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 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
diff --git a/README.md b/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..557302c8a1a0e8bc7384feb24bc7ea4388b1e9cc
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+++ b/README.md
@@ -0,0 +1,182 @@
+---
+license: mit
+language:
+  - en
+  - zh
+base_model: zai-org/GLM-4.7-Flash
+pipeline_tag: text-generation
+tags:
+  - quantized
+  - Mixture of Experts
+  - 4-bit
+  - GPTQ
+  - MMFP4
+  - glm
+  - metal-marlin
+  - moe
+library_name: transformers
+arxiv: "2508.06471"
+---
+
+# GLM-4.7-Flash-Marlin-MMFP4
+
+![](https://raw.githubusercontent.com/zai-org/GLM-4.5/refs/heads/main/resources/logo.svg)
+
+**MMFP4-quantized GLM-4.7-Flash** — a 30B-A3B MoE model compressed to **4 bits per weight** using GPTQ with actorder and Metal Marlin's E2M1 FP4 format.
+
+| Metric | Value |
+|--------|-------|
+| **Effective bits** | 4.0 bpw |
+| **Compression** | 4× vs FP16 |
+| **Model size** | ~16 GB (vs ~60 GB FP16) |
+| **Parameters** | 29.3B |
+| **Format** | HuggingFace sharded safetensors |
+
+## Model Description
+
+This is a quantized version of [zai-org/GLM-4.7-Flash](https://huggingface.co/zai-org/GLM-4.7-Flash), the strongest model in the 30B class that balances performance and efficiency.
+
+GLM-4.7-Flash features:
+
+- **30B-A3B MoE architecture** (64 experts + shared expert, 2-4 active per token)
+- **Multi-head Latent Attention (MLA)** for 8× KV cache compression
+- **State-of-the-art reasoning** (91.6% on AIME 2025, 59.2% on SWE-bench Verified)
+- **Bilingual** (English + Chinese)
+
+## Quantization Details
+
+Quantized using **MR-GPTQ** (Metal Marlin GPTQ) with CUDA acceleration:
+
+### Method
+
+- **Format**: MMFP4 (E2M1 FP4) — Metal Marlin's native FP4 format
+- **Quantization**: GPTQ with actorder (activation-order column permutation)
+- **Hessian calibration**: Pre-computed Hessians for attention layers
+- **Expert quantization**: Identity Hessian with actorder (no calibration data for MoE experts)
+- **Group size**: 128
+- **Hardware**: NVIDIA RTX 3090 Ti (CUDA-accelerated Cholesky factorization)
+
+### Quantization Statistics
+
+| Component | Bit Width | Notes |
+|-----------|-----------|-------|
+| Embeddings | FP16 | Full precision |
+| LM Head | FP16 | Full precision |
+| Attention (q/k/v/o) | 4-bit | GPTQ with Hessians |
+| MoE Experts (64×) | 4-bit | GPTQ with actorder |
+| Layer Norms | FP16 | Full precision |
+| Router Weights | FP16 | Full precision |
+
+- **Total tensors**: 19,066
+- **Shards**: 48 safetensors files
+- **Quantization time**: ~20 minutes (RTX 3090 Ti)
+
+## Files
+
+```
+GLM-4.7-Flash-Marlin-MMFP4/
+├── model-00001-of-00048.safetensors   # Layer 0 (embeddings)
+├── model-00002-of-00048.safetensors   # Layer 1
+├── ...
