Instructions to use BlossomsAI/petalRE-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BlossomsAI/petalRE-4B with Transformers:

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

pipe = pipeline("text-generation", model="BlossomsAI/petalRE-4B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BlossomsAI/petalRE-4B")
model = AutoModelForCausalLM.from_pretrained("BlossomsAI/petalRE-4B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BlossomsAI/petalRE-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BlossomsAI/petalRE-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BlossomsAI/petalRE-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BlossomsAI/petalRE-4B

SGLang

How to use BlossomsAI/petalRE-4B 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 "BlossomsAI/petalRE-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BlossomsAI/petalRE-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "BlossomsAI/petalRE-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BlossomsAI/petalRE-4B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BlossomsAI/petalRE-4B with Docker Model Runner:
```
docker model run hf.co/BlossomsAI/petalRE-4B
```

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petal-RE

A small, fast model that translates disassembly → C (asm → C). It does one job — structural translation — with no reasoning trace. Function naming, type recovery and analysis are delegated to a separate downstream model that reads petal-RE's C output plus the asm as context.

binary ─▶ disassembler ─▶ petal-RE (asm→C, tiny+local) ─▶ clean C ─▶ reasoning model

Model

Base: Qwen/Qwen3.5-4B-Base, re-tokenized to a compact 24,576-token asm/C BPE vocabulary (down from 248k — frees ~25-30% of params, no human-language tokens), embeddings warm-started via Fast Vocabulary Transfer.
Training: LoRA continued-pretraining (settle transplanted embeddings) → merge → LoRA SFT (asm→C, loss masked to the C). embed_tokens fully trained throughout.
Corpus: BlossomsAI/PetalRE-Corpus — Decompile-Bench (clean x86-64), repo-gen (clean multi-arch), obf-exebench + Tigress (obfuscated: control-flow flattening, MBA / encode-arithmetic).

Prompt format

petal-RE uses structured tags. The model is trained to emit C between <c> and </c>:

<src=asm><arch=x86_64><opt=O2><dec=raw>
<func>
<normalized AT&T assembly>
</func>
<c>

Generate until </c>. Calls are normalized to fn/fn0/fn1... and RIP-relative operands to REL(%rip) — symbol names are deliberately stripped, so the model recovers structure, not identifiers (that's the downstream model's job).

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

m = AutoModelForCausalLM.from_pretrained("BlossomsAI/petalRE-4B", torch_dtype=torch.bfloat16).cuda()
t = AutoTokenizer.from_pretrained("BlossomsAI/petalRE-4B")

asm = "push %rbp\nmov %rsp,%rbp\nmov %edi,%eax\nimul %edi,%eax\nadd %edi,%eax\npop %rbp\nret"
prompt = "<src=asm><arch=x86_64><opt=O2><dec=raw>\n<func>\n" + asm + "\n</func>\n<c>\n"

out = m.generate(**t(prompt, return_tensors="pt").to("cuda"),
                 max_new_tokens=512, do_sample=False,
                 stop_strings=["</c>"], tokenizer=t)
print(t.decode(out[0], skip_special_tokens=False))

Limitations

No identifier recovery. Input is symbol-stripped; the model produces the correct control-flow / call skeleton with placeholder names. Pair with a reasoning model that has symbol/context access for production decompilation.
Best on x86-64 and AArch64 (priority arches); other ISAs are lower-coverage.

Apache-2.0, same as the base model.

Downloads last month: -

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for BlossomsAI/petalRE-4B

Base model

Qwen/Qwen3.5-4B-Base

Finetuned

(266)

this model