Podcast
Questions and Answers
What is the minimum recommended parameter size for a model to effectively generate thinking tokens when using GRPO?
What is the minimum recommended parameter size for a model to effectively generate thinking tokens when using GRPO?
- 1.5B parameters (correct)
- 500M parameters
- 2B parameters
- 1B parameters
Training loss tracking for GRPO requires external tools like wandb when using Unsloth.
Training loss tracking for GRPO requires external tools like wandb when using Unsloth.
False (B)
Besides GRPO, name one other reinforcement learning method that Unsloth supports for language model training?
Besides GRPO, name one other reinforcement learning method that Unsloth supports for language model training?
Online DPO (or PPO or RLOO)
Using Unsloth with vLLM allows you to finetune and perform ________ on the model simultaneously.
Using Unsloth with vLLM allows you to finetune and perform ________ on the model simultaneously.
What is the approximate VRAM savings observed when loading vLLM and Unsloth together for Llama 3.2 3B, as inspired by Boris?
What is the approximate VRAM savings observed when loading vLLM and Unsloth together for Llama 3.2 3B, as inspired by Boris?
Without memory optimizations, finetuning Llama 3.3 70B with Unsloth and vLLM requires less than 48GB of VRAM.
Without memory optimizations, finetuning Llama 3.3 70B with Unsloth and vLLM requires less than 48GB of VRAM.
Match the following components with their roles in fast inference using Unsloth:
Match the following components with their roles in fast inference using Unsloth:
What speed improvement can be achieved with LoRA loading in vLLM via parsing a state dict instead of loading from disk?
What speed improvement can be achieved with LoRA loading in vLLM via parsing a state dict instead of loading from disk?
What key advantage does Group Relative Policy Optimization (GRPO) offer over Proximal Policy Optimization (PPO) in the context of training reasoning models?
What key advantage does Group Relative Policy Optimization (GRPO) offer over Proximal Policy Optimization (PPO) in the context of training reasoning models?
DeepSeek's R1-Zero model learned to allocate more thinking time without any human feedback by using Group Relative Policy Optimization (GRPO).
DeepSeek's R1-Zero model learned to allocate more thinking time without any human feedback by using Group Relative Policy Optimization (GRPO).
What type of reward functions are crucial when using GRPO to train a model for reasoning?
What type of reward functions are crucial when using GRPO to train a model for reasoning?
Unsloth enhances the GRPO process, making it use 80% less _______ than Hugging Face + FA2.
Unsloth enhances the GRPO process, making it use 80% less _______ than Hugging Face + FA2.
Match the following components with their roles in training reasoning models:
Match the following components with their roles in training reasoning models:
Why is it necessary to pip install diffusers when using GRPO with Unsloth locally?
Why is it necessary to pip install diffusers when using GRPO with Unsloth locally?
According to the content, one should only wait for 100 steps for the reward to actually increase when using GRPO.
According to the content, one should only wait for 100 steps for the reward to actually increase when using GRPO.
What observed behavior during the training of DeepSeek's R1-Zero model indicated an 'aha moment'?
What observed behavior during the training of DeepSeek's R1-Zero model indicated an 'aha moment'?
Flashcards
GRPO
GRPO
A training method requiring at least 12 hours for effective results on models with 1.5B+ parameters.
Unsloth
Unsloth
A framework that incorporates GRPO and provides built-in loss tracking without external tools.
Online DPO
Online DPO
An advanced generation method supported by Unsloth for improved token processing.
vLLM
vLLM
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VRAM Consumption
VRAM Consumption
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LoRA Loading
LoRA Loading
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Finetuning
Finetuning
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Inference
Inference
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R1 reasoning model
R1 reasoning model
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Group Relative Policy Optimization (GRPO)
Group Relative Policy Optimization (GRPO)
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'Aha moment'
'Aha moment'
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Thinking token
Thinking token
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Self-verification
Self-verification
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Reward function
Reward function
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Reinforcement learning (RL)
Reinforcement learning (RL)
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Study Notes
Unsloth (GRPO) Reasoning Model
- Unsloth now incorporates reasoning capabilities using Group Relative Policy Optimization (GRPO)
- GRPO significantly reduces VRAM usage (80% less than Hugging Face + FA2).
- Enables training R1-Zero's reasoning capabilities with only 7GB VRAM using Qwen2.5 (1.5B).
- Free GRPO notebooks available on Colab for Llama 3.1 (8B) and other models (Phi-4).
- R1-Zero autonomously learned to allocate thinking time without human feedback using GRPO.
- GRPO is a reinforcement learning (RL) algorithm, optimizing responses without a value function, unlike PPO.
- GRPO helps models develop self-verification and reasoning abilities automatically.
- Training involves creating reward functions (e.g., correct answers = 1, spelling error = -0.1).
- Train with GRPO for at least 12 hours for better results.
- Use at least a 1.5B parameter model for GRPO to generate thinking tokens.
Model Training with GRPO
- Requires a chat template for base models
- Training loss tracking is integrated within Unsloth (no need for external tools).
- Works with Online DPO, PPO, and RLOO (plus GRPO)
vLLM Integration and Performance
- Enables direct use of vLLM in finetuning stacks improving throughput.
- Concurrent finetuning and inference on models.
- 300 tokens/sec on 16GB Tesla T4 (free Colab GPU) with Llama 3 models
- Significant VRAM savings enabled by dynamic 4bit quantization.
- Memory usage reduced to 48GB with dynamic quantization from 80GB requirement.
- Installation (pip install diffusers) and fast inference integration (
fast_inference).
LoRA Loading in vLLM
- LoRA loading in vLLM is 1.5x faster through state dict parsing.
- Direct editing of LoRA adapters within vLLM is an active area of research. (to boost speed further)
Additional Notes
- DeepSeek researchers' "aha moment" involved R1-Zero's self-improvement without human intervention in the training process.
- GRPO creates reasoning "traces" for tasks (e.g. calculation).
- Example models used in notebooks include Llama 3.1 (8B) and Phi-4.
- GitHub repository for Unsloth: github.com/unslothai/unsloth
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