Instructions to use raafatabualazm/decompiler-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raafatabualazm/decompiler-v2 with Transformers:

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

pipe = pipeline("text-generation", model="raafatabualazm/decompiler-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("raafatabualazm/decompiler-v2")
model = AutoModelForCausalLM.from_pretrained("raafatabualazm/decompiler-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use raafatabualazm/decompiler-v2 with PEFT:
```
Task type is invalid.
```
Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raafatabualazm/decompiler-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raafatabualazm/decompiler-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raafatabualazm/decompiler-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/raafatabualazm/decompiler-v2

SGLang

How to use raafatabualazm/decompiler-v2 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 "raafatabualazm/decompiler-v2" \
    --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": "raafatabualazm/decompiler-v2",
		"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 "raafatabualazm/decompiler-v2" \
        --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": "raafatabualazm/decompiler-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use raafatabualazm/decompiler-v2 with Docker Model Runner:
```
docker model run hf.co/raafatabualazm/decompiler-v2
```

decompiler-v2

This repository contains merged fine‑tuned weights for the base model Qwen/Qwen3-4B-Thinking-2507.

Task: idiomatic decompilation (assembly → high-level code)
Training: LoRA/DoRA adapters trained with TRL SFT on custom assembly→Dart/Swift pairs
How to load (merged):

from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "raafatabualazm/decompiler-v2"
tok = AutoTokenizer.from_pretrained(repo_id, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, torch_dtype="bfloat16", trust_remote_code=True)

Replace the repo id with your own if you fork or rename this repository.

Downloads last month: 8

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for raafatabualazm/decompiler-v2

Base model

Qwen/Qwen3-4B-Thinking-2507

Adapter

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