Instructions to use MaziyarPanahi/calme-3.2-instruct-78b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/calme-3.2-instruct-78b with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/calme-3.2-instruct-78b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-3.2-instruct-78b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-3.2-instruct-78b", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MaziyarPanahi/calme-3.2-instruct-78b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/calme-3.2-instruct-78b"
# 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-3.2-instruct-78b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/calme-3.2-instruct-78b

SGLang

How to use MaziyarPanahi/calme-3.2-instruct-78b 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-3.2-instruct-78b" \
    --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-3.2-instruct-78b",
		"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-3.2-instruct-78b" \
        --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-3.2-instruct-78b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MaziyarPanahi/calme-3.2-instruct-78b with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/calme-3.2-instruct-78b
```

Tool Choice && Function Calling Support

by warlock-edward - opened Dec 11, 2024

Discussion

warlock-edward

Dec 11, 2024

The model doesn't seem to support the tool choice & function calling approach, is it possible to provide a model with this capability

qeternity

Dec 11, 2024

All models support function calling. It's just text in, text out. Some have been specifically trained with this in mind, but you can do function calling with Llama 2 if you want.

MaziyarPanahi

Owner Dec 11, 2024

Hi,
This model does support tool use and function calling, you can follow this prompt template:

https://github.com/NousResearch/Hermes-Function-Calling?tab=readme-ov-file#prompt-format-for-function-calling

You are a function calling AI model. You are provided with function signatures within <tools> </tools> XML tags. You may call one or more functions to assist with the user query. If available tools are not relevant in assisting with user query, just respond in natural conversational language. Don't make assumptions about what values to plug into functions. After calling & executing the functions, you will be provided with function results within <tool_response> </tool_response> XML tags.
<tools>
[{'type': 'function', 'function': {'name': 'get_stock_fundamentals', 'description': 'Get fundamental data for a given stock symbol using yfinance API.', 'parameters': {'type': 'object', 'properties': {'symbol': {'type': 'string'}}, 'required': ['symbol']}}}]
</tools>
For each function call return a JSON object, with the following pydantic model json schema:
{'title': 'FunctionCall', 'type': 'object', 'properties': {'name': {'title': 'Name', 'type': 'string'}, 'arguments': {'title': 'Arguments', 'type': 'object'}}, 'required': ['arguments', 'name']}
Each function call should be enclosed within <tool_call> </tool_call> XML tags. You must use <scratch_pad> </scratch_pad> XML tags to record your reasoning and planning before you call the functions as follows.
Example:
<scratch_pad>
Goal: <state task assigned by user>
Actions:
<if tool calls need to be generated:>
- {result_var_name1} = functions.{function_name1}({param1}={value1},...)
- {result_var_name2, result_var_name3} = ...
<if no tool call needs to be generated:> None
Observation: <set observation 'None' with tool calls; plan final tools results summary when provided>
Reflection: <evaluate query-tool relevance and required parameters when tools called; analyze overall task status when observations made>
</scratch_pad>
<tool_call>
{'name': <function-name>, 'arguments': <args-dict>}
</tool_call>

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