iapp/openthai2.0-qwen3.8-27b-MLX-4bit

🤗 Hugging Face 来源image-text-to-textapache-2.027.4B 参数55 GBsafetensors✓ 5 个校验和今天更新

一条命令提交

在你的模型文件夹旁边运行它。它会制作种子、将文件与 Hugging Face 比对，然后提交。你只需开始做种，并粘贴你账户中的密钥。它只读取你的文件，绝不修改。如果愿意，可以先阅读脚本。

curl -fsSL https://pirateface.co/package.sh | bash -s -- --repo iapp/openthai2.0-qwen3.8-27b-MLX-4bit ./model-folder

OpenThai2.0 - Opensource Thai Knowledge, Document, and Agentic AI (MLX 4-bit)

MLX 4-bit quantization (mlx-vlm) of iapp/openthai2.0-qwen3.8-27b for Apple silicon. Vision included. ~16 GB — runs on 24 GB+ unified memory.

v2.0.3 — rebuilt from the v2.0.3 weights (Thai knowledge-recall fix). The v2.0.0 launch build stays available at revision tag v2.0.0.

Run

pip install mlx-vlm
python -m mlx_vlm generate --model iapp/openthai2.0-qwen3.8-27b-MLX-4bit \
  --image document.jpg --prompt "อ่านข้อความในเอกสารนี้ทั้งหมด" --max-tokens 8192

⚠️ The model reasons before it answers — leave a large generation budget (8k+), or replies may come back empty.

Notes: the MTP draft head is not included (mlx-vlm has no drafter support for this architecture yet). Sanity-verified on-device: Thai factual prompts answered correctly. Full benchmarks and model card: see the main bf16 repo.