+├── model-00048-of-00048.safetensors   # Layer 47 + lm_head
+├── model.safetensors.index.json       # Weight map
+├── config.json                        # Model config
+├── generation_config.json
+├── tokenizer.json                     # Tokenizer
+└── tokenizer_config.json
+```
+
+## Usage
+
+### With Metal Marlin (Apple Silicon)
+
+```python
+from metal_marlin import MarlinForCausalLM
+from transformers import AutoTokenizer
+
+model = MarlinForCausalLM.from_pretrained(
+    "RESMP-DEV/GLM-4.7-Flash-Marlin-MMFP4",
+    device="mps"
+)
+tokenizer = AutoTokenizer.from_pretrained("zai-org/GLM-4.7-Flash")
+
+prompt = "<|user|>\nExplain quantum computing in simple terms.\n<|assistant|>\n"
+input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to("mps")
+output = model.generate(input_ids, max_new_tokens=256, temperature=0.7)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+### Tensor Format
+
+Each quantized weight tensor has corresponding scale factors:
+
+- `{name}.weight`: Packed FP4 weights (uint8)
+- `{name}.scales`: FP16 per-group scales (group_size=128)
+
+## Hardware Requirements
+
+| Device | Memory | Notes |
+|--------|--------|-------|
+| Apple M4 Max | 36 GB+ | Via Metal Marlin |
+| Apple M2 Ultra | 64 GB+ | Via Metal Marlin |
+| NVIDIA RTX 3090 | 24 GB | With offloading |
+| NVIDIA RTX 4090 | 24 GB | Native |
+
+## Benchmarks
+
+### Original Model Performance (from Z.AI)
+
+| Benchmark | GLM-4.7-Flash | Qwen3-30B-A3B | GPT-OSS-20B |
+|-----------|---------------|---------------|-------------|
+| AIME 2025 | **91.6** | 85.0 | 91.7 |
+| GPQA | **75.2** | 73.4 | 71.5 |
+| SWE-bench Verified | **59.2** | 22.0 | 34.0 |
+| τ²-Bench | **79.5** | 49.0 | 47.7 |
+| BrowseComp | **42.8** | 2.29 | 28.3 |
+
+### Quantized Model Notes
+
+- GPTQ with actorder minimizes quality loss vs RTN
+- Expected degradation: ~1-2% on benchmarks vs FP16
+- E2M1 FP4 format optimized for Metal Performance Shaders
+
+## Comparison with Trellis Quant
+
+| Model | Format | Size | Bits | Method |
+|-------|--------|------|------|--------|
+| [GLM-4.7-Flash-Trellis-MM](https://huggingface.co/RESMP-DEV/GLM-4.7-Flash-Trellis-MM) | Trellis | 14 GB | 3.78 bpw | EXL3-style mixed precision |
+| **This model** | MMFP4 | 16 GB | 4.0 bpw | GPTQ + actorder |
+
+Choose **Trellis** for smaller size, **MMFP4** for simpler tensor format and potentially better compatibility.
+
+## Limitations
+
+- **Metal Marlin required** for optimal inference on Apple Silicon
+- **No speculative decoding** yet
+- **Quality loss**: ~1-2% on benchmarks vs FP16 (typical for 4-bit quantization)
+
+## Credits
+
+- **Original model**: [Z.AI / GLM Team](https://huggingface.co/zai-org/GLM-4.7-Flash)
+- **Quantization method**: GPTQ with actorder
+- **Quantization toolkit**: [Metal Marlin](https://github.com/RESMP-DEV/metal-marlin)
+
+## Citation
+
+If you use this model, please cite the original GLM-4.5 paper:
+
+```bibtex
+@misc{glm2025glm45,
+      title={GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models}, 
+      author={GLM Team and Aohan Zeng and Xin Lv and others},
+      year={2025},
+      eprint={2508.06471},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL},
+      url={https://arxiv.org/abs/2508.06471}, 
+}
+```
+
+## License
+
+This quantized model inherits the **MIT License** from the original GLM-4.7-Flash model.
diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..6637ccbc226eb78776af6a66b6558ce6421be20e
--- /dev/null
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+{
+  "architectures": [
+    "Glm4MoeLiteForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "pad_token_id": 154820,
+  "eos_token_id": [
+    154820,
+    154827,
+    154829
+  ],
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "intermediate_size": 10240,
+  "max_position_embeddings": 202752,
+  "model_type": "glm4_moe_lite",
+  "moe_intermediate_size": 1536,
+  "topk_method": "noaux_tc",
+  "norm_topk_prob": true,
+  "num_attention_heads": 20,
+  "n_group": 1,
+  "topk_group": 1,
+  "n_routed_experts": 64,
+  "n_shared_experts": 1,
+  "routed_scaling_factor": 1.8,
+  "num_experts_per_tok": 4,
+  "first_k_dense_replace": 1,
+  "num_hidden_layers": 47,
+  "num_key_value_heads": 20,
+  "num_nextn_predict_layers": 1,
+  "partial_rotary_factor": 1.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "dtype": "bfloat16",
+  "transformers_version": "5.0.0rc0",
+  "q_lora_rank": 768,
+  "kv_lora_rank": 512,
+  "qk_nope_head_dim": 192,
+  "qk_rope_head_dim": 64,
+  "v_head_dim": 256,
+  "vocab_size": 154880
+}
diff --git a/generation_config.json b/generation_config.json
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+{
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+  "eos_token_id": [
+    154820,
+    154827,
+    154829
+  ],
+  "pad_token_id": 154820,
+  "temperature": 1.0,
+  "transformers_version": "5.0.0.dev0"
+}
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+}
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diff --git a/tokenizer.json b/tokenizer.json
new file mode 100644
index 0000000000000000000000000000000000000000..aba40197a4cdb5607f4ab7a05fb0a4ee8054fd6d
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d
+size 20217442
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..f6d3074564f6dd3a9431bd43b968cef999d58c54
--- /dev/null
+++ b/tokenizer_config.json
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