Add model card README

README.md

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+---
+license: apache-2.0
+base_model: Qwen/Qwen3.6-27B
+tags:
+  - safetensors
+  - qwen3_5
+  - 4-bit
+  - auto-round
+  - quantized
+pipeline_tag: image-text-to-text
+---
+
+# Qwen3.6-27B-int4-AutoRound
+
+This is an Int4 [AutoRound](https://github.com/intel/auto-round) quantization of [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B), produced using [spark-auto-round](https://github.com/whpthomas/spark-auto-round).
+
+## Quantization Details
+
+| Parameter | Value |
+|---|---|
+| **Original Model** | [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B) |
+| **Quantization Method** | AutoRound (W4A16, symmetric) |
+| **Bits** | 4 |
+| **Group Size** | 128 |
+| **Calibration Dataset** | [opencode-instruct](https://huggingface.co/nvidia/OpenCodeInstruct) |
+| **Calibration Samples** | 512 |
+| **Calibration Sequence Length** | 2048 |
+| **Tuning Iterations** | 1000 |
+| **Batch Size** | 8 |
+| **Packing Format** | `auto_round:auto_gptq` |
+| **AutoRound Version** | 0.14.2 |
+| **Model Size** | ~19 GB |
+
+### Layers Kept in FP16
+
+The `linear_attn.in_proj_a` and `linear_attn.in_proj_b` projections across all DeltaNet layers, as well as `mtp.fc`, are kept at FP16 precision for quality preservation.
+
+## Quantization Report
+
+All 64 transformer blocks passed sensitivity analysis (63 PASS, 1 WARN at layer 58).
+
+| Layer Range | Cosine Similarity | PSNR (dB) |
+|---|---|---|
+| Layers 0-10 | 0.9999 - 1.0000 | 80.7 - 84.0 |
+| Layers 11-20 | 0.9995 - 0.9999 | 74.9 - 81.5 |
+| Layers 21-30 | 0.9988 - 0.9995 | 73.6 - 78.7 |
+| Layers 31-40 | 0.9976 - 0.9986 | 69.4 - 73.2 |
+| Layers 41-50 | 0.9943 - 0.9976 | 60.2 - 69.2 |
+| Layers 51-63 | 0.9883 - 0.9934 | 53.4 - 66.5 |
+
+Full per-layer reports are available in the repository: [`quantization-report.txt`](quantization-report.txt) and [`quantization-report.csv`](quantization-report.csv).
+
+### Memory Usage During Quantization
+
+- Peak RAM: 36.13 GB
+- Peak VRAM: 43.13 GB
+
+## How to Use
+
+### With vLLM
+
+```bash
+vllm serve coder3101/Qwen3.6-27B-int4-AutoRound
+```
+
+### With Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "coder3101/Qwen3.6-27B-int4-AutoRound"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+
+prompt = "Explain the theory of relativity in simple terms."
+messages = [{"role": "user", "content": prompt}]
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Acknowledgments
+
+- Quantization performed using [spark-auto-round](https://github.com/whpthomas/spark-auto-round) by [@whpthomas](https://github.com/whpthomas)
+- Based on [AutoRound](https://github.com/intel/auto-round) by Intel
+
+---
+
+# Original Model Card -- Qwen3.6-27B
+
+> Below is the original model card from [Qwen/Qwen3.6-27B](https://huggingface.co/Qwen/Qwen3.6-27B).
+
+## Qwen3.6-27B
+
+### Highlights
+
+Qwen3.6-27B follows the Qwen3.5 series with key upgrades:
+
+- **Agentic Coding**: Handles frontend workflows and repo-level reasoning with greater fluency.
+- **Thinking Preservation**: New option to retain reasoning context from historical messages, reducing overhead in iterative development.
