Text Generation
Transformers
Safetensors
English
k2_horizon
decision-model
jev
classification
calibration
pointer-head
conversational
custom_code
Instructions to use IFM/K2-Type-0.9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use IFM/K2-Type-0.9B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="IFM/K2-Type-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-Type-0.9B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use IFM/K2-Type-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-Type-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-Type-0.9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/IFM/K2-Type-0.9B
- SGLang
How to use IFM/K2-Type-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-Type-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-Type-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-Type-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-Type-0.9B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use IFM/K2-Type-0.9B with Docker Model Runner:
docker model run hf.co/IFM/K2-Type-0.9B
Download decision_config.json from IFM/K2-Type-0.9B: direct link, hf CLI and curl.
- Browser
- Download file 492 Bytes
-
https://huggingface.co/IFM/K2-Type-0.9B/resolve/main/decision_config.json
- Command line
-
hf download hf://IFM/K2-Type-0.9B/decision_config.json
-
curl -L -o decision_config.json https://huggingface.co/IFM/K2-Type-0.9B/resolve/main/decision_config.json
492 Bytes
| { | |
| "name": "k2-type-0.9b", | |
| "base_model": "IFM/K2-Horizon-0.9B", | |
| "temperature": 1.4778441535263076, | |
| "head_dim": 256, | |
| "marker_tokens": { | |
| "state": "reserved_special_token_100", | |
| "q": "reserved_special_token_101", | |
| "opt": "reserved_special_token_102", | |
| "end_opt": "reserved_special_token_103", | |
| "decide": "reserved_special_token_104" | |
| }, | |
| "max_len": 8192, | |
| "max_options": 255, | |
| "question_types": [ | |
| "noul", | |
| "choice", | |
| "score" | |
| ], | |
| "api": "POST /v1/systemone (TypeSafe-compatible)" | |
| } |