# trustformers-models TODO List

## Overview

The `trustformers-models` crate provides implementations of 21+ transformer architectures,
covering encoder-only, decoder-only, encoder-decoder, vision, multimodal, and state-space models.
All models are built on top of trustformers-core abstractions and follow consistent patterns
for configuration, weight loading, and forward passes.

**Key Responsibilities:**
- Model architecture implementations (BERT, GPT-2, LLaMA, Mistral, T5, ViT, CLIP, Mamba, etc.)
- Task-specific heads (CausalLM, SequenceClassification, QuestionAnswering, etc.)
- Weight loading from HuggingFace format (SafeTensors, PyTorch, JSON)
- Model configuration management
- Generation methods (greedy, sampling, beam search)
- Integration with trustformers-core layers

---

## Current Status

### Implementation Status
✅ **PRODUCTION-READY** - All major model architectures implemented
✅ **COMPREHENSIVE MODEL ZOO** - 21+ models covering all major categories
✅ **ZERO COMPILATION ERRORS** - Clean compilation across workspace
✅ **COMPLETE WEIGHT LOADING** - 19/21 models with full weight loading (CLIP/some multimodal have placeholders)
✅ **HUGGINGFACE COMPATIBLE** - Weight format compatibility with HF Transformers

### Model Categories
- **Encoder Models:** 6 models (BERT family)
- **Decoder Models:** 10 models (GPT family + modern LLMs)
- **Encoder-Decoder Models:** 2 models (T5, BART)
- **Vision & Multimodal:** 7 models (ViT, CLIP, LLaVA, etc.)
- **State-Space & Linear Attention:** 5 models (S4, Mamba, RWKV, RetNet, Hyena)
- **Specialized Models:** 7+ models (code, math, quantum, etc.)

---

## Completed Model Implementations

### Encoder Models (BERT Family)

#### BERT (Bidirectional Encoder Representations from Transformers)
- ✅ **Architecture**
  - Bidirectional self-attention
  - Absolute position embeddings (learned, max 512 tokens)
  - Segment embeddings for sentence pairs
  - [CLS] token for classification, [SEP] for sentence separation

- ✅ **Variants**
  - BERT-base: 12 layers, 768 hidden, 12 heads (110M params)
  - BERT-large: 24 layers, 1024 hidden, 16 heads (340M params)

- ✅ **Weight Loading**
  - Complete weight loading from HuggingFace
  - SafeTensors, PyTorch, JSON formats
  - Automatic model variant detection

- ✅ **Task Heads**
  - Sequence classification (sentiment analysis, text classification)
  - Token classification (NER, POS tagging)
  - Question answering (SQuAD-style)
  - Masked language modeling

#### RoBERTa (Robustly Optimized BERT)
- ✅ **Architecture**
  - Same as BERT but trained differently
  - No Next Sentence Prediction (NSP) loss
  - Dynamic masking during training
  - Byte-level BPE tokenization

- ✅ **Improvements**
  - Larger batch sizes (8K vs 256)
  - More training data
  - Longer training time
  - Better performance on downstream tasks

#### ALBERT (A Lite BERT)
- ✅ **Architecture**
  - Factorized embedding parameterization
  - Cross-layer parameter sharing (weight sharing across layers)
  - Sentence-order prediction (SOP) instead of NSP

- ✅ **Memory Efficiency**
  - Embedding factorization: V×H → V×E + E×H (V=vocab, H=hidden, E=embedding)
  - Parameter sharing reduces model size by ~80%
  - Still maintains competitive performance

#### DeBERTa (Decoding-enhanced BERT with disentangled attention)
- ✅ **Architecture**
  - Disentangled attention mechanism
  - Enhanced mask decoder
  - Relative position encodings
  - Better position awareness

- ✅ **Innovations**
  - Content and position are represented separately
  - Attention weights consider both content-to-content and relative positions
  - Virtual adversarial training for improved robustness

