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
```

MaziyarPanahi commited on Nov 7, 2024

Commit

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