Instructions to use IFM/K2-Horizon-0.9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IFM/K2-Horizon-0.9B with Transformers:

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

pipe = pipeline("text-generation", model="IFM/K2-Horizon-0.9B", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("IFM/K2-Horizon-0.9B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IFM/K2-Horizon-0.9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IFM/K2-Horizon-0.9B"
# 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-Horizon-0.9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IFM/K2-Horizon-0.9B

SGLang

How to use IFM/K2-Horizon-0.9B 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-Horizon-0.9B" \
    --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-Horizon-0.9B",
		"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-Horizon-0.9B" \
        --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-Horizon-0.9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IFM/K2-Horizon-0.9B with Docker Model Runner:
```
docker model run hf.co/IFM/K2-Horizon-0.9B
```

chen11003 commited on 7 days ago

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@@ -212,21 +212,6 @@ submetric over the pre-distillation merge. This makes the model useful for
 research on compact reasoning models, local inference, distillation, and
 task-specific adaptation.
-## Model Card Comparison Table
-| Benchmark | **K2-Horizon-0.9B** | MiniCPM5-1B | Qwen3.5-0.8B | Qwen3.5-2B |
-|---|---:|---:|---:|---:|
-| IFEval (strict instruction) | **80.8†** | 80.41‡ | 44.0‡ | 78.6‡ |
-| GPQA-Diamond (avg@16) | **27.3†** | 26.26‡ | 11.9‡ | 51.6‡ |
-| HMMT February 2026 (avg@16) | **25.8†** | 23.3† | 0.57‡ | 18.56† |
-| AIME 2025 (avg@16) | **41.7†** | 40.42‡ | 1.04‡ | 26.46† |
-| AIME 2026 (avg@16) | **48.5†** | 40.42‡ | 0.21‡ | 25.42† |
-| HumanEval+ (pass@1) | **79.9†** | 65.2† | 26.22† | 42.68† |
-| MBPP+ (pass@1) | **68.0†** | 60.6† | 32.8† | 47.09† |
-| LiveCodeBench v6 (avg@3) | **37.41†** | 33.52‡ | 5.33‡ | 13.08† |
-- **† Local  result.**
-- **‡ Published comparison/model-card value; protocol is not necessarily matched.**
 ## How to Use

 research on compact reasoning models, local inference, distillation, and
 task-specific adaptation.
 ## How to Use