{
  "hf_id": "LiquidAI/LFM2.5-1.2B-JP",
  "meta": {
    "title": "LFM2.5 1.2B JP",
    "slug": "lfm2.5-1.2b-jp",
    "provider": "LiquidAI",
    "description": "Liquid AI's 1.2B Japanese-specialized chat model on the LFM2 hybrid conv+attention backbone, with a 32K context window on a single small GPU.",
    "date_added": "2026-06-25",
    "date_updated": "2026-06-24",
    "tasks": [
      "text"
    ],
    "performance_headline": "Japanese-tuned 1.2B hybrid chat model — single small GPU",
    "related_recipes": [
      "LiquidAI/LFM2.5-1.2B-JP-202606",
      "LiquidAI/LFM2.5-1.2B-Instruct"
    ],
    "hardware": {
      "h100": "verified",
      "h200": "verified",
      "b200": "verified"
    }
  },
  "recommended_command": {
    "hardware": "h200",
    "strategy": "single_node_tp",
    "variant": "default",
    "node_count": 1,
    "deploy_type": "single_node",
    "env": {},
    "docker_image": "vllm/vllm-openai:latest",
    "command": "vllm serve LiquidAI/LFM2.5-1.2B-JP \\\n  --tensor-parallel-size 1",
    "argv": [
      "vllm",
      "serve",
      "LiquidAI/LFM2.5-1.2B-JP",
      "--tensor-parallel-size",
      "1"
    ],
    "docker_command": "docker run --gpus all \\\n  --privileged --ipc=host -p 8000:8000 \\\n  -v ~/.cache/huggingface:/root/.cache/huggingface \\\n  vllm/vllm-openai:latest LiquidAI/LFM2.5-1.2B-JP \\\n  --tensor-parallel-size 1",
    "docker_argv": [
      "docker",
      "run",
      "--gpus",
      "all",
      "--privileged",
      "--ipc=host",
      "-p",
      "8000:8000",
      "-v",
      "~/.cache/huggingface:/root/.cache/huggingface",
      "vllm/vllm-openai:latest",
      "LiquidAI/LFM2.5-1.2B-JP",
      "--tensor-parallel-size",
      "1"
    ],
    "strategy_spec": {
      "name": "single_node_tp",
      "deploy_type": "single_node",
      "display_name": "Tensor Parallel",
      "short_label": "TP",
      "orientation": "latency",
      "description": "Single-node tensor parallel. Splits the model across all local GPUs. TP size is set to the GPU count at deploy time. The simplest multi-GPU strategy — works for all model architectures.\n",
      "hardware_match": {
        "min_gpus": 1,
        "max_gpus": 8,
        "multi_node": false
      },
      "vllm_args": [],
      "parallel_flag": "--tensor-parallel-size"
    },
    "hardware_profile": {
      "brand": "NVIDIA",
      "generation": "hopper",
      "compute_arch": "sm90",
      "display_name": "H200",
      "description": "NVIDIA H200 SXM 141 GB HBM3e · 8-GPU HGX node",
      "gpu_count": 8,
      "vram_gb": 1128,
      "multi_node": false
    },
    "alternatives": {
      "kv_store_centralized_mooncake": "/LiquidAI/LFM2.5-1.2B-JP/strategies/kv_store_centralized_mooncake.json",
      "kv_store_distributed_mooncake": "/LiquidAI/LFM2.5-1.2B-JP/strategies/kv_store_distributed_mooncake.json"
    },
    "by_hardware": {
      "h200": "/LiquidAI/LFM2.5-1.2B-JP/hw/h200.json",
      "h100": "/LiquidAI/LFM2.5-1.2B-JP/hw/h100.json",
      "b200": "/LiquidAI/LFM2.5-1.2B-JP/hw/b200.json",
      "gb200": "/LiquidAI/LFM2.5-1.2B-JP/hw/gb200.json",
      "b300": "/LiquidAI/LFM2.5-1.2B-JP/hw/b300.json",
      "gb300": "/LiquidAI/LFM2.5-1.2B-JP/hw/gb300.json",
      "mi300x": "/LiquidAI/LFM2.5-1.2B-JP/hw/mi300x.json",
      "mi325x": "/LiquidAI/LFM2.5-1.2B-JP/hw/mi325x.json",
      "mi350x": "/LiquidAI/LFM2.5-1.2B-JP/hw/mi350x.json",
      "mi355x": "/LiquidAI/LFM2.5-1.2B-JP/hw/mi355x.json"
    }
  },
  "model": {
    "model_id": "LiquidAI/LFM2.5-1.2B-JP",
    "min_vllm_version": "0.23.0",
    "architecture": "dense",
    "parameter_count": "1.2B",
    "active_parameters": "1.2B",
    "context_length": 32768,
    "base_args": [],
    "base_env": {},
    "install": {
      "pip": {
        "command": "uv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto"
      },
      "docker": {
        "command": "docker pull vllm/vllm-openai:latest"
