Reinforcement Learning
Transformers
Safetensors
qwen2
text-generation
rlve
grpo
program-trees
text-generation-inference
Instructions to use davidheineman/RL-Base-R1-Distill-Q1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use davidheineman/RL-Base-R1-Distill-Q1.5B with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("davidheineman/RL-Base-R1-Distill-Q1.5B") model = AutoModelForCausalLM.from_pretrained("davidheineman/RL-Base-R1-Distill-Q1.5B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 950 Bytes
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
library_name: transformers
tags:
- reinforcement-learning
- rlve
- grpo
- program-trees
---
# RL-Base-R1-Distill-Q1.5B
DeepSeek-R1-Distill-Qwen-1.5B continued with reinforcement learning on
random program trees built from string-operation primitives. The trees have
depth 1–2 and up to two children at each node. Training used 20,000 sampled
rollouts (2,500 updates), one prompt and eight rollouts per update, four GPUs,
and 1,024 initial generation tokens plus answer forcing with up to 128 more
tokens. This is the final model checkpoint at update 2,499.
The training run and its in-distribution evaluation history are on
[Weights & Biases](https://wandb.ai/david-heineman/rl-trees-base-rl/runs/f75d51ca). The optimizer and sampler state are retained
in the original training checkpoint; this repository provides model weights
and tokenizer files for inference or further fine-tuning.
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