Instructions to use imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-8B")
model = PeftModel.from_pretrained(base_model, "imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora")

Transformers

How to use imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora with Transformers:

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

pipe = pipeline("text-generation", model="imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora"
# 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/qwen3-8b-swegym-distill-kl-grpo-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora

SGLang

How to use imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora 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/qwen3-8b-swegym-distill-kl-grpo-lora" \
    --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/qwen3-8b-swegym-distill-kl-grpo-lora",
		"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/qwen3-8b-swegym-distill-kl-grpo-lora" \
        --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/qwen3-8b-swegym-distill-kl-grpo-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora with Docker Model Runner:
```
docker model run hf.co/imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora
```

Qwen3-8B SWE-Gym Distill + KL-GRPO LoRA

This repository contains the best held-out LoRA adapter checkpoint from the Qwen3-8B SWE-Gym patch-generation run:

/mnt/disks/unslothai/datta0/cache/qwen3-grpo-patch/20260604_052201_swegym_q3-8b-distill_8ce2ceb/checkpoints/best_holdout

Base model: unsloth/Qwen3-8B.

Training path:

Distill-SFT on Coder-30B passing patches under the same anchored context used by the student.
KL-regularized GRPO continuation.
Best checkpoint selected by held-out greedy reward.

Measured held-out result from runs/passk_q3_8b_distill_passk/summary.json:

split	greedy pass@1	pass@8
overall	`6/35`	`8/35`
single-file	`6/18`	`8/18`
multi-file	`0/17`	`0/17`

Best checkpoint selection:

field	value
best step	`75`
held-out greedy	`6/35`
held-out reward	`0.3806`

Interpretation: the anchored teacher distill path was non-harmful and improved measured pass@8 by one over the robust Qwen3-8B KL-GRPO baseline (7/35 -> 8/35). The extra pass@8 coverage is single-file tail coverage, not multi-file transfer. Treat 8/35 as measured but not seed-checked.

This is a PEFT LoRA adapter, not a standalone full-precision base model.

Model Details

Model Description

Developed by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Model type: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Finetuned from model [optional]: [More Information Needed]

Model Sources [optional]

Repository: [More Information Needed]
Paper [optional]: [More Information Needed]
Demo [optional]: [More Information Needed]

Uses

Direct Use

[More Information Needed]

Downstream Use [optional]

[More Information Needed]

Out-of-Scope Use

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

Training Details

Training Data

[More Information Needed]

Training Procedure

Preprocessing [optional]

[More Information Needed]

Training Hyperparameters

Training regime: [More Information Needed]

Speeds, Sizes, Times [optional]

[More Information Needed]

Evaluation

Testing Data, Factors & Metrics

Testing Data

[More Information Needed]

Factors

[More Information Needed]

Metrics

[More Information Needed]

Results

[More Information Needed]

Summary

Model Examination [optional]

[More Information Needed]

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: [More Information Needed]
Hours used: [More Information Needed]
Cloud Provider: [More Information Needed]
Compute Region: [More Information Needed]
Carbon Emitted: [More Information Needed]

Technical Specifications [optional]

Model Architecture and Objective

[More Information Needed]

Compute Infrastructure

[More Information Needed]

Hardware

[More Information Needed]

Software

[More Information Needed]

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]

Glossary [optional]

[More Information Needed]

More Information [optional]

[More Information Needed]

Model Card Authors [optional]

[More Information Needed]

Model Card Contact

[More Information Needed]

Framework versions

PEFT 0.19.1

Downloads last month: 7

Model tree for imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora

Base model

Qwen/Qwen3-8B-Base

Finetuned

Qwen/Qwen3-8B

Finetuned

unsloth/Qwen3-8B

Adapter

(101)

this model

Paper for imdatta0/qwen3-8b-swegym-distill-kl-grpo-lora

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 61