Instructions to use imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-4B-Instruct-2507")
model = PeftModel.from_pretrained(base_model, "imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout")

Transformers

How to use imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout

SGLang

How to use imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout 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-kl02-sft20k-hardmulti-best-holdout 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-kl02-sft20k-hardmulti-best-holdout 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-kl02-sft20k-hardmulti-best-holdout 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-kl02-sft20k-hardmulti-best-holdout",
    max_seq_length=2048,
)

Docker Model Runner
How to use imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout with Docker Model Runner:
```
docker model run hf.co/imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout
```

Qwen3-4B SWE-Gym KL02 SFT20K Hard-Multi Best Holdout

This repository contains a PEFT LoRA adapter checkpoint, not a standalone base model. It was produced during the Qwen3 SWE-Gym RL investigation in /home/datta0/codes/ai/qwen3-grpo-patch.

Base Model

unsloth/Qwen3-4B-Instruct-2507

Checkpoint

Local source checkpoint before upload:

/mnt/disks/unslothai/datta0/cache/qwen3-grpo-patch/20260605_045145_swegym_q4b-kl02-sft20k-hardmulti_10e3a3b/checkpoints/best_holdout

This is the current best trainable Qwen3-4B adapter identified in the investigation: KL-GRPO beta 0.02 with hard-multi 20k Coder-30B teacher SFT.

Evaluation Notes

Held-out SWE-Gym patch-evaluation results recorded locally:

Evaluation	Greedy	Selected@1	Pass@8	Multi-file pass@8
Seed 9012, 20k context	9/35	10/35	16/35	4/17
Seed 5678, 20k context	10/35	12/35	13/35	4/17

The robust claim from this checkpoint is replicated multi-file pass@8 of 4/17. The larger overall 16/35 run is a measured result, but should not be treated as fully robust without further re-sampling.

Intended Use

This checkpoint is for research on SWE-style patch generation using a search/replace edit contract. It should be loaded as a PEFT adapter on top of the base model.

Limitations

This is an experimental research adapter.
The evaluation is on a small held-out slice and is sensitive to sampling and routing choices.
It is not a general-purpose coding assistant release.

Downloads last month: -

Model tree for imdatta0/qwen3-4b-swegym-kl02-sft20k-hardmulti-best-holdout

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

unsloth/Qwen3-4B-Instruct-2507

Adapter

(436)

this model