#### DistilBERT (Distilled BERT)
- ✅ **Architecture**
  - 6-layer student network (vs 12-layer BERT-base)
  - Same hidden size as BERT-base (768)
  - Knowledge distillation from BERT-base teacher

- ✅ **Performance**
  - 40% smaller model size
  - 60% faster inference
  - Retains 97% of BERT's performance on GLUE

#### ELECTRA (Efficiently Learning an Encoder)
- ✅ **Architecture**
  - Replaced token detection (RTD) pretraining
  - Generator-discriminator setup
  - More compute-efficient than MLM

- ✅ **Pretraining**
  - Generator creates plausible alternatives
  - Discriminator detects replaced tokens
  - More efficient than masked language modeling

---

### Decoder Models (GPT Family & Modern LLMs)

#### GPT-2 (Generative Pre-trained Transformer 2)
- ✅ **Architecture**
  - Causal self-attention (attend only to past)
  - Learned absolute position embeddings (max 1024 tokens)
  - Layer normalization before each sub-layer
  - Byte-level BPE tokenization (50,257 vocab)

- ✅ **Variants**
  - Small: 12 layers, 768 hidden, 12 heads (124M params)
  - Medium: 24 layers, 1024 hidden, 16 heads (355M params)
  - Large: 36 layers, 1280 hidden, 20 heads (774M params)
  - XL: 48 layers, 1600 hidden, 25 heads (1.5B params)

- ✅ **Generation Methods**
  - Greedy decoding
  - Top-k sampling (k=50 default)
  - Top-p (nucleus) sampling (p=0.9 default)
  - Temperature scaling
  - Beam search (beam_size=5 default)

#### GPT-Neo (EleutherAI)
- ✅ **Architecture**
  - Mix of local and global attention layers
  - Rotary position embeddings (RoPE) option
  - Similar to GPT-3 architecture

- ✅ **Variants**
  - 125M: 12 layers, 768 hidden
  - 1.3B: 24 layers, 2048 hidden
  - 2.7B: 32 layers, 2560 hidden

- ✅ **Attention Pattern**
  - Alternating local (window=256) and global attention
  - More efficient than full attention for long sequences

#### GPT-J (EleutherAI, 6B)
- ✅ **Architecture**
  - Rotary position embeddings (RoPE)
  - Parallel attention and FFN (not sequential)
  - Dense attention across full sequence
  - No alternating patterns

- ✅ **Performance**
  - 6B parameters
  - Competitive with GPT-3 (6.7B) on many tasks
  - Open-weights model

#### LLaMA (Large Language Model Meta AI)
- ✅ **Architecture**
  - RoPE (Rotary Position Embeddings) for position info
  - RMSNorm instead of LayerNorm (no bias, no centering)
  - SwiGLU activation in FFN (gated linear unit with Swish)
  - Pre-normalization (normalize before attention/FFN)

- ✅ **Variants**
  - 7B: 32 layers, 4096 hidden, 32 heads
  - 13B: 40 layers, 5120 hidden, 40 heads
  - 30B: 60 layers, 6656 hidden, 52 heads
  - 65B: 80 layers, 8192 hidden, 64 heads

- ✅ **Weight Loading**
  - Complete weight loading infrastructure
  - Embeddings, attention projections (Q, K, V, O)
  - MLP weights (gate, up, down projections)
  - Layer norms and final normalization
  - Location: `src/llama/model.rs:501-564`

#### Mistral (7B with Innovations)
- ✅ **Architecture**
  - Sliding window attention (window size 4096)
  - Grouped-query attention (GQA) for efficiency
  - RoPE with theta=10000
  - Byte-fallback BPE tokenizer (32,000 vocab)

- ✅ **Sliding Window Attention**
  - Each token attends to 4096 previous tokens
  - Reduces memory and computation
  - Maintains long-range dependencies via layering

- ✅ **Weight Loading**
  - Q/K/V/O projection weights fully loaded
  - Gate/up/down MLP weights complete
  - Location: `src/mistral/model.rs:341-408`

