---
license: apache-2.0
language:
  - en
pipeline_tag: text-generation
tags:
  - pytorch
  - safetensors
  - text-generation
  - small-llm
  - custom-architecture
  - linear-attention
  - gated-deltanet
  - test-time-training
  - hybrid-attention
  - research
library_name: genesis-llm
datasets:
  - HuggingFaceTB/smol-smoltalk
base_model: []
---

<div align="center">
  <h1>🧬 Genesis-152M-Instruct</h1>
  <p><em>A Research-Oriented Small Language Model with Hybrid Linear Attention</em></p>
  
  <p>
    <a href="#architecture"><img alt="Architecture" src="https://img.shields.io/badge/Architecture-Hybrid_GLA%2BFoX-blue"></a>
    <a href="#training"><img alt="Training" src="https://img.shields.io/badge/Pre--training-2B_tokens-green"></a>
    <a href="#license"><img alt="License" src="https://img.shields.io/badge/License-Apache_2.0-orange"></a>
  </p>
</div>

---

## Table of Contents

- [Overview](#overview)
- [Model Summary](#model-summary)
- [Architecture Deep Dive](#architecture-deep-dive)
  - [Hybrid Attention Layout](#hybrid-attention-layout)
  - [Gated DeltaNet (GLA)](#gated-deltanet-gla)
  - [Forgetting Attention (FoX)](#forgetting-attention-fox)
  - [Test-Time Training (TTT)](#test-time-training-ttt)
  - [Selective Activation](#selective-activation)
  - [Additional Components](#additional-components)
- [Comparison with Other Architectures](#comparison-with-other-architectures)
- [Training Details](#training-details)
  - [Pre-training](#pre-training)
  - [Supervised Fine-Tuning (SFT)](#supervised-fine-tuning-sft)
- [Usage](#usage)
- [Benchmarks](#benchmarks)
- [Limitations](#limitations)
- [Citation](#citation)
- [License](#license)

---

## Overview

**Genesis-152M-Instruct** is an experimental small language model that combines recent advances in efficient attention mechanisms into a single architecture. It serves as a research platform for exploring:

- **Hybrid attention**: Mixing O(n) linear attention with O(n²) softmax attention
- **Efficient inference**: Sub-quadratic complexity for most layers
- **Adaptive computation**: Test-time training for dynamic model adaptation

> ⚠️ **Experimental Model**: This is a research artifact, not a production-ready model. It demonstrates architectural innovations but has limitations typical of small models.

---

## Model Summary

| Property | Value |
|----------|-------|
| **Parameters** | 151.8M total (~122.8M non-embedding) |
| **Architecture** | Hybrid GLA + FoX Attention |
| **Context Length** | 2,048 tokens |
| **Vocab Size** | 50,279 (GPT-NeoX + ChatML tokens) |
| **Pre-training Data** | 2B tokens |
| **SFT Dataset** | [smol-smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) |
| **License** | Apache 2.0 |

### Files in this Repository

```
├── genesis_152m_instruct.safetensors  # Model weights
├── README.md                           # This model card
└── LICENSE                             # Apache 2.0
```

---

## Architecture Deep Dive

Genesis follows a **"deep-and-thin"** design philosophy inspired by [SmolLM2](https://arxiv.org/abs/2502.02737) and [MobileLLM](https://arxiv.org/abs/2402.14905), which has proven effective for small language models.

### Core Configuration

| Component | Value | Rationale |
|-----------|-------|-----------|
| Layers | 30 | Deep architecture for better representation |
| Hidden Size | 576 | Optimal width for 150M scale |
| Attention Heads | 9 | Query heads |
| KV Heads | 3 | 3:1 GQA ratio for memory efficiency |
| Head Dimension | 64 | Standard for efficient attention |
| FFN Size | 1,440 | 2.5× expansion (SwiGLU-efficient) |
| Weight Tying | ✓ | Embeddings tied with LM head |

---

### Hybrid Attention Layout

Genesis employs a **hybrid attention layout** inspired by [Qwen3-Next](https://huggingface.co/docs/transformers/main/en/model_doc/qwen3_next), alternating between linear and full attention:

```
Layer Distribution (30 layers):
├── 23 layers: GLA (Gated DeltaNet) - O(n) linear attention
└──  7 layers: FoX (Forgetting Attention) - O(n²) softmax with forget gate

Ratio: 75% Linear / 25% Full Attention
```

**Why hybrid?** Pure linear attention struggles with precise retrieval tasks (e.g., copying, in-context learning). Interleaving full attention layers restores this capability while maintaining overall efficiency.

