paper for more details and clues as to why this is so. This is interesting. Do let me know if you have any findings!

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