Instructions to use LLM360/K2-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LLM360/K2-Chat with Transformers:

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

pipe = pipeline("text-generation", model="LLM360/K2-Chat")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LLM360/K2-Chat")
model = AutoModelForCausalLM.from_pretrained("LLM360/K2-Chat")
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

vLLM

How to use LLM360/K2-Chat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LLM360/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": "LLM360/K2-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LLM360/K2-Chat

SGLang

How to use LLM360/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 "LLM360/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": "LLM360/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 "LLM360/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": "LLM360/K2-Chat",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LLM360/K2-Chat with Docker Model Runner:
```
docker model run hf.co/LLM360/K2-Chat
```

hunterhector commited on Jan 12, 2025

Commit

fe59027

verified ·

1 Parent(s): 58fdb6c

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@@ -32,7 +32,7 @@ We utilized the following datasets:
 | LogiQA2.0 (0-shot)      | **38.0**    | 36.83    |
 | BBH CoT (0-shot)        | 64.9    | **70.37**    |
 | **Code Benchmarks**     |         |          |
-| HumanEval (pass@1)      | 47.9    | **71.20**    |
 | **Domain Specific (Medical)** |   |          |
 | MedQA (0-shot)          | **53.6**    | 52.87    |
 | MedMCQA (5-shot)        | **51.3**    | 50.71    |

 | LogiQA2.0 (0-shot)      | **38.0**    | 36.83    |
 | BBH CoT (0-shot)        | 64.9    | **70.37**    |
 | **Code Benchmarks**     |         |          |
+| HumanEval (pass@1)      | 47.9    | **60.82**    |
 | **Domain Specific (Medical)** |   |          |
 | MedQA (0-shot)          | **53.6**    | 52.87    |
 | MedMCQA (5-shot)        | **51.3**    | 50.71    |