Instructions to use MaziyarPanahi/calme-3.1-instruct-3b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/calme-3.1-instruct-3b with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/calme-3.1-instruct-3b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-3.1-instruct-3b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-3.1-instruct-3b", device_map="auto")
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 MaziyarPanahi/calme-3.1-instruct-3b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/calme-3.1-instruct-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": "MaziyarPanahi/calme-3.1-instruct-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/calme-3.1-instruct-3b

SGLang

How to use MaziyarPanahi/calme-3.1-instruct-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 "MaziyarPanahi/calme-3.1-instruct-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": "MaziyarPanahi/calme-3.1-instruct-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 "MaziyarPanahi/calme-3.1-instruct-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": "MaziyarPanahi/calme-3.1-instruct-3b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/calme-3.1-instruct-3b with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/calme-3.1-instruct-3b
```

calme-3.1-instruct-3b / README.md

MaziyarPanahi

Update README.md

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	---
	license: other
	license_name: qwen-research
	license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
	language:
	- fr
	- en
	pipeline_tag: text-generation
	tags:
	- chat
	- qwen
	- qwen2.5
	- finetune
	- french
	- english
	library_name: transformers
	inference: false
	model_creator: MaziyarPanahi
	quantized_by: MaziyarPanahi
	base_model: Qwen/Qwen2.5-3B
	model_name: calme-3.1-instruct-3b
	datasets:
	- MaziyarPanahi/french_instruct_sharegpt
	- arcee-ai/EvolKit-20k
	---

	<img src="./calme_3.png" alt="Calme-3 Models" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>

	> [!TIP]
	> This is avery small model, so it might not perform well for some prompts and may be sensitive to hyper parameters. I would appreciate any feedback to see if I can fix any issues in the next iteration. ❤️

	# MaziyarPanahi/calme-3.1-instruct-3b

	This model is an advanced iteration of the powerful `Qwen/Qwen2.5-3B`, specifically fine-tuned to enhance its capabilities in generic domains.


	# ⚡ Quantized GGUF

	All GGUF models are available here: [MaziyarPanahi/calme-3.1-instruct-3b-GGUF](https://huggingface.co/MaziyarPanahi/calme-3.1-instruct-3b-GGUF)


	# 🏆 [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)

	Leaderboard 2 coming soon!


	# Prompt Template

	This model uses `ChatML` prompt template:

	```
	<\|im_start\|>system
	{System}
	<\|im_end\|>
	<\|im_start\|>user
	{User}
	<\|im_end\|>
	<\|im_start\|>assistant
	{Assistant}
	````

	# How to use


	```python

	# Use a pipeline as a high-level helper

	from transformers import pipeline

	messages = [
	{"role": "user", "content": "Who are you?"},
	]
	pipe = pipeline("text-generation", model="MaziyarPanahi/calme-3.1-instruct-3b")
	pipe(messages)


	# Load model directly

	from transformers import AutoTokenizer, AutoModelForCausalLM

	tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-3.1-instruct-3b")
	model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-3.1-instruct-3b")
	```



	# Ethical Considerations

	As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.