Text Generation
Transformers
Safetensors
English
Chinese
k2_horizon
k2-horizon
0.9b
dense
reasoning
knowledge-distillation
ifm
conversational
custom_code
Instructions to use IFM/K2-Horizon-0.9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| { | |
| "aurora_commit": "e47c5555caa987ded29dba0624d3de1b34cf6162", | |
| "aurora_repo": "/mnt/weka/home/mrunner/workspace/code/eval360-v2/runtime/eval-runtime/releases/20260825T235203Z-e47c5555caa9-646960/repo/vendor/xllm-dataloader1", | |
| "completed_utc": "2026-09-02T21:20:07Z", | |
| "converter": "/mnt/weka/home/mrunner/workspace/code/tools/conversion_aurora2horizon/convert_k2aurora_to_k2horizon.py", | |
| "horizon_commit": "42ae4f88016b083a8f514655d93521d6af006de9", | |
| "horizon_repo": "/mnt/weka/home/mrunner/workspace/code/eval360-v2/runtime/eval-runtime/releases/20260825T235203Z-e47c5555caa9-646960/repo/vendor/xllm-dataloader1-horizon", | |
| "output_checkpoint": "/mnt/weka/home/mrunner/workspace/checkpoints/k2horizon_bf16_safetensors/xllm_mopd_v1_a10b05_3t_mathcode_stem_if_train500_lr1e7_opd01_refkl001_v1/checkpoints/checkpoint_0000249", | |
| "schema_version": 1, | |
| "source_checkpoint": "/mnt/weka/shrd/k2m/junlin.chen/xllm_1b_final/model", | |
| "source_hashes": { | |
| "config.json": "f0ebfa59d5569edf77de34e32739b43978a8f1da355fefd825d6ed6284143870", | |
| "configuration_k2_aurora.py": "90efba266ef94d3959e7f490e56146da48225e4539cca87ed6fcfea77872ce6d", | |
| "model.safetensors.index.json": "5652ee0f725133f74a7a2cfb2673845230cf89cfc7d480cd64b90211db53e19e", | |
| "modeling_k2_aurora.py": "aa6e3ea27c90eca3a88b7fcfaa0aa5235c6ac22e45e33ea4f0a613dc07821924" | |
| }, | |
| "source_model_type": "k2_aurora", | |
| "target_hashes": { | |
| "config.json": "0ba8f6a0fe8daa5003f88c335735cabc7dba20600ace939efab949ae5e59b936", | |
| "configuration_k2_horizon.py": "5c2f993c1053d9462ebea6dea416c897fddfbb4a5edd904e486936b20d4badc5", | |
| "model.safetensors.index.json": "5652ee0f725133f74a7a2cfb2673845230cf89cfc7d480cd64b90211db53e19e", | |
| "modeling_k2_horizon.py": "fb09e010956bd51cfa7d4055b4381cff34c9e06164066b49e3546f38b2e6242f" | |
| }, | |
| "target_model_type": "k2_horizon", | |
| "weight_mode": "copy", | |
| "weights": { | |
| "dtypes": [ | |
| "BF16" | |
| ], | |
| "filenames": [ | |
| "model-00000-of-00001.safetensors" | |
| ], | |
| "index_sha256": "5652ee0f725133f74a7a2cfb2673845230cf89cfc7d480cd64b90211db53e19e", | |
| "logical_bytes": 2156571648, | |
| "physical_bytes": 2156600968, | |
| "shards": 1, | |
| "tensors": 255 | |
| }, | |
| "weights_reencoded": false | |
| } | |