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SeongryongJung/Qwen3-4B-Chemistry-RLSD

sourceHugging Faceapache-2.0updated 3mo agoView on Hugging Face

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Model Card

Qwen3-4B Chemistry RLSD

This repository contains Chemistry fine-tuned Qwen3-4B checkpoints from the local SciKnowEval-style generalization setup.

—Root checkpoint: final global_step_100 merged to Hugging Face safetensors.
—best_avg16/: checkpoint with the highest validation avg@16 during training, merged to Hugging Face safetensors.

Checkpoints

Checkpoint	Source step	Validation avg@16	best@16 / pass@16	maj@16
Root final	100	0.789286	0.855124	0.795124
`best_avg16/`	90	0.804167	0.855981	0.806176

Training Run

qwen3gen-chemistry-RLSD-Qwen-Qwen3-4B-mbs8-decay0-ema0.05-train256-rollout8-lr1e-6-vllm0.8

Base Model

—Base model: Qwen/Qwen3-4B
—Fine-tuning type: full-parameter FSDP RL training
—Dataset: datasets/sciknoweval/chemistry
—Train split: 1,890 examples
—Validation split: 210 examples

Method

—Method: RLSD
—Config: rlsd
—Policy loss mode: rlsd
—Reward: local SciKnowEval multiple-choice reward checker
—Rollout correction: token-level importance sampling, threshold 2.0

Hyperparameters

Field	Value
Base model	`Qwen/Qwen3-4B`
Training steps	100
Train batch size	256
Rollouts per prompt	8
Generations per step	2048
PPO mini batch size	8
Learning rate	`1e-6`
LR warmup steps	10
Weight decay	0.01
Grad clip	1.0
Max prompt length	2048
Max response length	8192
Max model length	10240
Train temperature	1.0
Train top_p	1.0
Validation generations	16
Validation temperature	0.6
Validation top_p	0.95
vLLM GPU memory utilization	0.8
GPUs	8 x NVIDIA H200
Save frequency	every 10 steps
Validation frequency	every 10 steps
Token reweight lambda	0.5
Token reweight eps_w	0.2
Token reweight decay steps	0
Teacher update rate	0.05
Max reprompt length	10240

Metrics

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CSV files:

—`training_score.csv`
—`validation_metrics.csv`

Metric	Value
Final training step	100
Final `critic/score/mean`	0.905762
Final `critic/rewards/mean`	0.905762
Final validation `avg@16`	0.789286
Peak validation `avg@16`	0.804167
Peak validation step	90

Loading

Root final checkpoint:

python

from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("SeongryongJung/Qwen3-4B-Chemistry-RLSD")
tokenizer = AutoTokenizer.from_pretrained("SeongryongJung/Qwen3-4B-Chemistry-RLSD")

Best avg@16 checkpoint:

python

model = AutoModelForCausalLM.from_pretrained("SeongryongJung/Qwen3-4B-Chemistry-RLSD", subfolder="best_avg16")
tokenizer = AutoTokenizer.from_pretrained("SeongryongJung/Qwen3-4B-Chemistry-RLSD", subfolder="best_avg16")

Intended Use

This model is intended for research on RL fine-tuning and self-distillation behavior on science/generalization tasks. It has not been broadly safety evaluated for production use.

Limitations

The reported scores are training-time and validation-time metrics from the local experimental setup. They should not be interpreted as broad benchmark results without independent evaluation.