Roy WangandClaude Opus 5.5
b515648641
Signed-off-by: esmeetu <jasonailu87@gmail.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
98 lines
3.3 KiB
YAML
98 lines
3.3 KiB
YAML
meta:
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title: "K2-Horizon-MoVA-36B-A4B"
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slug: "k2-horizon-mova-36b-a4b"
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provider: "IFM"
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description: "Sparse MoVA and MoE long-context model for research and production-style serving experiments"
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date_added: 2026-08-31
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date_updated: 2026-09-03
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tasks: [text]
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performance_headline: "37.44B stored parameters including embeddings / 5.95B active parameters per token"
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related_recipes: ["IFM/K2-Horizon-32B", "IFM/K2-Horizon-375B-A23B"]
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hardware:
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h200: verified
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model:
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model_id: "IFM/K2-Horizon-MoVA-36B-A4B"
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architecture: moe
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parameter_count: "37.44B"
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active_parameters: "5.95B"
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context_length: 524288
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base_args: ["--trust-remote-code", "--chat-template-content-format", "string", "--dtype", "bfloat16", "--enable-expert-parallel"]
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base_env: {}
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features:
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reasoning:
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description: "Extract K2-Horizon reasoning into message.reasoning"
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args: ["--reasoning-parser", "k2_horizon"]
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tool_calling:
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description: "Enable automatic tool choice with the K2-Horizon parser"
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args: ["--enable-auto-tool-choice", "--tool-call-parser", "k2_horizon"]
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variants:
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default:
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precision: bf16
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vram_minimum_gb: 90
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description: "BF16 serving runtime with native vLLM execution"
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compatible_strategies: [single_node_tep, multi_node_tep, multi_node_tp_pp]
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default_strategy: single_node_tep
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hardware_overrides: {}
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strategy_overrides:
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single_node_tep:
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tp: 2
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guide: |
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## Overview
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K2-Horizon-MoVA-36B-A4B is a mixture-of-expert (MoE) model in the IFM K2-Horizon
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family. It combines MoVA attention and MoE layers, with 37.44B stored parameters
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including embeddings, 5.95B active parameters per token, and support for up
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to 524,288 tokens of context.
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K2-Horizon is an open-weight IFM model family built for transparent
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foundation-model research, staged checkpoint analysis, and practical
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deployment. The series spans compact dense models for local experimentation,
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dense mid-size and large models for high-quality research workloads, and
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mixture-of-expert (MoE) models such as K2-Horizon-MoVA-36B-A4B for higher-capacity
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serving.
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## Launch Commands
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```bash
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vllm serve IFM/K2-Horizon-MoVA-36B-A4B \
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--tensor-parallel-size 2 \
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--trust-remote-code \
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--chat-template-content-format string \
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--dtype bfloat16 \
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--enable-expert-parallel \
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--reasoning-parser k2_horizon \
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--enable-auto-tool-choice \
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--tool-call-parser k2_horizon
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```
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## Client usage
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```python
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from openai import OpenAI
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client = OpenAI(
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api_key="EMPTY", base_url="http://localhost:8000/v1", timeout=3600
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)
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resp = client.chat.completions.create(
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model="IFM/K2-Horizon-MoVA-36B-A4B",
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messages=[{"role": "user", "content": "Give me three primes above 100."}],
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temperature=1.0, top_p=0.95, max_tokens=2048,
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)
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print(resp.choices[0].message.content)
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```
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### Thinking modes
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The model supports selectable thinking through `chat_template_kwargs`, per
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request or server-wide via `--default-chat-template-kwargs`:
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- `{"reasoning_effort": "high"}` — full thinking (default).
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- `{"reasoning_effort": "medium"}` — faster thinking.
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- `{"reasoning_effort": "low"}` — fastest thinking.
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## References
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- [IFM/K2-Horizon-MoVA-36B-A4B on Hugging Face](https://huggingface.co/IFM/K2-Horizon-MoVA-36B-A4B)
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- [K2 Horizon Collection](https://huggingface.co/collections/IFM/k2-horizon)
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