> 📖 **Reference**: The hybrid approach is validated by Qwen3-Next (2025) and research showing that [3:1 to 6:1 linear-to-full ratios](https://arxiv.org/abs/2507.06457) optimize the efficiency-quality tradeoff.

---

### Gated DeltaNet (GLA)

The primary attention mechanism (75% of layers) is **Gated DeltaNet**, a state-of-the-art O(n) linear attention mechanism from NVIDIA.

#### Key Features

| Feature | Description | Paper Reference |
|---------|-------------|-----------------|
| **Delta Rule** | Online learning rule for recurrent state updates | [Schlag et al., 2021](https://arxiv.org/abs/2102.11174) |
| **Gated Forget** | Mamba-style data-dependent forgetting | [Gu & Dao, 2023](https://arxiv.org/abs/2312.00752) |
| **Short Convolution** | 1D conv on Q, K, V for local context | [Gu et al., 2022](https://arxiv.org/abs/2212.14052) |
| **L2 QK-Norm** | Stabilizes attention scores | Standard practice |

#### Mathematical Formulation

The delta rule update enables the model to selectively write to and erase from a recurrent state:

```
S_t = α_t * S_{t-1} + β_t * (v_t ⊗ k_t - S_{t-1} @ k_t ⊗ k_t)
o_t = S_t @ q_t
```

Where:
- `S_t`: Recurrent state matrix
- `α_t`: Forget gate (data-dependent)
- `β_t`: Learning rate gate (per-token)

> 📖 **Paper**: [Gated Delta Networks: Improving Mamba2 with Delta Rule](https://arxiv.org/abs/2412.06464) (ICLR 2025)
> 
> 📦 **Code**: [NVlabs/GatedDeltaNet](https://github.com/NVlabs/GatedDeltaNet)

#### Configuration in Genesis

```python
gla_expand_k: 0.75      # Key expansion ratio
gla_expand_v: 1.5       # Value expansion ratio (asymmetric)
gla_gate_fn: "swish"    # Gating activation
gla_use_short_conv: True
gla_conv_size: 4
gla_chunk_size: 64      # For chunked parallel training
gla_use_delta_rule: True
gla_qk_norm: "l2"
gla_use_mamba_gate: True
```

---

### Forgetting Attention (FoX)

The full attention layers (25%) use **FoX (Forgetting Transformer)**, which augments standard softmax attention with a learnable forget gate.

#### Why FoX over Standard Attention?

| Aspect | Standard Attention | FoX |
|--------|-------------------|-----|
| Position Encoding | Requires RoPE/ALiBi | **NoPE** (implicit via forget gate) |
| Long-range Decay | Uniform attention | Data-dependent decay |
| Length Extrapolation | Poor | Better generalization |

#### Mechanism

FoX modifies attention scores with cumulative forget gates:

```
attn[i,j] = softmax(q_i @ k_j / √d + Σ_{k=j}^{i} log(f_k))
```

Where `f_k = sigmoid(W_f @ x_k)` is a learned forget gate that naturally down-weights distant tokens.

> 📖 **Paper**: [Forgetting Transformer: Softmax Attention with a Forget Gate](https://arxiv.org/abs/2503.02130) (ICLR 2025)
> 
> 📦 **Code**: [zhixuan-lin/forgetting-transformer](https://github.com/zhixuan-lin/forgetting-transformer)

#### FoX "Pro" Design

Genesis uses the enhanced "Pro" block design:

| Component | Purpose |
|-----------|---------|
| Output Gate | Controls information flow (like GLA) |
| QK-Norm | Training stability |
| Short Convolution | Local context on K, V |
| FusedRMSNormSwishGate | Efficient fused operations |

---

### Test-Time Training (TTT)

Genesis includes an experimental **TTT metacognition layer** that adapts the model during inference.

