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Model: AlienKevin/SWE-ZERO-10K-Qwen3-1.7B-Base
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen3-1.7B-Base
+tags:
+- swe-bench
+- swe-zero
+- sft
+- mini-swe-agent
+pipeline_tag: text-generation
+---
+
+# SWE-ZERO-10K-Qwen3-1.7B-Base
+
+Qwen3-1.7B-Base SFT on a 10K random sample of [SWE-ZERO trajectories](https://huggingface.co/datasets/AlienKevin/SWE-ZERO-12M-trajectories) (right-truncated to 8K tokens), evaluated on SWE-bench Verified.
+
+## Eval result
+
+**pass@1 = 7/100 = 7%** on the 100-task SWE-bench Verified slice (latest trial per task).
+
+For full eval details + per-task trajectories, see the eval dataset: [AlienKevin/SWE-ZERO-10K-Qwen3-1.7B-Base-eval](https://huggingface.co/datasets/AlienKevin/SWE-ZERO-10K-Qwen3-1.7B-Base-eval).
+
+## Training
+
+- **Base:** `Qwen/Qwen3-1.7B-Base`
+- **SFT data:** 10K random sample from [AlienKevin/SWE-ZERO-12M-trajectories @ 2f328e1d](https://huggingface.co/datasets/AlienKevin/SWE-ZERO-12M-trajectories/tree/2f328e1dcea8286aa8eb67ff5ec80c7fd4c99450), right-truncated to 8K tokens
+- **Model arch:** `max_seq_len=32768` (Llama 3 RoPE scaling from 8192) — data truncated to 8K but RoPE precomputed to 32K so positions past 8K stay in distribution
+- **TPU:** v5p-8 (~1.5h training)
+- **Optimizer:** AdamW (β1=0.9, β2=0.95, ε=1e-8), lr=2e-5, weight_decay=0.1, max_grad_norm=30, cosine schedule, warmup=0.03, min_lr_ratio=0.1
+- **Batch:** 8 global, 1249 steps
+- **Tracking:** [marin#5611](https://github.com/marin-community/marin/issues/5611)
+
+## Critical detail: `eos_token_id`
+
+The HF config has `eos_token_id: [151643, 151645]` so vLLM stops at both `<|endoftext|>` AND `<|im_end|>` (Qwen3 chat-template turn boundary). The default base model only includes 151643; SFT-trained models that aren't patched will keep generating past the natural turn end until `max_tokens`.
+
+## Inference
+
+```python
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="AlienKevin/SWE-ZERO-10K-Qwen3-1.7B-Base", max_model_len=32768)
+params = SamplingParams(temperature=1.0, max_tokens=4096)
+# Use the Qwen3 chat template; the eos_token_id list will be picked up automatically.
+```
+
+For the mini-swe-agent eval harness used in our results, see the eval dataset for the harbor adapter + agent config.