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IFM
/
K2-Horizon-7B

Text Generation
Transformers
Safetensors
English
k2_horizon
k2-horizon
7b
dense
open-weights
ifm
conversational
custom_code
Model card Files Files and versions
xet
Community
7

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

  • Libraries
  • Transformers

    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)
    # pip install -U transformers accelerate
    # 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
New discussion
Resources
  • PR & discussions documentation
  • Code of Conduct
  • Hub documentation

Support OpenAI-style text content lists

#7 opened 1 day ago by
hanseungwook

repo size is 18B and seems like 9B model not 7B

1
#6 opened 1 day ago by
mysterious-pie

Fix: add the missing @capture_outputs decorator (output_hidden_states is None) Fixes the issue reported in discussion #6. `K2HorizonModel.forward` carries `@auto_docstring` but not `@capture_outputs`, so `output_hidden_states=True` returns `None` (any tool that reads the residual stream per layer fails). Adding the decorator plus the import makes `outputs.hidden_states` a tuple of len `num_hidden_layers + 1`; greedy decoding is unchanged. Verified on 0.9B: 29 states (28 layers + 1), `[0]` == embeddings and `[-1]` == `last_hidden_state`; generation byte-identical with and without the decorator. The change is the same for every full-model repo in the family (the file is byte-identical across sizes).

#5 opened 4 days ago by
Avicennasis

I wish you delivered what you say you delivered

👀 2
9
#3 opened about 1 month ago by
NezTheNaughty
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