meta:
  title: "K2-Horizon-0.9B"
  slug: "k2-horizon-0.9b"
  provider: "IFM"
  description: "Compact dense model for mathematics, code, instruction following, and STEM tasks"
  date_added: 2026-08-31
  date_updated: 2026-08-31
  tasks:
    - text
  performance_headline: "1.08B stored parameters including embeddings, with 131K context and native K2-Horizon parsing"
  related_recipes:
    - "IFM/K2-Horizon-3.7B"
  hardware:
    h200: verified
model:
  model_id: "IFM/K2-Horizon-0.9B"
  architecture: dense
  parameter_count: "1.08B"
  active_parameters: "1.08B"
  context_length: 131072
  base_args:
    - "--trust-remote-code"
    - "--chat-template-content-format"
    - "string"
    - "--dtype"
    - "bfloat16"
    - "--max-model-len"
    - "131072"
    - "--gpu-memory-utilization"
    - "0.85"
  base_env: {}
features:
  reasoning:
    description: "Extract K2-Horizon reasoning into message.reasoning"
    args: ["--reasoning-parser", "k2_horizon"]
  tool_calling:
    description: "Enable automatic tool choice with the K2-Horizon parser"
    args: ["--enable-auto-tool-choice", "--tool-call-parser", "k2_horizon"]
variants:
  default:
    precision: bf16
    vram_minimum_gb: 3
    description: "Released BF16 checkpoint; the command starts at an 8K runtime context"
compatible_strategies:
  - single_node_tp
  - multi_node_tp
  - multi_node_tp_pp
hardware_overrides: {}
guide: |
  ## Overview

  K2-Horizon-0.9B is a compact dense model in the IFM K2-Horizon
  family, with 1.08B stored parameters including embeddings and a 131,072-token
  context window.

  K2-Horizon is an open-weight IFM model family built for transparent
  foundation-model research, staged checkpoint analysis, and practical
  deployment. The series spans compact dense models such as K2-Horizon-0.9B
  for local experimentation, dense mid-size and large models for high-quality
  research workloads, and mixture-of-expert (MoE) models for higher-capacity
  serving.

  ## Launch Commands

  K2-Horizon-0.9B's `config.json` already enables YaRN, so the full 131,072-token context needs no RoPE override.

  ```bash
  vllm serve IFM/K2-Horizon-0.9B \
    --trust-remote-code \
    --chat-template-content-format string \
    --dtype bfloat16 \
    --max-model-len 131072 \
    --gpu-memory-utilization 0.85 \
    --reasoning-parser k2_horizon \
    --enable-auto-tool-choice \
    --tool-call-parser k2_horizon
  ```

  Lower `--max-model-len` (e.g. 8192) to reduce KV-cache memory.

  ## Client usage

  ```python
  from openai import OpenAI

  client = OpenAI(
      api_key="EMPTY", base_url="http://localhost:8000/v1", timeout=3600
  )

  resp = client.chat.completions.create(
      model="IFM/K2-Horizon-0.9B",
      messages=[{"role": "user", "content": "Give me three primes above 100."}],
      temperature=1.0, top_p=0.95, max_tokens=2048,
  )
  print(resp.choices[0].message.content)
  ```

  ### Thinking modes

  The model supports selectable thinking through `chat_template_kwargs`, per
  request or server-wide via `--default-chat-template-kwargs`:

  - `{"reasoning_effort": "high"}` — full thinking (default).
  - `{"reasoning_effort": "medium"}` — faster thinking.
  - `{"reasoning_effort": "low"}` — fastest thinking.

  ## References

  - [IFM/K2-Horizon-0.9B on Hugging Face](https://huggingface.co/IFM/K2-Horizon-0.9B)
  - [K2 Horizon Collection](https://huggingface.co/collections/IFM/k2-horizon)