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Model Details
Model Provider
ubicloud
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Base
Qwen/Qwen3-14B
Adapter
this model
Input Modalities
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Output Modalities
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Supported Functionality
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SWE-Qwen3-14B is a LoRA fine-tuned SWE agent model based on Qwen3-14B, trained on 20K filtered trajectories from SWE-Star collected under a modified OpenHands scaffold.
🔗 Data: SWE-Openhands-Devstral-32k-20K
| Item | Value |
|---|---|
| Base Model | Qwen3-14B |
| PEFT | LoRA (rank=32, alpha=64, dropout=0.1) |
| Target Modules | All linear layers (q/k/v/o/up/down/gate_proj) |
| Training Data | 20K filtered SWE-Star trajectories |
| Max Context | 32,768 tokens |
| Epochs | 2 (1,250 steps) |
| Batch Size | 16 (micro=1/GPU, grad_accum=8) |
| Learning Rate | 1e-4, cosine, warmup 5% |
| History Truncation | keep_fraction=0.5 |
| Hardware | 2× GPU (FSDP2) |
Context length and history truncation. We use a maximum context length of 32,768 tokens. Since many agent trajectories exceed this limit, we enable history truncation with a keep fraction of 0.5: when a trajectory exceeds the window, the oldest turns are dropped while preserving the most recent 50%. This ensures the model always sees the most relevant context (recent edits, test results, error messages) rather than losing the tail end, which typically contains the final fix and submission.
python
from peft import PeftModelfrom transformers import AutoModelForCausalLM, AutoTokenizerbase_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-14B", torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-14B")model = PeftModel.from_pretrained(base_model, "ubicloud/SWE-Qwen3-14B")