#### Gemma (Google, Lightweight LLM)
- ✅ **Architecture**
  - Multi-query attention (MQA)
  - GeGLU activation (GELU variant of gated linear units)
  - RMSNorm normalization
  - RoPE position embeddings

- ✅ **Variants**
  - 2B: Efficient for edge devices
  - 7B: Balanced performance/efficiency

- ✅ **Weight Loading**
  - Full component support
  - Enhanced error handling
  - Location: `src/gemma/model.rs:404-462`

#### Qwen (Alibaba, Multilingual)
- ✅ **Architecture**
  - Multilingual pretraining (Chinese, English, and more)
  - Extended context length support
  - Multiple size variants
  - Instruction-tuned versions available

- ✅ **Weight Loading**
  - Multi-format support (SafeTensors, PyTorch, JSON)
  - Advanced architecture component handling
  - Location: `src/qwen/model.rs:399-464`

#### Phi-3 (Microsoft, Small Language Model)
- ✅ **Architecture**
  - High performance at small scale (3.8B params)
  - Efficient architecture
  - Specialized high-quality training data
  - Long context support (128K tokens)

- ✅ **Design Philosophy**
  - Data quality over quantity
  - Curriculum learning
  - Synthetic data generation

#### Falcon (Technology Innovation Institute)
- ✅ **Architecture**
  - Multi-query attention
  - RoPE positional encodings
  - Parallel attention and FFN
  - Efficient architecture

- ✅ **Weight Loading**
  - QKV weight splitting (combined QKV → separate Q, K, V)
  - Architecture-specific optimizations
  - Tensor manipulation for optimal memory usage
  - Location: `src/falcon/model.rs:485-574`

#### StableLM (Stability AI)
- ✅ **Architecture**
  - Multiple variants (base, zephyr, code)
  - 1.6B to 12B parameter range
  - Grouped-query attention
  - RoPE with partial rotary factor

- ✅ **Variants**
  - Base: General-purpose language model
  - Zephyr: Instruction-tuned for chat
  - Code: Specialized for code generation

- ✅ **Weight Loading**
  - Embeddings, attention projections, MLP weights loaded
  - LM head properly loaded
  - Location: `src/stablelm/model.rs:547-604`

---

### Encoder-Decoder Models

#### T5 (Text-to-Text Transfer Transformer)
- ✅ **Architecture**
  - Encoder-decoder architecture
  - Relative position bias (learned biases for relative positions)
  - Shared embedding for encoder and decoder
  - SentencePiece tokenization

- ✅ **Variants**
  - Small: 6 layers enc+dec, 512 hidden (60M params)
  - Base: 12 layers enc+dec, 768 hidden (220M params)
  - Large: 24 layers enc+dec, 1024 hidden (770M params)
  - 3B: 24 layers enc+dec, 2048 hidden
  - 11B (XXL): 24 layers enc+dec, 4096 hidden

- ✅ **Text-to-Text Format**
  - All tasks as text-to-text (translation, summarization, QA, etc.)
  - Task prefix: "translate English to German:", "summarize:", etc.
  - Unified framework for all NLP tasks

#### BART (Bidirectional and Auto-Regressive Transformers)
- ✅ **Architecture**
  - Encoder-decoder architecture
  - Denoising autoencoder pretraining
  - Standard transformer architecture

- ✅ **Pretraining**
  - Token masking, deletion, infilling
  - Sentence permutation
  - Document rotation

---

### Vision & Multimodal Models

#### Vision Transformer (ViT)
- ✅ **Architecture**
  - Patch embeddings (split image into 16×16 or 32×32 patches)
  - Position embeddings for spatial information
  - [CLS] token for image classification
  - Standard transformer encoder

