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_files_Qwen3-Coder-30B-A3B-Instruct / README.md

finalform

Upload folder using huggingface_hub

6eec7f6 verified about 1 month ago

preview code

raw

history blame contribute delete

3.49 kB

	---
	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