      }
    }
  },
  "variants": {
    "default": {
      "precision": "bf16",
      "vram_minimum_gb": 3,
      "description": "Full BF16 — single 8 GB+ NVIDIA GPU with room for KV cache"
    }
  },
  "hardware_overrides": {
    "blackwell": {
      "extra_env": {
        "VLLM_USE_OINK_OPS": "1"
      }
    }
  },
  "guide": "## Overview\n\n[LFM2.5-1.2B-JP](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP) is the Japanese-specialized\n1.2B chat model from [Liquid AI](https://www.liquid.ai/), built on the **LFM2 hybrid backbone**\n(short-range gated convolution blocks interleaved with grouped-query attention). It serves on a\nsingle small GPU through vLLM's OpenAI-compatible API. For the latest Japanese checkpoint with\ntool calling, see\n[LFM2.5-1.2B-JP-202606](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP-202606).\n\n### Key Features\n- **Japanese-specialized**: Tuned for Japanese chat and instruction following.\n- **Hybrid backbone**: Gated short convolutions interleaved with grouped-query attention — a smaller KV cache and lower decode latency than a same-size full-attention transformer.\n- **32K context**: 32,768-token context window.\n- **Native vLLM support**: Served via the `Lfm2ForCausalLM` architecture.\n\n### Supported Variants\n\nDense:\n- `LiquidAI/LFM2.5-230M` (230M)\n- `LiquidAI/LFM2.5-350M` (350M)\n- `LiquidAI/LFM2.5-1.2B-Instruct` (1.2B)\n- `LiquidAI/LFM2.5-1.2B-Thinking` (1.2B, reasoning)\n- `LiquidAI/LFM2.5-1.2B-JP` / `LiquidAI/LFM2.5-1.2B-JP-202606` (Japanese)\n- `LiquidAI/LFM2.5-1.2B-Base` (pretrained base)\n\nMoE:\n- `LiquidAI/LFM2.5-8B-A1B` (8B total / ~1B active)\n\nVision-Language:\n- `LiquidAI/LFM2.5-VL-450M`, `LiquidAI/LFM2.5-VL-1.6B`\n\nSee the [LFM2.5 usage guide](../../LiquidAI/LFM2.5) for the full family.\n\n## Prerequisites\n\n- **Hardware**: 1× GPU with ≥8 GB VRAM.\n- **vLLM**: ≥ 0.23.0.\n\n### pip (NVIDIA CUDA)\n```bash\nuv venv\nsource .venv/bin/activate\nuv pip install -U vllm --torch-backend auto\n```\n\n## Deployment Configurations\n\n### Quick Start (Single GPU, BF16)\n```bash\nvllm serve LiquidAI/LFM2.5-1.2B-JP\n```\n\n### Docker (NVIDIA)\n```bash\ndocker run -itd --name lfm2.5-jp \\\n  --ipc=host --network host --shm-size 16G --gpus all \\\n  -v ~/.cache/huggingface:/root/.cache/huggingface \\\n  vllm/vllm-openai:latest \\\n    --model LiquidAI/LFM2.5-1.2B-JP \\\n    --host 0.0.0.0 --port 8000\n```\n\n## Client Usage\n\n### Text Generation\nThe card recommends temperature `0.3`, `min_p 0.15`, `repetition_penalty 1.05` for the JP\ncheckpoint (`min_p` and `repetition_penalty` ride in `extra_body`).\n```python\nfrom openai import OpenAI\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"EMPTY\")\nresponse = client.chat.completions.create(\n    model=\"LiquidAI/LFM2.5-1.2B-JP\",\n    messages=[{\"role\": \"user\", \"content\": \"日本の四季について簡単に説明してください。\"}],\n    temperature=0.3,\n    extra_body={\"min_p\": 0.15, \"repetition_penalty\": 1.05},\n)\nprint(response.choices[0].message.content)\n```\n\n## Configuration Tips\n- Set `--max-model-len` to match your workload (up to 32K); lowering it frees VRAM for KV cache.\n- `--gpu-memory-utilization 0.90–0.95` maximizes KV cache capacity.\n- Sampling presets are per-request client defaults — don't bake them into `vllm serve`.\n\n## References\n- [Model card](https://huggingface.co/LiquidAI/LFM2.5-1.2B-JP)\n- [LFM2.5 usage guide](../../LiquidAI/LFM2.5)\n- [Liquid AI](https://www.liquid.ai/)\n- [vLLM supported models](https://docs.vllm.ai/en/latest/models/supported_models.html)\n",
  "source": {
    "source": "vllm",
    "repo": "vllm-project/vllm",
    "ref": "main",
    "sha": "389ffebe555705c85337025d5df108d0384d365e",
    "path": "recipes/models/LiquidAI/LFM2.5-1.2B-JP.yaml",
    "edit_url": "https://github.com/vllm-project/vllm/edit/main/recipes/models/LiquidAI/LFM2.5-1.2B-JP.yaml",
    "overlays": []
  }
}