Instructions to use minpeter/Llama-3.2-1B-chatml-tool-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use minpeter/Llama-3.2-1B-chatml-tool-v2 with Transformers:

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

pipe = pipeline("text-generation", model="minpeter/Llama-3.2-1B-chatml-tool-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("minpeter/Llama-3.2-1B-chatml-tool-v2")
model = AutoModelForCausalLM.from_pretrained("minpeter/Llama-3.2-1B-chatml-tool-v2")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use minpeter/Llama-3.2-1B-chatml-tool-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "minpeter/Llama-3.2-1B-chatml-tool-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "minpeter/Llama-3.2-1B-chatml-tool-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/minpeter/Llama-3.2-1B-chatml-tool-v2

SGLang

How to use minpeter/Llama-3.2-1B-chatml-tool-v2 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 "minpeter/Llama-3.2-1B-chatml-tool-v2" \
    --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": "minpeter/Llama-3.2-1B-chatml-tool-v2",
		"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 "minpeter/Llama-3.2-1B-chatml-tool-v2" \
        --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": "minpeter/Llama-3.2-1B-chatml-tool-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use minpeter/Llama-3.2-1B-chatml-tool-v2 with Docker Model Runner:
```
docker model run hf.co/minpeter/Llama-3.2-1B-chatml-tool-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Model Performance Comparison (BFCL)

task name	minpeter/Llama-3.2-1B-chatml-tool-v2	meta-llama/Llama-3.2-1B-Instruct (measure)	meta-llama/Llama-3.2-1B-Instruct (Reported)
parallel_multiple	0.000	0.025	0.15
parallel	0.000	0.035	0.36
simple	0.72	0.215	0.2925
multiple	0.695	0.17	0.335

*Parallel calls are not taken into account. 0 points are expected. We plan to fix this in v3.

Note

The only difference from Llama-3.2-1B-chatml-tool-v1 is that it uses AlternateTokenizer, which does not define tool-related tokens (<tools>, <tool_call>, <tool_response>).

In the case of the existing tool-AlternateTokenizer, the <tool_call> tag was not properly generated before the function call, but in v2, it was observed that it performed well when trained with the general AlternateTokenizer.

We need to check whether this phenomenon is repeated in larger models (3B, 8B).

Downloads last month: 9

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for minpeter/Llama-3.2-1B-chatml-tool-v2

minpeter/Llama-3.2-1B-AlternateTokenizer-chatml

minpeter/QLoRA-Llama-3.2-1B-chatml-tool-v2

Merge model

this model

Quantizations

1 model

minpeter
/

Llama-3.2-1B-chatml-tool-v2

Model Performance Comparison (BFCL)

Note

Model tree for minpeter/Llama-3.2-1B-chatml-tool-v2

Datasets used to train minpeter/Llama-3.2-1B-chatml-tool-v2