nanochat-students/rl-d20

NanoChat SFT

This is the RL trained checkpoint from Andrej Karpathy's fullstack llm project to build an LLM, nanochat.

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer


model_name = "nanochat-students/rl-d20"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True).to(device)
model.eval()

conversation = [
    {"role": "user", "content": "Hello, who are you?"},
]
rendered = tokenizer.apply_chat_template(
    conversation,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([rendered], return_tensors="pt").to(model.device)

generated = model.generate(**model_inputs, max_new_tokens=256)
output_ids = generated[0, model_inputs.input_ids.shape[1]:]
print(tokenizer.decode(output_ids, skip_special_tokens=True))

Chat RL Training Metrics

timestamp: 2025-10-15 12:59:52

• run: burtenshaw-20251015111354
• source: sft
• dtype: bfloat16
• device_batch_size: 8
• examples_per_step: 16
• num_samples: 16
• max_new_tokens: 256
• temperature: 1.0000
• top_k: 50
• unembedding_lr: 0.0040
• embedding_lr: 0.2000
• matrix_lr: 0.0200
• weight_decay: 0.0000
• init_lr_frac: 0.0500
• num_epochs: 1
• save_every: 60
• eval_every: 60
• eval_examples: 400

Chat evaluation RL

timestamp: 2025-10-15 13:04:39

• source: rl
• task_name: GSM8K
• dtype: bfloat16
• temperature: 0.0000
• max_new_tokens: 512
• num_samples: 1
• top_k: 50
• batch_size: 8
• model_tag: None
• step: None
• max_problems: None
• GSM8K: 0.0970

Logs from training can be found here: https://huggingface.co/spaces/nanochat-students/trackio