Instructions to use IFM/K2-Horizon-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use IFM/K2-Horizon-7B with Transformers:

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

pipe = pipeline("text-generation", model="IFM/K2-Horizon-7B", 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-7B", trust_remote_code=True, device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use IFM/K2-Horizon-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IFM/K2-Horizon-7B"
# 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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IFM/K2-Horizon-7B

SGLang

How to use IFM/K2-Horizon-7B 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-7B" \
    --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-7B",
		"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-7B" \
        --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-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use IFM/K2-Horizon-7B with Docker Model Runner:
```
docker model run hf.co/IFM/K2-Horizon-7B
```

K2-Horizon-7B

File size: 754 Bytes

8fbc6fc

{
  "architecture": "K2HorizonForCausalLM",
  "checkpoint": "/mnt/weka/home/mrunner/workspace/checkpoints/k2horizon_bf16_safetensors/k2v3-7B_iso_attn_shared_small_phase2_sft_38n_2251790/checkpoints/checkpoint_0002500",
  "dtypes": [
    "BF16"
  ],
  "logical_bytes": 17998356480,
  "migration": {
    "completed_utc": "2026-09-02T19:23:56Z",
    "source_checkpoint": "/mnt/weka/shrd/k2m/junlin.chen/ckpts/k2v3-7B_iso_attn_shared_small_phase2_sft_38n_2251790/huggingface/checkpoint_0002500_k2aurora_bf16_safetensors",
    "source_model_type": "k2_aurora",
    "target_model_type": "k2_horizon",
    "validation_basis": "all safetensors headers and index",
    "weight_mode": "copy",
    "weights_reencoded": false
  },
  "shards": 36,
  "tensors": 327
}