Text Generation
Transformers
Safetensors
English
k2_horizon
k2-horizon
7b
dense
open-weights
ifm
conversational
custom_code
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
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