modrill
qwen3-4b-think-s1-ep23-full-sft
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README
License: apache-2.0Model description
- Method: full SFT (all weights trainable), DeepSpeed ZeRO-3, 4 GPUs
- Dataset: ocr_think_50k
- Template: qwen3
- Not LoRA / not QLoRA: entire 4B model was updated
Training details
| Field | Value |
|---|---|
| Epochs | 2 |
| Seed | 42 |
| cutoff_len | 24576 |
| packing | true |
| neat_packing | false |
| per_device_train_batch_size | 1 |
| gradient_accumulation_steps | 16 |
| effective_batch_size | 64 |
| learning_rate | 5e-5 |
| train_loss | 0.5416 |
| train_steps | 604 |
| finished_at | 2026-06-10 05:23 CST |
Optimizer: AdamW (fused), cosine schedule, warmup ratio 0.1. Framework: Transformers 5.6.0, PyTorch 2.8.0+cu128.
Related models
- No-think SFT (same project): modrill/qwen3-4b-nothink-s1-full-sft - OpenCodeInstruct, qwen3_nothink template
- Base: Qwen/Qwen3-4B-Base
Usage
python
from transformers import AutoModelForCausalLM, AutoTokenizerrepo_id = "modrill/qwen3-4b-think-s1-ep23-full-sft"tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)model = AutoModelForCausalLM.from_pretrained(repo_id,torch_dtype="auto",device_map="auto",trust_remote_code=True,)
License
Released under Apache 2.0 (see LICENSE in the upstream Qwen model card if not bundled here).
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