- ✅ **Variants**
  - Tiny: 12 layers, 192 hidden
  - Small: 12 layers, 384 hidden
  - Base: 12 layers, 768 hidden (86M params)
  - Large: 24 layers, 1024 hidden (307M params)
  - Huge: 32 layers, 1280 hidden (632M params)

- ✅ **Image Processing**
  - Patch extraction and flattening
  - Linear projection to embedding dimension
  - Classification head

#### CLIP (Contrastive Language-Image Pre-training)
- ✅ **Architecture**
  - Dual encoder: Text encoder + Vision encoder
  - Contrastive learning objective
  - Zero-shot image classification
  - Text-image similarity scoring

- ✅ **Components**
  - Text encoder: Transformer (similar to GPT-2)
  - Vision encoder: ViT or ResNet
  - Projection heads for embedding alignment

- ⚠️ **Weight Loading Status**
  - Placeholder implementation (logit_scale only)
  - Text encoder weight loading requires additional work
  - Vision encoder weight loading requires additional work
  - Location: `src/clip/model.rs:546-656`

#### CogVLM (Visual Language Model)
- ✅ **Architecture**
  - Temporal encoder for video understanding
  - Multi-frame attention mechanisms
  - Vision-language alignment
  - Supports both image and video inputs

#### BLIP-2 (Bootstrap Language-Image Pre-training v2)
- ✅ **Architecture**
  - Querying Transformer (Q-Former)
  - Vision-language alignment
  - Frozen vision and language models
  - Efficient training with lightweight Q-Former

#### LLaVA (Large Language and Vision Assistant)
- ✅ **Architecture**
  - Vision encoder (CLIP ViT) + LLM (LLaMA/Vicuna)
  - Visual instruction tuning
  - Multi-modal conversation capability
  - Simple linear projection for vision-language connection

#### DALL-E (Text-to-Image Generation)
- ✅ **Architecture**
  - VQ-VAE for image tokenization
  - Autoregressive generation of image tokens
  - Discrete codebook (8192 codes)
  - Transformer decoder for sequence modeling

#### Flamingo (Visual Language Model)
- ✅ **Architecture**
  - Perceiver Resampler for vision features
  - Cross-attention between vision and language
  - Few-shot learning capability
  - Interleaved image-text inputs

---

### State-Space & Linear Attention Models

#### S4 (Structured State Space)
- ✅ **Architecture**
  - HiPPO initialization (LEGS, LEGT, LAGT, Fourier methods)
  - Efficient long-range dependencies
  - O(N log N) complexity with FFT convolution
  - Diagonal plus low-rank (DPLR) structure

- ✅ **State Space Formulation**
  - Continuous-time: dx/dt = Ax + Bu, y = Cx + Du
  - Discretization with bilinear transform
  - Efficient parallel and recurrent modes

#### Mamba (Selective State-Space Model)
- ✅ **Architecture**
  - Selective scan mechanism (data-dependent parameters)
  - Linear time complexity O(N)
  - Hardware-efficient implementation
  - Superior long-context performance

- ✅ **Innovations**
  - Selection mechanism for filtering relevant info
  - Hardware-aware algorithm design
  - Efficient GPU implementation

#### RWKV (Receptance Weighted Key Value)
- ✅ **Architecture**
  - Linear attention mechanism
  - Recurrent and parallelizable modes
  - O(N) time and O(1) space for inference
  - Time-mixing and channel-mixing blocks

- ✅ **Efficiency**
  - No quadratic attention complexity
  - RNN-like inference (O(1) per step)
  - Transformer-like training (parallel)

#### RetNet (Retention Mechanism)
- ✅ **Architecture**
  - Multi-scale retention replacing attention
  - O(N) inference complexity
  - Chunk-based parallel processing
  - Recurrent, parallel, and chunkwise-recurrent modes

- ✅ **Retention Mechanism**
  - Exponential decay for temporal information
  - Group normalization for stability
  - Multi-head retention for expressiveness

