Instructions to use imdatta0/qwen3-4b-swegym-moto-hardmulti-sft20k-teachergap-v1-adapter with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use imdatta0/qwen3-4b-swegym-moto-hardmulti-sft20k-teachergap-v1-adapter with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/mnt/disks/unslothai/datta0/.cache/hub/models--unsloth--Qwen3-4B-Instruct-2507/snapshots/992063681dc2f7de4ee976110199552935cad284") model = PeftModel.from_pretrained(base_model, "imdatta0/qwen3-4b-swegym-moto-hardmulti-sft20k-teachergap-v1-adapter") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Unsloth Studio
How to use imdatta0/qwen3-4b-swegym-moto-hardmulti-sft20k-teachergap-v1-adapter with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for imdatta0/qwen3-4b-swegym-moto-hardmulti-sft20k-teachergap-v1-adapter to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for imdatta0/qwen3-4b-swegym-moto-hardmulti-sft20k-teachergap-v1-adapter to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for imdatta0/qwen3-4b-swegym-moto-hardmulti-sft20k-teachergap-v1-adapter to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="imdatta0/qwen3-4b-swegym-moto-hardmulti-sft20k-teachergap-v1-adapter", max_seq_length=2048, )
Qwen3-4B SWE-Gym Moto Hardmulti Teacher-Gap V1 Adapter
This repository contains a PEFT LoRA adapter for unsloth/Qwen3-4B-Instruct-2507.
The adapter was trained as a bounded SFT continuation from the hard-multi 20k Qwen3-4B frontier adapter. The training mix preserved all 18 hard-multi rows and added 62 train-only teacher-gap rows selected from existing Coder-30B passing labels.
Evaluation
Held-out SWE-Gym Moto search/replace patch evaluation, 20k anchored retrieval context, bfloat16, sample seed 9012:
| adapter | context | seed | greedy | selected@1 | pass@8 | single pass@8 | multi pass@8 |
|---|---|---|---|---|---|---|---|
| hard-multi plus teacher-gap SFT | 20k | 9012 | 10/35 | 11/35 | 14/35 | 9/18 | 5/17 |
This was the first measured 4B checkpoint in this investigation with 5/17 multi-file pass@8 in a single seed, gaining moto-6641 versus the seed9012 hard-multi frontier. It is not promoted over the hard-multi frontier overall because overall pass@8 drops from 16/35 to 14/35.
Contents
adapter_model.safetensors: PEFT LoRA adapter weightsadapter_config.json: PEFT adapter configurationcheckpoint_metadata.json: local training metadata- tokenizer files and chat template copied with the checkpoint
The base model weights are not included.
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Model tree for imdatta0/qwen3-4b-swegym-moto-hardmulti-sft20k-teachergap-v1-adapter
Base model
Qwen/Qwen3-4B-Instruct-2507