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SriGoli0123
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Model Details
Model Provider
SriGoli0123
Model Tree
Qwen/Qwen3-4B-Base
Input Modalities
Output Modalities
Supported Functionality
The root model-*.safetensors files contain the merged actor weights from training step 409 of 409. They load directly with Transformers:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "SriGoli0123/Qwen3-4B-Base-GRPO-641644-bs32"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",)The merged export contains 4,022,468,096 bfloat16 parameters across 398 tensors. The final deterministic DAPO Math validation accuracy was 0.329 on 1,000 examples at step 409 (best during training: 0.345 at step 355).
Qwen/Qwen3-4B-Base906bfd4b4dc7f14ee4320094d8b41684abff8539a100/ioher-mistake-judge, commit d169b69)actor.ioh_sft_coef=0)data.train_batch_size): 32 promptsrollout.n): 8actor.ppo_mini_batch_size): 8 prompts (4 optimizer steps per training step)actor.ppo_micro_batch_size_per_gpu): 2rollout.log_prob_micro_batch_size_per_gpu): 2actor.use_dynamic_bsz, rollout.log_prob_use_dynamic_bsz): enabled (the per-GPU token budgets below determine the actual micro-batches)actor.ppo_max_token_len_per_gpu, rollout.log_prob_max_token_len_per_gpu): 24,576actor.ppo_epochs): 1data.max_prompt_length): 2,048 tokensdata.max_response_length): 4,096 tokens1e-6[0.9, 0.999]torch.compile: disabled-1)This is a research checkpoint. Differences of about ±1.5 points are within noise on the 1,000-example validation set. Evaluate the model on the intended benchmark and decoding configuration before downstream use.
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "SriGoli0123/Qwen3-4B-Base-GRPO-641644-bs32"tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",)