#### Concept

Traditional models have **fixed weights** at inference. TTT layers have a small set of **fast weights** that update based on the input sequence, allowing the model to "learn" from context.

```
Standard: y = f(x; θ_fixed)
TTT:      y = f(x; θ_fixed, θ_fast(x))
```

#### Implementation Details

| Parameter | Value | Description |
|-----------|-------|-------------|
| `ttt_rank` | 4 | Low-rank adaptation dimension |
| `ttt_inner_lr` | 0.01 | Learning rate for fast weights |
| `ttt_mode` | "dual" | Parallel dual-form computation |
| `ttt_chunk_size` | 64 | Chunking for efficiency |

The "dual form" enables fully parallel gradient computation:

```python
# Instead of sequential updates:
# W_1 = W_0 - lr * grad_0
# W_2 = W_1 - lr * grad_1
# ...

# Dual form computes all at once:
# W_t = W_0 - lr * Σ_{i<t} grad_i  (via cumsum)
```

> 📖 **Paper**: [Learning to (Learn at Test Time): RNNs with Expressive Hidden States](https://arxiv.org/abs/2407.04620) (ICML 2024)
> 
> 📦 **Code**: [test-time-training/ttt-lm-pytorch](https://github.com/test-time-training/ttt-lm-pytorch)

#### When TTT Activates

TTT is designed for **inference-time adaptation** and runs only during `model.eval()`. During training, it's disabled to avoid overhead.

---

### Selective Activation

The FFN layers use **SwiGLU** with optional top-k sparsity masking.

#### SwiGLU FFN

```python
FFN(x) = (Swish(W_gate @ x) ⊙ (W_up @ x)) @ W_down
```

> 📖 **Paper**: [GLU Variants Improve Transformer](https://arxiv.org/abs/2002.05202) (Shazeer, 2020)

#### Selective Activation (Experimental)

| Parameter | Value |
|-----------|-------|
| `selective_k_ratio` | 0.85 (keeps top 85%) |
| `selective_use_soft_mask` | True |

**Important**: This is a **regularization technique**, not a speedup mechanism. Real sparse acceleration requires specialized kernels (e.g., Triton sparse GEMM).

> 📖 **Related**: [ReLU Strikes Back](https://arxiv.org/abs/2310.04564) (Apple, ICLR 2024) shows natural activation sparsity can be exploited for inference.

---

### Additional Components

#### Grouped Query Attention (GQA)

Genesis uses 3:1 GQA (9 query heads, 3 KV heads) for memory efficiency during inference.

> 📖 **Paper**: [GQA: Training Generalized Multi-Query Transformer Models](https://arxiv.org/abs/2305.13245) (Google, 2023)

#### Rotary Position Embeddings (RoPE)

Partial RoPE (50% rotation) is applied in GLA layers for position awareness.

> 📖 **Paper**: [RoFormer: Enhanced Transformer with Rotary Position Embedding](https://arxiv.org/abs/2104.09864) (Su et al., 2021)

#### µP (Maximal Update Parametrization)

Hyperparameters were tuned using µP for potential scaling transfer.

> 📖 **Paper**: [Tensor Programs V: Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer](https://arxiv.org/abs/2203.03466) (Yang et al., 2022)
>
> 📖 **Guide**: [The Practitioner's Guide to µP](https://www.cerebras.ai/blog/the-practitioners-guide-to-the-maximal-update-parameterization) (Cerebras)

#### Zero-Centered RMSNorm

Used throughout for better weight decay compatibility with µP.

---

## Comparison with Other Architectures

### vs. SmolLM2-135M (HuggingFace)

| Aspect | Genesis-152M | SmolLM2-135M |
|--------|--------------|--------------|
| **Attention** | Hybrid GLA + FoX | Standard Multi-Head |
| **Complexity** | O(n) for 75% layers | O(n²) all layers |
| **Position Encoding** | RoPE (GLA) / NoPE (FoX) | RoPE |
| **TTT** | ✓ Experimental | ✗ |
| **Pre-training** | 2B tokens | 2T tokens |
| **Architecture** | 30L × 576 | 30L × 576 |

> SmolLM2 uses 1000× more training tokens, making direct benchmark comparison unfair. Genesis demonstrates architectural innovations, not data scaling.

