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 Settings

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_files_Qwen3-Coder-30B-A3B-Instruct

File size: 3,487 Bytes

6eec7f6

---
library_name: peft
license: other
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
tags:
- base_model:adapter:Qwen/Qwen3-Coder-30B-A3B-Instruct
- llama-factory
- lora
- transformers
metrics:
- accuracy
pipeline_tag: text-generation
model-index:
- name: rewrite_results
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# rewrite_results

This model is a fine-tuned version of [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/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