#### Hyena (Implicit Long Convolutions)
- ✅ **Architecture**
  - Subquadratic complexity O(N log N)
  - FlashFFT integration for efficiency
  - Long-context optimization (up to 1M tokens)
  - Data-controlled implicit filters

- ✅ **Implicit Filters**
  - Parameterized by position MLP
  - Long convolution kernels
  - Efficient FFT-based computation

---

## Weight Loading Infrastructure

### HuggingFace Format Support
- ✅ **SafeTensors** - Rust-native safe tensor format
- ✅ **PyTorch** - Pickle-based checkpoint format
- ✅ **JSON** - Configuration files (config.json)
- ✅ **Automatic Detection** - Smart format selection

### Weight Loading Features
- ✅ **Intelligent Tensor Mapping** - Automatic name mapping (HF → TrustformeRS)
- ✅ **Component Recognition** - Identify embeddings, attention, FFN, etc.
- ✅ **Lazy Loading** - On-demand weight loading
- ✅ **Memory-Mapped Loading** - Zero-copy for large models
- ✅ **Error Handling** - Graceful degradation on missing/corrupted weights
- ✅ **Caching** - Tensor caching for performance

### Per-Model Weight Loading Status
- ✅ **Complete (19/21):** BERT, RoBERTa, ALBERT, DeBERTa, DistilBERT, ELECTRA, GPT-2, GPT-Neo, GPT-J, LLaMA, Mistral, Gemma, Qwen, Phi-3, Falcon, StableLM, T5, BART, ViT
- ⚠️ **Partial (1/21):** CLIP (logit_scale only, encoders need work)
- ⚠️ **Limited (1/21):** Some multimodal models (complex architecture)

---

## Model Features

### Common Components
- ✅ Multi-head attention (MHA, GQA, MQA)
- ✅ Flash Attention integration
- ✅ RoPE, absolute, and relative position embeddings
- ✅ LayerNorm and RMSNorm
- ✅ Dropout with training/inference modes
- ✅ Feed-forward networks (FFN, SwiGLU, GeGLU)
- ✅ Residual connections
- ✅ Causal and padding attention masks

### Task-Specific Heads
- ✅ **CausalLM** - Next token prediction (GPT-2, LLaMA, etc.)
- ✅ **MaskedLM** - Masked language modeling (BERT)
- ✅ **SequenceClassification** - Text classification
- ✅ **TokenClassification** - NER, POS tagging
- ✅ **QuestionAnswering** - Extractive QA (SQuAD-style)
- ✅ **ImageClassification** - Vision models

### Generation Support
- ✅ **Greedy Decoding** - argmax selection
- ✅ **Beam Search** - Multiple hypothesis tracking
- ✅ **Sampling Methods**
  - Temperature scaling
  - Top-k sampling (k most likely tokens)
  - Top-p (nucleus) sampling (cumulative probability threshold)
  - Min-p sampling
- ✅ **Advanced Generation**
  - Constrained generation (force specific tokens/patterns)
  - Guided generation with CFG (classifier-free guidance)
  - Speculative decoding (draft-and-verify)
  - Assisted generation
- ✅ **Streaming Generation** - Token-by-token output

---

## Code Organization

### Module Structure
```
trustformers-models/src/
├── bert/          # BERT and variants
├── gpt2/          # GPT-2 family
├── gpt_neo/       # GPT-Neo
├── gpt_j/         # GPT-J
├── llama/         # LLaMA family
├── mistral/       # Mistral
├── gemma/         # Gemma
├── qwen/          # Qwen
├── phi3/          # Phi-3
├── falcon/        # Falcon
├── stablelm/      # StableLM
├── t5/            # T5 family
├── bart/          # BART
├── vit/           # Vision Transformer
├── clip/          # CLIP
├── mamba/         # Mamba
├── rwkv/          # RWKV
├── retnet/        # RetNet
├── hyena/         # Hyena
├── s4/            # S4
├── weight_loading/  # Weight loading infrastructure
└── lib.rs         # Module exports
```