### vs. Qwen3-Next

| Aspect | Genesis-152M | Qwen3-Next-80B-A3B |
|--------|--------------|---------------------|
| **Scale** | 152M | 80B (3B active) |
| **Linear Attention** | GLA (same) | GLA |
| **Full Attention** | FoX | Standard |
| **Hybrid Ratio** | 75/25 | Similar |
| **MoE** | ✗ | ✓ |

Genesis can be seen as a **miniature research version** of the hybrid attention approach that Qwen3-Next uses at scale.

### vs. Mamba / Mamba-2

| Aspect | Genesis-152M | Mamba-2 |
|--------|--------------|---------|
| **Architecture** | Hybrid (Linear + Softmax) | Pure SSM |
| **Retrieval** | Strong (FoX layers) | Limited |
| **Implementation** | PyTorch + Optional Triton | Requires CUDA |
| **Flexibility** | Modular | Monolithic |

---

## Training Details

### Pre-training

| Parameter | Value |
|-----------|-------|
| **Tokens** | 2 billion |
| **Dataset Mix** | FineWeb-Edu (51%), DCLM (22%), FineMath (12%), Stack-Edu (8%), Cosmopedia (5%), Synth (2%) |
| **Context Length** | 2,048 |
| **Batch Size** | 128 |
| **Learning Rate** | 1e-3 (WSD schedule) |
| **Optimizer** | AdamW (β₁=0.9, β₂=0.95) |
| **Weight Decay** | 0.1 |
| **Warmup** | 5% of steps |
| **Hardware** | Single A100 80GB |

#### Learning Rate Schedule

**WSD (Warmup-Stable-Decay)**:
- Warmup: 5% of training (linear ramp)
- Stable: 85% of training (constant LR)
- Decay: 10% of training (cosine to min_lr)

### Supervised Fine-Tuning (SFT)

| Parameter | Value |
|-----------|-------|
| **Dataset** | [smol-smoltalk](https://huggingface.co/datasets/HuggingFaceTB/smol-smoltalk) |
| **Samples** | ~485K conversations |
| **Epochs** | 1 |
| **Learning Rate** | 1e-3 |
| **Batch Size** | 32 (effective: 128 with grad accum) |

#### smol-smoltalk Composition

The SFT dataset is the same used to train SmolLM2-135M-Instruct:

| Subset | Purpose |
|--------|---------|
| smol-magpie-ultra-short | Instruction following |
| everyday-conversations | Multi-turn dialogue |
| smol-rewrite | Text editing |
| smol-summarize | Summarization |
| openhermes-100k | Knowledge & reasoning |
| systemchats-30k | System prompt following |

> This dataset was specifically curated for small models (<1B params) and avoids issues like `<think>` tags from reasoning models.

---

## Usage

### Installation

```bash
pip install genesis-llm
```

### Download Weights

```bash
pip install "huggingface-hub>=0.20"
huggingface-cli download guiferrarib/genesis-152m-instruct genesis_152m_instruct.safetensors --local-dir .
```

### Interactive Chat

```bash
genesis --model ./genesis_152m_instruct.safetensors
```

### Python API

```python
import json
import torch
from safetensors import safe_open
from safetensors.torch import load_file
from genesis import Genesis, GenesisConfig, get_tokenizer

# 1. Load config from checkpoint metadata
model_path = "./genesis_152m_instruct.safetensors"
with safe_open(model_path, framework="pt", device="cpu") as f:
    metadata = f.metadata() or {}
    config_dict = json.loads(metadata.get("genesis_config_json", "{}"))
    config = GenesisConfig(**config_dict) if config_dict else GenesisConfig.genesis_147m()

# 2. Load model weights
device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu"
state_dict = load_file(model_path, device=device)
model = Genesis(config).to(device)
model.load_state_dict(state_dict, strict=False)
model.eval()

# 3. Setup tokenizer (GPT-NeoX + ChatML tokens)
tokenizer = get_tokenizer("neox")
tokenizer.add_chat_tokens()

# 4. Build ChatML prompt
prompt = """<|im_start|>system
You are a helpful assistant.
<|im_end|>
<|im_start|>user
Explain what linear attention is in simple terms.
<|im_end|>
<|im_start|>assistant
"""

# 5. Generate
input_ids = torch.tensor([tokenizer.encode(prompt)], device=device)
with torch.no_grad():
    output_ids = model.generate(input_ids, max_new_tokens=256, temperature=0.7)
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:].tolist())
print(response)
```

### Prompt Format

Genesis uses **ChatML** format:

```
<|im_start|>system
{system_message}
<|im_end|>
<|im_start|>user
{user_message}
<|im_end|>
<|im_start|>assistant
{assistant_response}<|im_end|>
```

---

## Benchmarks

Evaluated using LightEval on MPS (Apple Silicon).

