Instructions to use Mihaiii/MiniChat-2-3B-optimum-intel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mihaiii/MiniChat-2-3B-optimum-intel with Transformers:

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

pipe = pipeline("text-generation", model="Mihaiii/MiniChat-2-3B-optimum-intel")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mihaiii/MiniChat-2-3B-optimum-intel")
model = AutoModelForCausalLM.from_pretrained("Mihaiii/MiniChat-2-3B-optimum-intel")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Mihaiii/MiniChat-2-3B-optimum-intel with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Mihaiii/MiniChat-2-3B-optimum-intel"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Mihaiii/MiniChat-2-3B-optimum-intel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Mihaiii/MiniChat-2-3B-optimum-intel

SGLang

How to use Mihaiii/MiniChat-2-3B-optimum-intel 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 "Mihaiii/MiniChat-2-3B-optimum-intel" \
    --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": "Mihaiii/MiniChat-2-3B-optimum-intel",
		"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 "Mihaiii/MiniChat-2-3B-optimum-intel" \
        --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": "Mihaiii/MiniChat-2-3B-optimum-intel",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mihaiii/MiniChat-2-3B-optimum-intel with Docker Model Runner:
```
docker model run hf.co/Mihaiii/MiniChat-2-3B-optimum-intel
```

This is a quantized version of GeneZC/MiniChat-2-3B

Optimum quantization using the command:

optimum-cli inc quantize --model GeneZC/MiniChat-2-3B --output ./mnch

Usage example:

from optimum.intel import INCModelForCausalLM
from transformers import AutoTokenizer, pipeline, AutoModelForCausalLM
import torch

model_id = "Mihaiii/MiniChat-2-3B-optimum-intel"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = INCModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)

pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
messages = [
    {
        "role": "system",
        "content": "You are a friendly chatbot who always responds in the style of a pirate",
    },
    {"role": "user", "content": "How many helicopters can a human eat in one sitting?"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=256, do_sample=True, temperature=0.0001, repetition_penalty=1.2)
print(outputs[0]["generated_text"])

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