+
+### Model Architecture
+
+| Property | Value |
+|---|---|
+| Type | Causal Language Model with Vision Encoder |
+| Parameters | 27B |
+| Hidden Dimension | 5120 |
+| Token Embedding | 248320 (Padded) |
+| Number of Layers | 64 |
+| Hidden Layout | `16 x (3 x (Gated DeltaNet -> FFN) -> 1 x (Gated Attention -> FFN))` |
+| FFN Intermediate Dimension | 17408 |
+| Context Length | 262,144 (natively), up to 1,010,000 with YaRN |
+
+**Gated DeltaNet**: 48 linear attention heads for V, 16 for QK (head dim: 128)
+**Gated Attention**: 24 heads for Q, 4 for KV (head dim: 256, RoPE dim: 64)
+
+### Benchmark Results -- Language
+
+| Benchmark | Qwen3.5-27B | Qwen3.5-397B-A17B | Gemma4-31B | Claude 4.5 Opus | Qwen3.6-35B-A3B | **Qwen3.6-27B** |
+|---|---|---|---|---|---|---|
+| SWE-bench Verified | 75.0 | 76.2 | 52.0 | 80.9 | 73.4 | **77.2** |
+| SWE-bench Pro | 51.2 | 50.9 | 35.7 | 57.1 | 49.5 | **53.5** |
+| SWE-bench Multilingual | 69.3 | 69.3 | 51.7 | 77.5 | 67.2 | **71.3** |
+| Terminal-Bench 2.0 | 41.6 | 52.5 | 42.9 | 59.3 | 51.5 | **59.3** |
+| SkillsBench Avg5 | 27.2 | 30.0 | 23.6 | 45.3 | 28.7 | **48.2** |
+| QwenWebBench | 1068 | 1186 | 1197 | 1536 | 1397 | **1487** |
+| NL2Repo | 27.3 | 32.2 | 15.5 | 43.2 | 29.4 | **36.2** |
+| Claw-Eval Avg | 64.3 | 70.7 | 48.5 | 76.6 | 68.7 | **72.4** |
+| Claw-Eval Pass^3 | 46.2 | 48.1 | 25.0 | 59.6 | 50.0 | **60.6** |
+| QwenClawBench | 52.2 | 51.8 | 41.7 | 52.3 | 52.6 | **53.4** |
+| MMLU-Pro | 86.1 | 87.8 | 85.2 | 89.5 | 85.2 | **86.2** |
+| MMLU-Redux | 93.2 | 94.9 | 93.7 | 95.6 | 93.3 | **93.5** |
+| SuperGPQA | 65.6 | 70.4 | 65.7 | 70.6 | 64.7 | **66.0** |
+| C-Eval | 90.5 | 93.0 | 82.6 | 92.2 | 90.0 | **91.4** |
+| GPQA Diamond | 85.5 | 88.4 | 84.3 | 87.0 | 86.0 | **87.8** |
+| HLE | 24.3 | 28.7 | 19.5 | 30.8 | 21.4 | **24.0** |
+| LiveCodeBench v6 | 80.7 | 83.6 | 80.0 | 84.8 | 80.4 | **83.9** |
+| HMMT Feb 25 | 92.0 | 94.8 | 88.7 | 92.9 | 90.7 | **93.8** |
+| HMMT Nov 25 | 89.8 | 92.7 | 87.5 | 93.3 | 89.1 | **90.7** |
+| HMMT Feb 26 | 84.3 | 87.9 | 77.2 | 85.3 | 83.6 | **84.3** |
+| IMOAnswerBench | 79.9 | 80.9 | 74.5 | 84.0 | 78.9 | **80.8** |
+| AIME26 | 92.6 | 93.3 | 89.2 | 95.1 | 92.7 | **94.1** |
+
+### Benchmark Results -- Vision Language
+
+| Benchmark | Qwen3.5-27B | Qwen3.5-397B-A17B | Gemma4-31B | Claude 4.5 Opus | Qwen3.6-35B-A3B | **Qwen3.6-27B** |
+|---|---|---|---|---|---|---|
+| MMMU | 82.3 | 85.0 | 80.4 | 80.7 | 81.7 | **82.9** |
