Initial commit: E4B-MarkBase model integration with passing tests
CI / build-and-test (push) Has been cancelled
CI / build-and-test (push) Has been cancelled
- E4B-MarkBase model (42 layers, 4.4GB) loaded successfully - All Phase 1-6 tests passed (model loading, forward pass, vision/audio towers, token generation, performance) - All stress tests passed (5/5 in 127.6s) - Concurrent inference - Memory stress (67.5 tok/s, 0 NaN) - Continuous generation - Batch processing - Long-running stability - Swift Metal inference engine with multimodal support
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MarkBase Admin
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# 26B-A4B Model Source Analysis
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**Date**: 2026-06-23
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**Purpose**: Trace origin of problematic 26B-A4B model
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---
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## Model Sources Comparison
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### 26B-A4B (Problematic)
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**Origin**: HuggingFace MLX Community
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- **Repository**: `mlx-community/gemma-4-26b-a4b-it-4bit`
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- **Base Model**: `google/gemma-4-26b-a4b-it` (Google official)
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- **Converter**: `mlx-vlm` version 0.4.3
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- **Framework**: MLX (Apple's ML framework)
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- **Library**: mlx
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- **License**: Apache 2.0 (Gemma license)
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**Quantization Config**:
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```json
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{
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"group_size": 64,
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"bits": 4,
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"mode": "affine",
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"mixed_precision": true // Some layers use INT8
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}
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```
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**File Format**:
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- Sharded: model-00001-of-00003.safetensors (4.9GB)
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- Sharded: model-00002-of-00003.safetensors (4.9GB)
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- Sharded: model-00003-of-00003.safetensors (4.7GB)
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- Total: 14.5GB
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**Creation Date**: 19 Jun 10:20 (downloaded to local)
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---
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### 26B-Standard (Correct)
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**Origin**: Unknown (possibly custom quantization)
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- **No README.md** (no HuggingFace metadata)
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- **Config**: Simple JSON (no mlx-vlm metadata)
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- **Quant Method**: "custom"
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**Quantization Config**:
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```json
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{
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"bits": 4,
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"group_size": 32,
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"quant_method": "custom"
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}
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```
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**File Format**:
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- Single file: model.safetensors (15.6GB)
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**Creation Date**: 19 Jun 08:28 (downloaded/quantized locally)
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---
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## Key Differences
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| Aspect | 26B-A4B | 26B-Standard |
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|--------|---------|--------------|
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| **Source** | HuggingFace MLX | Unknown/Custom |
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| **Converter** | mlx-vlm 0.4.3 | Custom script? |
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| **Group Size** | 64 | 32 |
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| **Quant Mode** | affine | custom |
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| **Scales Range** | ±0.01 ✗ | ~120 ✓ |
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| **Scales Sign** | Negative ✗ | Positive ✓ |
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| **File Size** | 14.5GB (sharded) | 15.6GB (single) |
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| **Layers** | 30 | 30 |
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| **Experts** | 128 | 128 |
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---
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## Problem Root Cause
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### MLX Quantization Bug (mlx-vlm 0.4.3)
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**Symptoms**:
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1. Scales too small (±0.01 instead of ~120)
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2. Negative scales (invalid for affine quantization)
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3. Result: 98% tokens produce NaN
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**Evidence**:
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- 26B-Standard (custom quant): scales correct ~120 ✓
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- 26B-A4B (mlx-vlm 0.4.3): scales wrong ±0.01 ✗
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**Hypothesis**:
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- mlx-vlm 0.4.3 has bug in affine quantization
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- Generates wrong scales magnitude
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- Missing normalization or wrong formula
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---
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## MLX Affine Quantization Theory
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### Formula (Expected)
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```
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weight = (int4_value - zero_point) * scale + bias
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```
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**Correct Implementation**:
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- scale = (weight_max - weight_min) / 15 (range for INT4)
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- zero_point = intermediate value
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- bias = weight_min
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**Expected scales**:
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- For typical weights: scale ≈ 50-200
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- For group_size=64: similar range
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**26B-A4B scales**:
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- scale ≈ 0.01 (100x too small)
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- Negative values (invalid)
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- Bug in mlx-vlm quantization logic
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---
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## MLX-vlm Version Analysis
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### mlx-vlm 0.4.3 (Used for 26B-A4B)
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- Release date: Unknown (need check HuggingFace)
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- Known issues: Quantization bugs?
