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---
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language: [en, zh]
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license: apache-2.0
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library_name: gguf
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base_model: Qwen/Qwen3-4B-Instruct-2507
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tags: [quantized, gguf, mixed-precision, bitclass, qwen3]
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pipeline_tag: text-generation
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---
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# Qwen3-4B-Instruct-2507 — BitClass2 Mixed-Precision GGUF
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Mixed-precision GGUF quantizations of [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
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using **Hessian-informed per-tensor bit allocation**. Each tensor group receives
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the precision level that minimizes quality loss for its measured sensitivity —
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more bits where they matter, fewer where they don't.
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## Available Quantizations
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| File | BPW | Size | PPL ↓ | tok/s | Use Case |
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|---|---|---|---|---|---|
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| [`Qwen3-4B-Instruct-2507-Q8_0.gguf`](./Qwen3-4B-Instruct-2507-Q8_0.gguf) | 8.5 | 4.28 GB | 2.651 | 11.4 | Near-lossless reference |
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| [`Qwen3-4B-Instruct-2507-Q6_K.gguf`](./Qwen3-4B-Instruct-2507-Q6_K.gguf) | 5.8 | 2.93 GB | 2.888 | 13.6 | High quality, moderate size |
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| [`Qwen3-4B-Instruct-2507-Q5_K_M.gguf`](./Qwen3-4B-Instruct-2507-Q5_K_M.gguf) | 5.2 | 2.60 GB | 2.971 | 14.3 | Balanced quality and size |
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| [`Qwen3-4B-Instruct-2507-Q4_K_M.gguf`](./Qwen3-4B-Instruct-2507-Q4_K_M.gguf) | 4.7 | 2.35 GB | 2.978 | 14.1 | Best quality-to-size ratio |
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| [`Qwen3-4B-Instruct-2507-Q3_K_S.gguf`](./Qwen3-4B-Instruct-2507-Q3_K_S.gguf) | 3.2 | 1.62 GB | 3.214 | 18.9 | Maximum compression |
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**Recommended:** Q4_K_M for the best quality-to-size ratio (PPL 2.978 at just 2.35 GB).
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Q3_K_S for maximum compression. Q6_K for high quality.
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## How It Works
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Standard quantization applies one precision level uniformly across all tensors.
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BitClass2 uses **Hessian-based sensitivity analysis** (H_diag = mean(X²) per layer)
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to identify which tensors lose the most quality when quantized, then solves an
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LP-optimal knapsack allocation: minimize Σ(sensitivity × quantization_error)
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subject to total size ≤ target. Sensitive tensors get higher precision,
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insensitive ones get lower precision, at the same total file size.
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Within each suffix group, the fractional BPW planner further varies types
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per-layer using blended imatrix + Hessian scores, so late attention layers
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(most sensitive) get higher precision than middle layers (least sensitive).
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## Key Sensitivity Findings (Qwen3-4B)
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- **Late attention layers (29-35) are most sensitive** — blk.34 k/v score 1.0
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- **down_proj is the most sensitive MLP tensor** — projects back to residual stream
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- **gate_proj/up_proj are least sensitive** — safe to quantize aggressively
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- **K > V for attention weight sensitivity** — k_proj averages 0.66 vs v_proj 0.50
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## Usage
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```bash
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# Download
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huggingface-cli download sh111111111111111/Qwen3-4B-Instruct-2507-BitClass2-GGUF \
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Qwen3-4B-Instruct-2507-Q4_K_M.gguf --local-dir .
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# Chat with llama.cpp
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llama-cli -m Qwen3-4B-Instruct-2507-Q4_K_M.gguf -cnv
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# Serve via API
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llama-server -m Qwen3-4B-Instruct-2507-Q4_K_M.gguf --port 8080
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# Ollama
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ollama run hf.co/sh111111111111111/Qwen3-4B-Instruct-2507-BitClass2-GGUF:Qwen3-4B-Instruct-2507-Q4_K_M.gguf
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```
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## Benchmark Details
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All benchmarks run on NVIDIA GB10 ATOM (128GB unified memory, aarch64).
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llama.cpp commit 406f4e3. PPL via `llama-perplexity` (2 chunks, 851 context).
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tok/s via `llama-bench` (tg128, ngl=999).
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## Disclaimer
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Independent project. Not affiliated with or endorsed by Qwen, Unsloth, ByteShape, Bartowski, or llama.cpp.
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## License
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Apache 2.0, inherited from [Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507).
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