### Weight Loading Module
- ✅ **Modular Design** - 7 focused modules (was 1962 lines, now split)
- ✅ **Configuration** - `WeightLoadingConfig`
- ✅ **HuggingFace Loader** - SafeTensors/PyTorch/JSON
- ✅ **Memory-Mapped Loader** - Zero-copy loading
- ✅ **Streaming Loader** - Chunk-based loading
- ✅ **Distributed Loader** - Multi-node weight loading
- ✅ **GGUF Loader** - GGML quantized format
- ✅ **Utilities** - Helper functions

---

## Testing & Validation

- ✅ Integration tests for all major models
- ✅ Weight loading validation tests
- ✅ Numerical parity tests with HuggingFace
- ✅ Forward pass correctness validation
- ✅ Generation quality tests
- ✅ Memory usage validation
- ✅ Cross-backend compatibility tests

---

## Known Limitations

### CLIP Model
- Text encoder weight loading incomplete
- Vision encoder weight loading incomplete
- Requires additional architectural integration work

### Some Multimodal Models
- Complex architectures may have partial weight loading
- Some components may require manual weight mapping

---

## Future Enhancements

### High Priority
- Complete CLIP text/vision encoder weight loading
- Enhanced multimodal model support
- Additional vision transformer variants

### New Models
- Latest research architectures as they emerge
- Domain-specific model variants
- More efficient architectures

### Optimizations
- Model-specific kernel optimizations
- Enhanced quantization support
- Memory usage improvements
- Faster weight loading

---

## Development Guidelines

### Adding a New Model

**Step-by-step checklist:**

1. **Create Module Structure**
   ```
   src/your_model/
   ├── config.rs    # Configuration struct
   ├── model.rs     # Base model implementation
   ├── tasks.rs     # Task-specific heads
   └── mod.rs       # Module exports
   ```

2. **Implement Configuration**
   - Create `YourModelConfig` struct
   - Implement `Config` trait from trustformers-core
   - Add `validate()` method for config validation
   - Support `from_pretrained` for HuggingFace compatibility

3. **Implement Base Model**
   - Create `YourModel` struct with layers
   - Implement `Model` trait from trustformers-core
   - Add `forward()` method for inference
   - Use only trustformers-core abstractions (no external deps)

4. **Implement Task Heads**
   - `YourModelForCausalLM` for text generation
   - `YourModelForSequenceClassification` for classification
   - `YourModelForTokenClassification` for NER
   - Other task-specific variants as needed

5. **Add Weight Loading**
   - Implement `load_from_path()` method
   - Use weight loading infrastructure from `weight_loading/`
   - Handle SafeTensors, PyTorch, JSON formats
   - Add proper error handling and logging

6. **Add Feature Gate**
   - Add to `Cargo.toml`: `your_model = []`
   - Use `#[cfg(feature = "your_model")]` guards

7. **Export Types**
   - Export in `src/lib.rs`
   - Add to relevant feature gates

8. **Write Tests**
   - Compare outputs with HuggingFace implementation
   - Test weight loading
   - Test forward pass correctness
   - Test generation (if applicable)

9. **Document**
   - Add rustdoc with architecture description
   - Include usage examples
   - Document any limitations or special requirements

### Code Standards
- **Use only trustformers-core abstractions** (no external dependencies directly)
- **File size limit:** <2000 lines per file
- **Error handling:** Use `Result<T, TrustformersError>`
- **Testing:** Compare with HuggingFace for numerical validation
- **Naming:** snake_case for all identifiers

### Build & Test Commands

```bash
# Build specific model
cargo build -p trustformers-models --features llama

# Test specific model
cargo nextest run -p trustformers-models --features llama

# Test all models
cargo nextest run -p trustformers-models --all-features

# Check compilation
cargo check -p trustformers-models --all-features
```

---

**Last Updated:** Refactored for alpha.1 release
**Status:** Production-ready model zoo
**Model Count:** 21+ architectures implemented
