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)

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

Inference
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
```

desaifan-mbzuai commited on Sep 28

Commit

fa7f5de

verified ·

1 Parent(s): 6fbc54f

Release artifacts updated.

Browse files

Files changed (1) hide show

README.md +5 -4

README.md CHANGED Viewed

@@ -28,8 +28,8 @@ K2-Horizon-0.9B is the compact dense member of the K2-Horizon family: a 0.9B-cla
 - **Compact reasoning model.** A 0.9B-class dense model evaluated across mathematics, coding, science, and tool-use benchmarks.
 - **128K context.** Supports up to 131,072 tokens with YaRN RoPE scaling.
 - **Multi-teacher distillation.** Trained with domain teachers for math and code, STEM, and instruction following.
-<!-- - **Intermediate checkpoints.** Intermediate checkpoints will be released so capability changes can be studied across training rather than at a single checkpoint. -->
-- **Fully open.** Training data/recipe and the training code will be made public.
 ## Benchmark Results
@@ -155,7 +155,7 @@ During RL, training branches into multiple expert models, which are then merged,
 The tables below list the release artifacts for **K2-Horizon-0.9B**, their availability, and the expected release dates for remaining items.
-**Last updated:** 2026-09-11
 **Status:**
@@ -172,7 +172,8 @@ The tables below list the release artifacts for **K2-Horizon-0.9B**, their avail
 | Blog post | [Blog post](https://ifm.ai/blog/k2/) | Available | N/A |
 | Checkpoints | [Checkpoint inventory](#checkpoint-inventory) | Available | N/A |
 | Technical report | Not yet available | In Progress | End of September 2026 |
-| Code repository | [GitHub](https://github.com/ifm-ai/xllm) | In Progress | End of September 2026 |
 ### Checkpoint Inventory

 - **Compact reasoning model.** A 0.9B-class dense model evaluated across mathematics, coding, science, and tool-use benchmarks.
 - **128K context.** Supports up to 131,072 tokens with YaRN RoPE scaling.
 - **Multi-teacher distillation.** Trained with domain teachers for math and code, STEM, and instruction following.
+<!-- - **Intermediate checkpoints.** Intermediate checkpoints are released so capability changes can be studied across training rather than at a single checkpoint. -->
+- **Fully open.** Training data/recipe and the training code are public.
 ## Benchmark Results
 The tables below list the release artifacts for **K2-Horizon-0.9B**, their availability, and the expected release dates for remaining items.
+**Last updated:** 2026-09-28
 **Status:**
 | Blog post | [Blog post](https://ifm.ai/blog/k2/) | Available | N/A |
 | Checkpoints | [Checkpoint inventory](#checkpoint-inventory) | Available | N/A |
 | Technical report | Not yet available | In Progress | End of September 2026 |
+| Code repository | [GitHub](https://github.com/ifm-ai/xllm) | Available | N/A |
+| Data | [Hugging Face](https://huggingface.co/datasets/IFM/TxT360-v2) | Available | N/A |
 ### Checkpoint Inventory