Chinchilla-1 is a ~73.5M parameter instruction-tuned language model built on the nanochat d6 architecture. The name references Chinchilla scaling laws — this is a research-scale model, not a production LLM.
This model was created as a learning exercise in SFT infrastructure (Modal, torchrun, MuonAdamW) and to explore nanochat's custom architecture (value embeddings, smear mechanism, backout residuals). It should be treated as a baseline for understanding how small transformers behave after SFT — not as a usable chatbot.
What it does well
Tool-augmented reasoning — correctly orchestrates <|python_start|> / <|python_end|> tokens to invoke Python for tasks like letter counting. Both checkpoints correctly emit 'strawberry'.count('r') and interpret the result. See evaluation Q7.
Basic Python generation — produces correct, idiomatic code for simple algorithmic tasks (e.g. palindrome: s == s[::-1] with alphanumeric filtering). See evaluation Q6.
Chat format fluency — consistently generates proper <|user_start|> / <|assistant_start|> conversation markers with no format breakages. Fluent, grammatically correct English prose. See evaluation §5.3–5.4.
Lightweight — 73.5M params, runs on CPU at ~2 tok/s, fits in ~280 MB (bf16) or ~140 MB (fp16 via NANOCHAT_DTYPE).
What it does poorly
This model has severe limitations that make it unsuitable for any factual or reasoning task without augmentation.
Pervasive hallucination — fabricates facts with high confidence. Examples: claims Paris is "located in the northern United States" (Q1), invents a mountain called "Krasnarol" (Q8), makes up fictional US government branches like "Rentalist" and "Operationalist" (Q10). See evaluation §6.1.
Arithmetic failure — cannot reliably perform multi-digit multiplication (15 × 37 was answered as 375 or avoided entirely). See evaluation §6.2.
Poor instruction following — ignores formatting constraints like "in one sentence" or "write a haiku", producing verbose multi-sentence paragraphs instead. See evaluation §6.4.
Rambling — responses degrade after ~50 tokens, becoming repetitive, self-contradictory, and off-topic. See evaluation §6.3.
Factual knowledge is absent — at 73.5M params the model lacks capacity to store reliable world knowledge. It falls back to plausible-sounding generation.
Value embeddings on alternating layers (layers 1, 3, 5) — full 32768 × 384 embedding tables injected into the residual stream via learned gates. Account for ~51% of total parameters (~37.7M of 73.5M).
Smear mechanism — blends previous token's embedding into the current position for cheap bigram-like coherence.
Backout residual subtraction — learns to subtract mid-layer features before the final LM head.
QK normalization — RMSNorm on query and key vectors before attention.
Logit softcap (tanh at 15) — prevents overconfident predictions.
Not weight-tied. Embedding and LM head are separate parameter matrices (2 × 12.6M params).
Parameter breakdown:
Component
Parameters
Token embedding
12,582,912
Value embeddings (3 layers)
37,748,736
LM head (unembedding)
12,582,912
6× Transformer (attention + MLP)
~10,616,832
Scalars & gates
254
Total
73,531,646
Training
Pretrain (base): nanochat d6 on 11 FineWeb-EDU shards (~1 GB), 32,768 BPE merges, trained to step 8600 with 512 sequence length on Kaggle GPUs.
SFT (this checkpoint): Supervised fine-tuning on a mixture of SmolTalk (460K rows), MMLU (~100K), GSM8K (~7.5K), SimpleSpelling (200K), SpellingBee (80K), and identity conversations (1K). Trained on Modal with 2× A10G GPUs for ~35 minutes at ~$1.20 total.
Knowledge distillation (optional): The model_001500.pt checkpoint was trained with KD from LFM2.5-350M (cross-tokenizer KD — limitations documented in the SFT report).
Hyperparameters: max_seq_len=2048, total_batch_size=524,288 tokens, device_batch_size=2 (KD run), AdamW for embeddings (LR 0.424) + Muon for matrix params (LR 0.02), linear warmup→constant→decay schedule over 1,500 steps.
Usage
⚠️ Not compatible with llama.cpp, Ollama, LM Studio, or HuggingFace Transformers' from_pretrained. This model uses a custom nanochat architecture that requires the nanochat inference engine.
bash
1# Install dependencies2pip install torch tiktoken rustbpe
34# Run inference5python inference.py --prompt "Write a Python function to reverse a string."
See inference.py — it loads the checkpoint and tokenizer from this repo.
Tool calls use <|python_start|>, <|python_end|>, <|output_start|>, <|output_end|> tokens for Python REPL integration.
Checkpoints included
File
Description
checkpoints/sft/model_001500.pt
Best SFT checkpoint (KD run, step 1500, val_bpb 0.4763)
checkpoints/sft/meta_001500.json
Training metadata (config, metrics)
checkpoints/tokenizer/tokenizer.pkl
rustbpe tokenizer for tiktoken
The alternative non-KD checkpoint (sft-d6, step 971, val_bpb 0.4891) is available on the Modal volume archive if needed.
Evaluation
A manual qualitative evaluation across 12 questions spanning factual knowledge, reasoning, math, code, tool use, and creative writing was conducted. Full results and example outputs in evaluations/checkpoints/report.md.
Scorecard (sft at step 1500 vs non-KD checkpoint at step 971):
Metric
Count
sft wins
2
d6 wins
3
Ties
7
The additional 529 SFT steps improved fluency modestly without improving factual accuracy. See the full checkpoint evaluation report for detailed question-by-question analysis.
Limitations
Do not use for any factual Q&A without retrieval augmentation (RAG).
Do not rely on for math or arithmetic — use the built-in Python tool instead.
Does not respect format instructions reliably.
Rambles on open-ended questions — consider setting max_tokens=64 for production use.
73.5M parameters is small even by 2020 standards (GPT-2 Small was 124M). Temper expectations accordingly.
Built on karpathy/nanochat (MIT license). Training on Modal cloud GPUs. SFT data includes SmolTalk by HuggingFaceH4, MMLU by hendrycks, GSM8K by OpenAI. The nanochat architecture and training recipe are the work of Andrej Karpathy and contributors.
