04_download_trainining_checkpoints

Downloading and Using Training Checkpoints

This folder explains how to download and use the chapter 7 training checkpoints from Hugging Face at https://huggingface.co/rasbt/qwen3-from-scratch-grpo-checkpoints.

The checkpoints are plain PyTorch state_dict files for the reasoning_from_scratch package. They are not Hugging Face Transformers checkpoints.

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Note: If you are not a uv user, replace uv run ...py with python ...py in the examples below.

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Available Checkpoint Folders

• 7_3_plus_tracking: GRPO checkpoints with additional metric tracking
• 7_4_plus_clip_ratio: GRPO checkpoints with clipped policy ratios
• 7_5_plus_kl: GRPO checkpoints with a KL term
• 7_6_plus_format_reward: GRPO checkpoints with an explicit format reward for <think> tags

The checkpoints are hosted in:

• rasbt/qwen3-from-scratch-grpo-checkpoints

 

Downloading a Checkpoint

Use download_qwen3_grpo_checkpoints(...) from reasoning_from_scratch.qwen3:

from reasoning_from_scratch.qwen3 import download_qwen3_grpo_checkpoints

checkpoint_path = download_qwen3_grpo_checkpoints(
    grpo_type="clip_ratio",
    step="00050",
    out_dir="qwen3",
)

 

Which Tokenizer to Use

Use the base tokenizer for:

• 7_3_plus_tracking
• 7_4_plus_clip_ratio
• 7_5_plus_kl

Use the reasoning tokenizer for:

• 7_6_plus_format_reward

The reason is that 7_6_plus_format_reward was trained from the reasoning model and expects the reasoning chat formatting.

 

Usage Example

The example below downloads a checkpoint, downloads the matching tokenizer, loads the model, and generates text with generate_text_basic_stream_cache from chapter 2:

from pathlib import Path
import torch

from reasoning_from_scratch.ch02 import (
    get_device,
    generate_text_basic_stream_cache,
)
from reasoning_from_scratch.ch03 import render_prompt
from reasoning_from_scratch.qwen3 import (
    download_qwen3_grpo_checkpoints,
    download_qwen3_small,
    Qwen3Model,
    Qwen3Tokenizer,
    QWEN_CONFIG_06_B,
)

device = get_device()
local_dir = Path("qwen3")

checkpoint_path = download_qwen3_grpo_checkpoints(
    grpo_type="clip_ratio",
    step="00050",
    out_dir=local_dir,
)
download_qwen3_small(kind="base", tokenizer_only=True, out_dir=local_dir)

tokenizer = Qwen3Tokenizer(tokenizer_file_path=local_dir / "tokenizer-base.json")
model = Qwen3Model(QWEN_CONFIG_06_B)
state_dict = torch.load(checkpoint_path, map_location=device)
model.load_state_dict(state_dict)
model.to(device)
model.eval()

prompt = render_prompt("Solve: If x + 7 = 19, what is x?")
input_ids = torch.tensor(tokenizer.encode(prompt), device=device).unsqueeze(0)

for token in generate_text_basic_stream_cache(
    model=model,
    token_ids=input_ids,
    max_new_tokens=256,
    eos_token_id=tokenizer.eos_token_id,
):
    token_id = token.squeeze(0).item()
    print(tokenizer.decode([token_id]), end="", flush=True)

 

Format-Reward Example

For 7_6_plus_format_reward, switch to the reasoning tokenizer:

from pathlib import Path

from reasoning_from_scratch.qwen3 import (
    download_qwen3_small,
    Qwen3Tokenizer,
)

local_dir = Path("qwen3")
download_qwen3_small(kind="reasoning", tokenizer_only=True, out_dir=local_dir)

tokenizer = Qwen3Tokenizer(
    tokenizer_file_path=local_dir / "tokenizer-reasoning.json",
    apply_chat_template=True,
    add_generation_prompt=True,
    add_thinking=True,
)

 

Chapter 6 Example

The same helper also supports the original chapter 6 no-KL checkpoint:

from reasoning_from_scratch.qwen3 import download_qwen3_grpo_checkpoints

download_qwen3_grpo_checkpoints(grpo_type="no_kl", step="00050", out_dir="qwen3")

 

Available Checkpoints

Section mapping:

• no_kl: chapter 6 baseline from the original no-KL GRPO setup
• tracking: section 7.3 in the main chapter
• clip_ratio: section 7.4 in the main chapter
• kl: section 7.5 in the main chapter
• format_reward: section 7.6 in the main chapter

Available saved steps:

• no_kl: 00050, 00100, 00500, 01000, 01500, 03000, 05000, 09000
• tracking: 00050, 00100, 00150, 00200, 00250, 00300, 00350, 00400, 00450, 00500
• clip_ratio: 00050, 00100, 00150, 00200, 00250, 00300, 00350, 00400, 00450, 00500
• kl: 00050, 00100, 00150, 00200, 00250, 00300, 00350, 00400, 00450, 00500
• format_reward: 00050, 00100, 00150, 00200, 00250, 00300, 00350, 00400, 00450, 00500