### Results

| Task | Metric | Score | Stderr |
|------|--------|-------|--------|
| **ARC-Easy** (25-shot) | acc_norm | 44.02% | ±1.02 |
| **ARC-Challenge** (25-shot) | acc_norm | 24.66% | ±1.26 |
| **BoolQ** (0-shot) | acc_norm | 56.30% | ±0.87 |
| **HellaSwag** (10-shot) | acc_norm | 30.19% | ±0.46 |
| **Winogrande** (5-shot) | acc | 49.09% | ±1.41 |
| **CommonsenseQA** (0-shot) | acc_norm | 29.16% | ±1.30 |
| **OpenBookQA** (0-shot) | acc_norm | 28.60% | ±2.02 |
| **SciQ** (0-shot) | acc_norm | 46.80% | ±1.58 |

### Interpretation

| Task | Random Baseline | Genesis | Signal |
|------|-----------------|---------|--------|
| ARC-Easy | 25% | 44% | ✅ **Strong** |
| BoolQ | 50% | 56% | ✅ Learning |
| HellaSwag | ~25% | 30% | ✅ Learning |
| Winogrande | 50% | 49% | ⚠️ At baseline |
| ARC-Challenge | ~25% | 25% | ⚠️ Too hard for size |

> **Note**: With only 2B pre-training tokens (vs. 2T for SmolLM2), benchmarks primarily reflect architectural capacity rather than world knowledge.

---

## Limitations

### Known Issues

1. **Hallucinations**: Frequent factual errors due to limited pre-training data
2. **Math**: Unreliable arithmetic and multi-step reasoning
3. **Instruction Following**: Can be brittle with strict constraints
4. **TTT Overhead**: Metacognition layer adds latency (can be disabled)

### Not Suitable For

- Production deployments requiring reliability
- Tasks requiring factual accuracy
- Complex multi-step reasoning
- Safety-critical applications

### Best Use Cases

- Architecture research and ablation studies
- Efficient attention mechanism exploration
- Small model behavior analysis
- Educational purposes

---

## Citation

If you use Genesis in your research, please cite:

```bibtex
@misc{genesis2025,
  title={Genesis: A Hybrid Linear Attention Architecture for Small Language Models},
  author={Ferrari Brescia, Guilherme},
  year={2025},
  url={https://huggingface.co/guiferrarib/genesis-152m-instruct}
}
```

### Related Papers

```bibtex
@inproceedings{yang2024gated,
  title={Gated Delta Networks: Improving Mamba2 with Delta Rule},
  author={Yang, Songlin and Wang, Bailin and Zhang, Yu and Shen, Yikang and Keutzer, Kurt},
  booktitle={ICLR},
  year={2025}
}

@inproceedings{lin2025forgetting,
  title={Forgetting Transformer: Softmax Attention with a Forget Gate},
  author={Lin, Zhixuan and others},
  booktitle={ICLR},
  year={2025}
}

@inproceedings{sun2024learning,
  title={Learning to (Learn at Test Time): RNNs with Expressive Hidden States},
  author={Sun, Yu and others},
  booktitle={ICML},
  year={2024}
}

@article{allal2025smollm2,
  title={SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model},
  author={Allal, Loubna Ben and others},
  journal={arXiv preprint arXiv:2502.02737},
  year={2025}
}
```

---

## License

| Component | License |
|-----------|---------|
| **Model Weights** | Apache 2.0 |
| **Code** | Apache 2.0 |
| **Training Data** | Various (see dataset cards) |

---

<div align="center">
  <p><em>Built with 🧬 by the Orch-Mind team</em></p>
  <p>
    <a href="https://pypi.org/project/genesis-llm/">PyPI</a>
  </p>
</div>