meta:
  title: "Jais-3-3.7B"
  slug: "jais-3-3.7b"
  provider: "IFM"
  description: "Dense model for long-context research, serving, and downstream adaptation"
  date_added: 2026-10-05
  date_updated: 2026-10-05
  tasks: [text]
  performance_headline: "5.06B stored parameters including embeddings, with up to 512K context"
  related_recipes: ["IFM/Jais-3-0.9B", "IFM/Jais-3-7B"]
  hardware:
    h200: verified
model:
  model_id: "IFM/Jais-3-3.7B"
  architecture: dense
  parameter_count: "5.06B"
  active_parameters: "5.06B"
  context_length: 524288
  base_args: ["--trust-remote-code", "--chat-template-content-format", "string", "--dtype", "bfloat16"]
  base_env: {}
features:
  reasoning:
    description: "Extract Jais-3 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: 13
    description: "BF16 serving runtime with a 131K context baseline"
compatible_strategies: [single_node_tp, multi_node_tp, multi_node_tp_pp]
hardware_overrides: {}
guide: |
  ## Overview

  Jais-3-3.7B is a small dense model in the IFM Jais-3 family,
  with 5.06B stored parameters including embeddings and support for up to
  524,288 tokens of context.

  Jais-3 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 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

  ```bash
  vllm serve IFM/Jais-3-3.7B \
    --trust-remote-code \
    --chat-template-content-format string \
    --dtype bfloat16 \
    --reasoning-parser k2_horizon \
    --enable-auto-tool-choice \
    --tool-call-parser k2_horizon
  ```

  ## 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/Jais-3-3.7B",
      messages=[{"role": "user", "content": "لدينا عددان مجموعهما 50 وحاصل ضربهما 600. ما هما العددان؟ اشرح خطوات الحل."}],
      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": "low"}` — no thinking.

  ## References

  - [IFM/Jais-3-3.7B on Hugging Face](https://huggingface.co/IFM/Jais-3-3.7B)
  - [Jais 3 Collection](https://huggingface.co/collections/IFM/jais-3)