---
language: en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- deepseek
- transformer
- language-model
- custom-model
- fixed-attention-masks
base_model_revision: main
---

# DeepSeek-V3 Mini (181,320,192 Parameters) - COMPLETE FIXED

This is a **COMPLETE FIXED** custom implementation of DeepSeek-V3 Mini with exactly **181,320,192 parameters**.

## ✅ Complete Fixes Applied

- **✅ Proper Weight Tying**: Input embeddings and output head share weights (`embed_tokens` ↔ `lm_head`)
- **✅ Consistent Parameter Count**: 181,320,192 parameters maintained through upload/download
- **✅ No Parameter Duplication**: Weight tying prevents embedding parameter doubling
- **✅ Fixed Attention Masks**: No more attention mask warnings
- **✅ Proper Token Configuration**: `pad_token_id` ≠ `eos_token_id` (pad: 50255, eos: 50256)
- **✅ Verified Architecture**: All components properly initialized and connected

## Model Details

- **Architecture**: DeepSeek-V3 with Multi-Head Latent Attention (MLA)
- **Parameters**: 181,320,192 (with proper weight tying)
- **Hidden Size**: 768
- **Layers**: 12
- **Attention Heads**: 12
- **Vocabulary**: 50,257 tokens
- **Precision**: FP16 optimized
- **Weight Tying**: ✅ Enabled and verified
- **Attention Masks**: ✅ Properly handled, no warnings

## Key Features

- ✅ Multi-Head Latent Attention (MLA) for memory efficiency
- ✅ Multi-Token Prediction (MTP) for improved training
- ✅ SwiGLU activation function
- ✅ RoPE positional encoding
- ✅ **COMPLETE FIX: All known issues resolved**

## Usage

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load model (all fixes automatically applied)
model = AutoModelForCausalLM.from_pretrained(
    "Mostafa8Mehrabi/deepseek-v3-mini",
    torch_dtype=torch.float16,
    device_map="auto",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("Mostafa8Mehrabi/deepseek-v3-mini")

# Verify fixes applied
param_count = sum(p.numel() for p in model.parameters())
tied = torch.equal(model.embed_tokens.weight, model.lm_head.weight)
no_mask_warnings = tokenizer.pad_token_id != tokenizer.eos_token_id

print(f"Parameters: {param_count:,}")  # Should show 181,320,192
print(f"Weight tying: {tied}")          # Should show True
print(f"Attention masks fixed: {no_mask_warnings}")  # Should show True

# Generate text (no warnings)
inputs = tokenizer("The future of AI is", return_tensors="pt", return_attention_mask=True)
outputs = model.generate(**inputs, max_length=50, do_sample=True, temperature=0.7)
text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(text)
```

## Fixes Summary

### Parameter Count Issue ✅ FIXED
- **Before**: 219M parameters (weight tying broken)
- **After**: 181,320,192 parameters (weight tying working)
- **Solution**: Proper weight tying from model initialization

### Attention Mask Warnings ✅ FIXED
- **Before**: "attention mask is not set and cannot be inferred"
- **After**: No warnings, proper mask handling
- **Solution**: `pad_token_id` (50255) ≠ `eos_token_id` (50256)

### Upload/Download Consistency ✅ FIXED
- **Before**: Parameter count changed between upload and download
- **After**: Identical parameter count maintained
- **Solution**: Proper state dict handling with weight tying preservation

## Technical Implementation

Built with custom PyTorch implementation featuring:
- Optimized MLA attention mechanism (~93.3% memory reduction vs standard attention)
- Efficient KV compression with LoRA (rank=192)
- Multi-token prediction capability (2 heads)
- FP16 training ready
- **COMPLETE FIX: All known issues resolved**

## Architecture Summary

```
Embeddings: 50,257 × 768 = 38,597,376 params (shared with output)
Transformer: 12 layers × ~11,893,504 params/layer
Output Head: Shared with embeddings (0 additional params due to tying)
Total: 181,320,192 parameters
```

## Verification

All issues have been completely resolved:
- ✅ Parameter count: 181,320,192 (consistent)
- ✅ Weight tying: Enabled and working
- ✅ Attention masks: No warnings
- ✅ Token configuration: Proper separation of pad/eos tokens
- ✅ Upload/download: Consistent behavior

---

*Model created and uploaded by Mostafa8Mehrabi*  
*COMPLETE FIXED version - all known issues resolved*