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<span style="background: #007aff; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">GGUF</span>
<span style="background: #af52de; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">Heretic</span>
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<h1 style="margin: 0 0 8px 0; font-size: 32px; font-weight: 700; color: #1d1d1f; letter-spacing: -0.5px; border: none; position: relative; z-index: 1;">QwenPaw-Flash-9B-heretic</h1>
<p style="margin: 8px 0 0 0; font-size: 14px; position: relative; z-index: 1;"><span style="color: #86868b;">English</span> | <a href="https://huggingface.co/SC117/QwenPaw-Flash-9B-heretic/blob/main/README_zh.md" style="color: #007aff; text-decoration: none;">📖 中文文档</a></p>
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F32 safetensors of **QwenPaw-Flash-9B-heretic**, a 9B dense model fine-tuned with Heretic methodology on Qwen3.5-9B.
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- **Base model**: Qwen3.5-9B
- **Precision**: F32 (float32 safetensors)
- **Parameters**: ~9B
- **Shards**: model-00001 ~ model-00008 (8 files, F32 main weights)
- **Additional**: model-00009 (BF16, Multi-Token Prediction head extracted from Qwen3.5-9B)
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<div style="background: linear-gradient(135deg, #ff6b35 0%, #f7931e 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>⚡</span> MTP (Multi-Token Prediction)</div>
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`model-00009-of-00009.safetensors` contains the MTP head weights extracted from Qwen3.5-9B. MTP enables the model to predict multiple future tokens in a single forward pass, improving generation speed via speculative decoding.
- MTP acceptance rate: ~43%
- Speedup: ~1.5-1.9x decode throughput
For MTP-enabled GGUF inference, see the MTP GGUF repo below.
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For inference with llama.cpp / Ollama / LM Studio, use the GGUF versions:
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<div style="background: linear-gradient(135deg, #ff6b35 0%, #f7931e 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>🚀</span> Usage</div>
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```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained(
"SC117/QwenPaw-Flash-9B-heretic",
torch_dtype=torch.float32,
device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained("SC117/QwenPaw-Flash-9B-heretic")
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Same as base model (Qwen3.5-9B).
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