+| MMMU-Pro | 75.0 | 79.0 | 76.9 | 70.6 | 75.3 | **75.8** |
+| MathVista mini | 87.8 | -- | 79.3 | -- | 86.4 | **87.4** |
+| DynaMath | 87.7 | 86.3 | 79.5 | 79.7 | 82.8 | **85.6** |
+| VlmsAreBlind | 96.9 | -- | 87.2 | -- | 96.6 | **97.0** |
+| RealWorldQA | 83.7 | 83.9 | 72.3 | 77.0 | 85.3 | **84.1** |
+| MMStar | 81.0 | 83.8 | 77.3 | 73.2 | 80.7 | **81.4** |
+| MMBenchEN-DEV-v1.1 | 92.6 | -- | 90.9 | -- | 92.8 | **92.3** |
+| SimpleVQA | 56.0 | 67.1 | 52.9 | 65.7 | 58.9 | **56.1** |
+| CharXiv RQ | 79.5 | 80.8 | 67.9 | 68.5 | 78.0 | **78.4** |
+| CC-OCR | 81.0 | 82.0 | 75.7 | 76.9 | 81.9 | **81.2** |
+| OCRBench | 89.4 | -- | 86.1 | -- | 90.0 | **89.4** |
+| ERQA | 60.5 | 67.5 | 57.5 | 46.8 | 61.8 | **62.5** |
+| CountBench | 97.8 | 97.2 | 96.1 | 90.6 | 96.1 | **97.8** |
+| RefCOCO avg | 90.9 | 92.3 | -- | -- | 92.0 | **92.5** |
+| EmbSpatialBench | 84.5 | -- | -- | -- | 84.3 | **84.6** |
+| RefSpatialBench | 67.7 | -- | 4.7 | -- | 64.3 | **70.0** |
+| VideoMME (w sub.) | 87.0 | 87.5 | -- | 77.7 | 86.6 | **87.7** |
+| VideoMMMU | 82.3 | 84.7 | 81.6 | 84.4 | 83.7 | **84.4** |
+| MLVU | 85.9 | 86.7 | -- | 81.7 | 86.2 | **86.6** |
+| MVBench | 74.6 | 77.6 | -- | 67.2 | 74.6 | **75.5** |
+| V* | 93.7 | 95.8 | -- | 67.0 | 90.1 | **94.7** |
+| AndroidWorld | 64.2 | -- | -- | -- | -- | **70.3** |
+
+### Serving Frameworks
+
+- **SGLang** (>=0.5.10)
+- **vLLM** (>=0.19.0)
+- **KTransformers**
+- **HuggingFace Transformers**
+
+### Sampling Parameters
+
+| Mode | Temperature | top_p | top_k | min_p | presence_penalty |
+|---|---|---|---|---|---|
+| Thinking (general) | 1.0 | 0.95 | 20 | 0.0 | 0.0 |
+| Thinking (precise coding/WebDev) | 0.6 | 0.95 | 20 | -- | -- |
+| Non-thinking / Instruct | 0.7 | 0.80 | 20 | -- | 1.5 |
+
+### Key Features
+
+- **Thinking mode** is on by default; can be disabled via `enable_thinking: False`.
+- Does **not** support soft switch (`/think` and `/nothink` from Qwen3).
+- **Preserve Thinking**: `preserve_thinking: True` retains reasoning traces from history.
+- Supports text, image, and video inputs.
+- Multi-Token Prediction (MTP) supported.
+- Native context length: 262,144 tokens; extensible to 1,010,000 tokens with YaRN RoPE scaling.
+
+### Citation
+
+```bibtex
+@misc{qwen3.6-27b,
+    title  = {{Qwen3.6-27B}: Flagship-Level Coding in a {27B} Dense Model},
+    author = {{Qwen Team}},
+    month  = {April},
+    year   = {2026},
+    url    = {https://qwen.ai/blog?id=qwen3.6-27b}
+}
+```