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iapp/openthai2.0-qwen3.8-27b-NVFP4
openthai2.0-qwen3.8-27b-NVFP4 is a machine learning model from iapp. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
NVFP4 weight + activation quantisation of iapp/openthai2.0-qwen3.8-27b (v2.0.1) via llm-compressor QuantizationModifier, text-calibrated (ultrachat, 512×2048). Vision tower, MTP draft head and lmhead are kept in bf16.…
Downloads · 30 days
490
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.safetensors20.6 GB · 100%
How the weights are stored.
U824.4B · 89%
From the Hugging Face model README
NVFP4 weight + activation quantisation of
iapp/openthai2.0-qwen3.8-27b (v2.0.1)
via llm-compressor QuantizationModifier, text-calibrated (ultrachat, 512×2048). Vision tower, MTP draft head
and lm_head are kept in bf16. See the base model card for benchmarks and prompts.
Earlier uploads of this repo had no mtp.* tensors while config.json still advertised
mtp_num_hidden_layers: 1. With --speculative-config vLLM ran a draft head with no weights:
every draft was rejected (mean acceptance length 1.00) and decoding was ~1.6× slower than
without the flag. Found by Dr. Panutat Tejasen (ThaiEval-v3). Cause: the quantisation export
loaded the model through a transformers class that has no MTP module, so the head was dropped
on load and the re:.*mtp.* ignore rule matched nothing.
This upload adds model-mtp.safetensors (bf16 head, 0.85 GB), maps it in the index, and adds
re:.*mtp.* to quantization_config.ignore. Verified under vLLM 0.26 with
{"method":"qwen3_5_mtp","num_speculative_tokens":2}: mean acceptance length 1.68 (bf16
production: 1.61). If your copy predates this, re-download or serve without
--speculative-config.
vllm serve iapp/openthai2.0-qwen3.8-27b-NVFP4 \
--max-model-len 32768 --reasoning-parser qwen3 --trust-remote-code \
--speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":2}'