🏛️ Govind222/68m-base

Govind222/68m-base is a 68.96M parameter Dense Foundation / Base Small Language Model (SLM) trained completely from scratch using transformer-toolkit on 4.0 Billion tokens of FineWeb (sample-10BT).

This checkpoint is the raw pretrained foundation base model. It serves as a data-efficient foundation for downstream task adaptation, instruction fine-tuning, domain specialization, or edge device deployment.

• SFT Fine-Tuned Version (Chat / Dialog): Govind222/68m-base-guppy-sft
• Training Framework: transformer-toolkit on GitHub
• Interactive Live Demo (SFT): https://fish-lm.runlog.in/

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⚡ Key Highlights

• Pretrained from Scratch: Trained on 4 Billion tokens of clean English web text from Hugging Face's FineWeb (sample-10BT).
• Pure Dense Transformer: 100% dense computation with zero MoE, maximizing per-parameter compute density and caching efficiency on constrained hardware.
• Modern Architectural Recipe:
  • Grouped-Query Attention (GQA): 8 query heads, 2 key/value heads (4:1 ratio) for fast KV-cache memory reuse.
  • SwiGLU FFN: Non-linear Swish-Gated Linear Units with 1,536 hidden dimension ($3 \times d_{\text{model}}$).
  • RoPE: Rotary Position Embeddings for robust position modeling.
  • RMSNorm: Pre-layer normalization with $\epsilon = 1\text{e-}6$.
• Data Efficiency: Competitive 0-shot benchmark accuracy against models trained on 10× to 50× more parameters/compute.
• Compact Footprint: Under ~280 MB RAM footprint at runtime, ideal for edge Android/iOS smartphones and Raspberry Pi.

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📊 Zero-Shot Benchmark Evaluation

Evaluated zero-shot using lm-evaluation-harness directly on the base pretrained checkpoint:

Benchmark	Task / Metric	Govind222/68m-base	Pythia 70M	Pythia 160M	GPT-Neo 125M	OPT 125M
PIQA	Physical Interaction QA (acc)	58.1%	58.6%	61.4%	62.3%	61.8%
Winogrande	Commonsense Reasoning (acc)	48.7%	51.6%	50.4%	49.5%	51.5%
ARC-Easy	Science QA Reasoning (acc_norm)	31.4%	34.2%	—	38.9%	39.8%
Average	Mean Accuracy across 4 tasks	39.6%	41.4%	43.7%	43.5%	43.9%

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⚙️ Model Architecture Specifications

Hyperparameter	Value	Description
Total Parameters	68,957,056 (~68.96M)	Exact non-embedding + embedding tensor weight sum
Model Type	Dense Decoder-Only SLM	Pure dense transformer (no MoE)
Layers (n_layers)	12	Transformer blocks
Hidden Dimension (dim)	512	Model width ($d_{\text{model}}$)
Query Attention Heads (n_heads)	8	Head dimension = 64
KV Attention Heads (n_kv_heads)	2	4:1 Grouped Query Attention (GQA)
Feed-Forward Dimension (hidden_dim)	1536	$3 \times d_{\text{model}}$
FFN Activation	swiglu	Dense SwiGLU projection (w1, w2, w3)
Positional Encoding	rope	Rotary Position Embeddings
Normalization	rmsnorm	Pre-normalization with $\epsilon = 1\text{e-}6$
Vocabulary Size	32000	Custom Rust BPE Tokenizer
Context Window (max_seq)	512	Sequence length
Weight Tying	false	Independent input embeddings and output LM head

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📈 Pretraining Details

• Dataset: 4.0 Billion tokens from HuggingFaceFW/fineweb (sample-10BT)
• Framework: transformer-toolkit
• Optimization: AdamW with cosine learning rate decay and linear warmup
• Hardware: 1× NVIDIA L40S GPU
• Training Compute Cost: $14 total cost (14 hours runtime)

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🚀 Quickstart & Inference

1. Installation

pip install torch transformer-toolkit