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
```

aaryamonvikram commited on 3 days ago

Commit

ee770e7

verified ·

1 Parent(s): b2a2931

Update README.md

Browse files

Files changed (1) hide show

README.md +10 -12

README.md CHANGED Viewed

@@ -3,18 +3,18 @@ pipeline_tag: text-generation
 library_name: transformers
 model_name: K2-Horizon-0.9B
 language:
-  - en
-  - zh
-license: other
 license_name: internal-only
 license_link: LICENSE
 tags:
-  - k2-horizon
-  - 0.9b
-  - dense
-  - reasoning
-  - knowledge-distillation
-  - ifm
 ---
 # K2-Horizon-0.9B
@@ -140,6 +140,4 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
   year   = {2026},
   url    = {https://ifm.ai/blog/k2/},
 }
-```

 library_name: transformers
 model_name: K2-Horizon-0.9B
 language:
+- en
+- zh
+license: apache-2.0
 license_name: internal-only
 license_link: LICENSE
 tags:
+- k2-horizon
+- 0.9b
+- dense
+- reasoning
+- knowledge-distillation
+- ifm
 ---
 # K2-Horizon-0.9B
   year   = {2026},
   url    = {https://ifm.ai/blog/k2/},
 }
+```