Andrej Karpathy issues 'nanochat': Small style pipeline, at the end you can train in 4 hours ~ $ 100

Andrej Karpathy is up open open In NanochatCompact, Complain-Light Codebase Using ChatGpt-Style-style Trainer – from Tokenzer Training Infence – aimed at recycling, unlocked llm in one GPU area.

The repo provides a single script issuing the full loop: Tokenwazation, the basis of legitimacy, middle-case / device use, RL option, an RL option in the UI of the UI. Recommended setup is 8 × H100 even; to ~ $ 100. Post-run report.md Metric (Core, Arc-ERC-e / c, MMLU, GSM8K, Hunterval, ChattCore).

Tokenzer and data method

• Tokenzer: BPE Brust BPE (built with a mateurin), about 65,536-token vocab; Using Training Fileweb-Edu Shards (re-curbed / trigger for easy access). Pedestrian transport
• A bundle to check: Selected set of Core . ~/.cache/nanochat/eval_bundle.

Model, measurement, and “Speedrun” consent

Speedrun Config Training a depth-20 transformer . The author estimates this run as ~ 4E19 Flops Type model. Using Training Pace By the bounds of matmul and Personally embeddown / infidelity; Lost reported to Bits-Per-Byte (BPB) to be tokozer-invarianant.

Training in the middle, SFT, and tools for tools

After pretense, Training in the middle Synchronizing a basic model to negotiations (SMOLTALK) and apparently teaching for a lot of selected moral (100k mmlu Au xliiliary 1 questions 1 Use of tools By installing <|python_start|>…<|python_end|> blocks; The small GSM8K cone is included in seed use-style of use. Automatic mixture: Smoltalk (460k), MMLU AUX-Train (100k), GSM8K Main (8K)volume 568k the lines.

Soup Then good tunes in high-quality conversations while accompanied by the test lines (good, non-performed lines) to reduce the complexity of train / design. EXAMPLE METRIC METRIC (Speedrun Tier) Report Arc-Easy 0.3876, Arc-challenge 0.2807, MML 0.3151, GSM8K 0.0455, Humeval 0.0854, ChattCore 0.0884.

The use of tools is the only line-up to the end: traditionally Mechanism Practical Things KV Cache, Read / Conece Humility, as well as easy Python translator The Sandbox of Run-Augment Augmented Run-Used in Transport for both training and evaluation.

RL option on GSM8K with simplified GRPO LOOP

The final phase (optional) is valid Emphasis on Reading despite of- Gsm8k with a Simplified GRPO process. Walkthrough specifies what is left with PPo-Style RLHF of canonical actually, behaves closely Tighten While you end up A relative of a group Counting of profits. Scripture scripts.chat_rl including scripts.chat_eval -i rl -a GSM8K Show LOOP.

Cost / Quality Scale and Large Models

Ready has two two biggest paintings over ~ $ 100 Speedrun:

• ~ $ 300 tier: D = 26 (~ 12 hours), Slowly passes by GPT-2 core; It needs more shards as if shards and batch-maid repairs.
• ~ $ 1,000 tier: ~ 41.6 hourswith a materially developed interaction and the best consulting ability / ability to write.

Repo also reacts to the previous test run when a D = 30 A model trained ~ 24 hours reached 40s in MMLU, 70s in ARC-easy, 20s in GSM8K.

Snapshot exam (Speedrun Tier)

Illustration report.md Table of ~ $ 100 / ≈4-Hour Run: CORE 0.2219 (BASE); After the average / sft training, Arc-e 0.3561 → 0.3876, Arc-C ~ 0.2875 → 0.2807, MML 0.3111 → 0.3151, GSM8K 0.0250 → 0.0455, Humeval 0.0671 → 0.0854, ChattCore 0.0730 → 0.0884; Wall-clock 3h51m.

Healed Key

• Nanochat is Chatgpt-to-End ChatGpt-Style-Style-Style ChatGPT (~ 8k Loc) running with single speedrun.sh In 8 × H100 and De (~ 4h ≈ $ 100).
• The pipe covers Tokenzer (Brust BPE), the foundation of home, between training, sft, an RL option in GSM8K (Simplified GRPO), testing, and operating (CLI + UI).
• Speedrun metrics (for example report.md): The basis of 0.2219; After SFT-ARC-Easy 0.3876, ARC-Challenge 0.2807, MML 0.3151, GSM8K 0.0455, Hunterval 0.0854.
• Rate Tiers described: ~ $ 300 (D = 26, “Slowly” GPHFROMS GPT-2 core “; ~ $ 1,000 (~ 41.6h) with better matters of material / consultation.

Karthy's In Nanochat Countries in the Middle Ages: One area, clean, leaning – the leaning of Tokozer training, prethaineng in Fleweb-Edu, Smoltalk / MP Translation) in the recent Speedrun In 8 × H100 and DO, you produce trail report.md By core / arc / mmlu / GSM8K / GSM8K / Hunterval and UI Little UI.

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