iapp/openthai2.0-qwen3.8-27b-GGUF

🤗 Hugging Face 来源apache-2.0激活 27B287 GBGGUF✓ 8 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-GGUF ./model-folder
需要做种者 →

OpenThai2.0 - Opensource Thai Knowledge, Document, and Agentic AI (GGUF)

GGUF quantizations of openthai2.0-qwen3.8-27b (v9) for llama.cpp. Includes the MTP head (exported as nextn layers) and the vision projector.

file size use
openthai2.0-qwen3.8-27b-Q4_K_M.gguf ~17 GB recommended balance
openthai2.0-qwen3.8-27b-IQ2_M.gguf ~9.8 GB recommended 2-bit — imatrix-calibrated (Thai+EN); passes factual sanity checks that plain Q2_K fails
openthai2.0-qwen3.8-27b-Q2_K.gguf ~11 GB plain 2-bit — ⚠️ factual slips observed; prefer IQ2_M
openthai2.0-qwen3.8-27b-Q8_0.gguf ~29 GB near-lossless
mmproj-openthai2.0-qwen3.8-27b-F16.gguf — vision projector (documents/images)

Run

llama-server -m openthai2.0-qwen3.8-27b-Q4_K_M.gguf       --mmproj mmproj-openthai2.0-qwen3.8-27b-F16.gguf -c 32768

⚠️ The model reasons before it answers. Use a large context (32k) and leave max_tokens unset or >= 8192, or replies may come back empty.

Sanity-verified: Q4_K_M (CPU) and IQ2_M (GPU) answer Thai factual prompts correctly; imatrix-a80.dat is included for community re-quants. Full benchmarks and model card: see the main bf16 repo.