Instructions to use IFM/K2-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IFM/K2-Chat with Transformers:

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

pipe = pipeline("text-generation", model="IFM/K2-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("IFM/K2-Chat")
model = AutoModelForCausalLM.from_pretrained("IFM/K2-Chat", 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 IFM/K2-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IFM/K2-Chat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "IFM/K2-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IFM/K2-Chat

SGLang

How to use IFM/K2-Chat 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 "IFM/K2-Chat" \
    --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": "IFM/K2-Chat",
		"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 "IFM/K2-Chat" \
        --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": "IFM/K2-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IFM/K2-Chat with Docker Model Runner:
```
docker model run hf.co/IFM/K2-Chat
```

victormiller commited on Oct 30, 2024

Commit

4d89b34

verified ·

1 Parent(s): c6d651f

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -44,9 +44,9 @@ We utilized the following datasets:
 | Avg Score               | 58.88   | 61.30    |
-## Function Calling
-# Chat Template
 Our model reuses [K2-Chat](https://huggingface.co/LLM360/K2-Chat) as the prompt format and is specifically trained for function calling. Different system prompts enable different ways to interact with this model. Note that the two modes are currently mainly tested individually, designing prompts that make them work togehter is possible but currently untested. It should be also possible to stimulate the model to produce function call behavior by injecting special token `<tool_call>` and expect the model to finish it. In this guide we mention the intended basic usage of the model.
 ## Conversational Chats

 | Avg Score               | 58.88   | 61.30    |
+# Function Calling
+## Chat Template
 Our model reuses [K2-Chat](https://huggingface.co/LLM360/K2-Chat) as the prompt format and is specifically trained for function calling. Different system prompts enable different ways to interact with this model. Note that the two modes are currently mainly tested individually, designing prompts that make them work togehter is possible but currently untested. It should be also possible to stimulate the model to produce function call behavior by injecting special token `<tool_call>` and expect the model to finish it. In this guide we mention the intended basic usage of the model.
 ## Conversational Chats