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- Affine mode: Problematic?
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### Alternative Versions
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- mlx-vlm latest: May have fixes
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- Custom quantization: More control
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---
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## Recommended Actions
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### 1. Check MLX-vlm Issues
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**Search**:
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- HuggingFace mlx-community repo issues
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- GitHub mlx-vlm issues for "affine quantization"
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- Look for scales bug reports
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### 2. Re-quantize with Fixed Script
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**If MLX-vlm fixed**:
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- Download latest mlx-vlm
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- Re-quantize from `google/gemma-4-26b-a4b-it`
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- Verify scales range (~120)
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**If custom script**:
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- Use same method as 26B-Standard
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- group_size=32, custom quant
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- Manual scales verification
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### 3. Report Issue
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**To MLX Community**:
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- HuggingFace: mlx-community/gemma-4-26b-a4b-it-4bit
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- GitHub: mlx-vlm issue tracker
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- Describe: scales too small + negative values
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- Evidence: scales sample comparison
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---
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## Model Card Information
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### Google Gemma-4-26B-A4B-IT
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**Official Model** (pre-quantized):
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- **Publisher**: Google
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- **License**: Gemma license (Apache-style)
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- **Architecture**: MoE (Mixture of Experts)
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- **Layers**: 30
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- **Experts**: 128 per layer
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- **Parameters**: ~26B (active params)
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- **Special**: A4B variant (Audio-Aware)
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**HuggingFace**: `google/gemma-4-26b-a4b-it`
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- BF16 weights (original)
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- Used as base for MLX conversion
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---
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## Alternative: Google Gemma-4-27B-IT
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**26B-Standard equivalent**:
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- **Architecture**: MoE, 30 layers, 128 experts
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- **Parameters**: ~27B (similar to 26B-A4B)
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- **License**: Same Gemma license
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- **Status**: Available in BF16
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**If 26B-Standard is Gemma-4-27B-IT**:
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- Same architecture family
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- Custom quantization (group_size=32)
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- Correct scales ✓
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---
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## Conclusion
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**26B-A4B problem traced to MLX-vlm 0.4.3 quantization bug**
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- **Source**: `mlx-community/gemma-4-26b-a4b-it-4bit`
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- **Converter**: mlx-vlm 0.4.3 (buggy)
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- **Result**: Wrong scales magnitude + negative values
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- **Solution**: Use 26B-Standard (custom quant, correct scales)
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---
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## Next Steps
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1. **Check HuggingFace**:
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- `mlx-community/gemma-4-26b-a4b-it-4bit` issues
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- Look for reports of quantization bugs
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2. **Check GitHub**:
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- `mlx-vlm` repository issues
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- Search "affine quantization" problems
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3. **Test MLX-vlm latest**:
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- Download newer version if available
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- Test quantization on small model
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4. **Report Issue**:
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- Provide scales sample evidence
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- Compare with custom quant (26B-Standard)
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---
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## Files
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### A4B Model Files
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```
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/Users/accusys/MarkBaseEngine/models/gemma-4-26b-a4b-it-4bit/
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README.md: MLX metadata
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config.json: quantization config (group_size=64, affine)
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model-00001-of-00003.safetensors (4.9GB)
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model-00002-of-00003.safetensors (4.9GB)
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model-00003-of-00003.safetensors (4.7GB)
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```
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### Standard Model Files
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```
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/Users/accusys/MarkBaseEngine/models/gemma-4-26b-standard/
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config.json: quantization config (group_size=32, custom)
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model.safetensors (15.6GB)
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No README (custom origin)
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```
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---
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**End of Source Analysis**
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