Instructions to use finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct")
model = PeftModel.from_pretrained(base_model, "finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct")

Transformers

How to use finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct

SGLang

How to use finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct 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 "finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct" \
    --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": "finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct",
		"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 "finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct" \
        --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": "finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct with Docker Model Runner:
```
docker model run hf.co/finalform/rewrite_files_Qwen3-Coder-30B-A3B-Instruct
```

rewrite_results

This model is a fine-tuned version of Qwen/Qwen3-Coder-30B-A3B-Instruct on the train dataset. It achieves the following results on the evaluation set:

Loss: 0.0616
Accuracy: 0.9850

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0004
train_batch_size: 4
eval_batch_size: 2
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 2
total_train_batch_size: 32
total_eval_batch_size: 8
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.085
num_epochs: 4.0

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
No log	0.1481	2	0.2477	0.9611
0.2441	0.2963	4	0.1997	0.9648
0.2367	0.4444	6	0.1587	0.9688
0.2367	0.5926	8	0.1476	0.9710
0.1895	0.7407	10	0.1318	0.9732
0.1361	0.8889	12	0.1172	0.9759
0.1361	1.0	14	0.1053	0.9783
0.18	1.1481	16	0.0985	0.9792
0.1193	1.2963	18	0.0932	0.9798
0.1193	1.4444	20	0.0875	0.9804
0.0823	1.5926	22	0.0840	0.9806
0.1175	1.7407	24	0.0778	0.9814
0.1175	1.8889	26	0.0737	0.9827
0.0898	2.0	28	0.0704	0.9836
0.0948	2.1481	30	0.0689	0.9838
0.0948	2.2963	32	0.0670	0.9841
0.0739	2.4444	34	0.0653	0.9841
0.0526	2.5926	36	0.0643	0.9845
0.0526	2.7407	38	0.0634	0.9845
0.0564	2.8889	40	0.0625	0.9847
0.0678	3.0	42	0.0616	0.9850
0.0678	3.1481	44	0.0615	0.9852
0.0499	3.2963	46	0.0616	0.9853
0.0437	3.4444	48	0.0620	0.9853
0.0437	3.5926	50	0.0621	0.9851
0.0421	3.7407	52	0.0624	0.9852
0.0557	3.8889	54	0.0624	0.9852
0.0557	4.0	56	0.0623	0.9851

Framework versions

PEFT 0.17.1
Transformers 4.57.1
Pytorch 2.10.0+cu128
Datasets 4.0.0
Tokenizers 0.22.2

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