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
```

victormiller commited on Jul 29, 2024

Commit

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@@ -14,6 +14,19 @@ Evaluations include standard best practice benchmarks, medical, math, and coding
 <center><img src="k2_chat_table_of_tables.png" alt="k2 big eval table"/></center>
 ## Datasets and Mix
 | Subset      | #Tokens | Avg. #Q | Avg. Query Len | Avg. #R | Avg. Reply Len |

 <center><img src="k2_chat_table_of_tables.png" alt="k2 big eval table"/></center>
+## Open LLM Leaderboard
+| Evaluation      | Score      | Raw Score      |
+| ----------- | ----------- | ----------- |
+| IFEval   | 51.52        | 52       |
+| BBH   | 33.79        | 54       |
+| Math Lvl 5   | 1.59        | 2       |
+| GPQA   | 7.49        | 31       |
+| MUSR   | 16.82        | 46       |
+| MMLU-PRO   | 26.34        | 34       |
+| Average   | 22.93        | 36.5       |
 ## Datasets and Mix
 | Subset      | #Tokens | Avg. #Q | Avg. Query Len | Avg. #R | Avg. Reply Len |