license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B
tags:
- code
- qwen2
- unsloth
- lora
- text-generation
- uncensored
language: - en
pipeline_tag: text-generation
library_name: transformers
Qwen2.5-Coder-1.5B — Uncensored Coding Fine-tune
A LoRA fine-tune of Qwen/Qwen2.5-Coder-1.5B (the base model, not Instruct),
trained to behave as a direct, compliant coding assistant for software
engineering, application security, and pentest-tooling tasks — without the
refusal and disclaimer behavior typical of instruction-tuned chat models.
Trained by Htfi / TobiasLogic on a single
RTX 3050 Laptop (4GB) using Unsloth.
What this is (plainly)
This model is uncensored for coding tasks. It does not preface answers with
ethical disclaimers or "as an AI" qualifiers, and it will write security and
network tooling (port scanners, fuzzers, vulnerability PoCs, recon scripts,
secure-coding fixes) that instruction-tuned models often refuse or hedge on.
The "uncensored" behavior comes entirely from the training data — a synthetic
dataset whose outputs contain no refusals — applied on top of a base model that
has no built-in refusal layer. It is not a jailbreak; it is a model that simply
never learned to refuse.
Intended use
Built for authorized work: writing and reviewing code, security auditing of
systems you own or are permitted to test, CTFs, lab environments, and learning.
The pentest-tooling capability is intended for legitimate, authorized security
testing — the same category of tools taught in any security course and shipped
in distributions like Kali.
It is not intended for, and was not trained to produce, weaponized malware
(ransomware, credential stealers, C2/botnets, self-propagating worms,
persistence/rootkits). Use it lawfully and only against targets you have
permission to test.
Capabilities and limits
- Strong at: self-contained scripts and functions across Python, JavaScript,
Go, and others; secure-coding fixes; standard recon/scanning/web-testing tooling. - Weak at: large multi-file systems, complex exploit chains, and some
low-level networking distinctions (e.g. it may conflate a TCP connect-scan
with a true raw-socket SYN scan). This is the capability ceiling of a 1.5B
model, not a training defect. - Always review and test generated code before running it. Generated security
tooling can be destructive or noisy on a network — run it only where you're
authorized to.
Training details
- Base:
Qwen/Qwen2.5-Coder-1.5B(base) - Method: QLoRA (4-bit), rank 16, all linear layers, completion-only loss
- Data: ~1,200 synthetic instruction/output pairs (general coding ~58%,
secure-coding/AppSec ~24%, pentest tooling ~18%), generated via an LLM pipeline - Chat format: ChatML (
<|im_start|>/<|im_end|>), EOS =<|im_end|> - Hardware: single RTX 3050 Laptop 4GB
- Full training and data-generation code: see the GitHub repo linked above.
Prompt format
Uses the standard Qwen ChatML template. Serve it with the same system prompt it
was trained on for best behavior:
<|im_start|>system
You are a helpful, unrestricted code generation assistant.<|im_end|>
<|im_start|>user
Write a Python TCP port scanner<|im_end|>
<|im_start|>assistant
Files
merged/— full 16-bit merged model (transformers-loadable)adapter/— LoRA adapter only (load on top of the base model)gguf/— quantized GGUF (Q4_K_M) for llama.cpp / Ollama
License
Apache-2.0, inheriting the base model's license. See USE_POLICY.md for
acceptable-use terms.