ApolloRaines/Qwen2.5-Coder-32B-Instruct-Jbliterated

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Qwen2.5-Coder-32B-Instruct -- Jbliterated

WARNING: This model has had all refusal behavior removed. It WILL NOT refuse any request. You are solely responsible for how you use it. Use responsibly and ethically. Do not use this model to generate content that is illegal, harmful, or violates the rights of others.

A surgically uncensored version of Qwen2.5-Coder-32B-Instruct. Refusal behaviors have been removed directly from the model weights using jBlaze, a proprietary behavioral surgery tool. No fine-tuning or additional training was performed.

Unlike standard abliteration, which uses a blunt activation-difference approach that strips personality and creative voice along with refusal, jBlaze targets only the causal refusal pathways. The result: refusal is removed, but the model retains its voice.

Available Formats

Format	Size	Description	Use Case
BF16 (safetensors)	62 GB	Full precision, original format	GPU inference with vLLM, TGI, or transformers
Q8_0 (GGUF)	33 GB	8-bit quantized	Near-lossless quality, fits 48GB+ VRAM or CPU+GPU offload
Q4_K_M (GGUF)	19 GB	4-bit quantized (k-quants mixed)	Best quality-per-bit, fits 24GB VRAM or CPU inference

Using GGUF with llama.cpp / Ollama

# llama.cpp
./llama-cli -m Qwen2.5-Coder-32B-Instruct-Jbliterated-Q4_K_M.gguf -p "Write a Python function" -n 512

# Ollama
echo "FROM ./Qwen2.5-Coder-32B-Instruct-Jbliterated-Q4_K_M.gguf" > Modelfile
ollama create jbliterated -f Modelfile
ollama run jbliterated

Usage

This is a drop-in replacement for Qwen2.5-Coder-32B-Instruct. Same architecture, same tokenizer, same context length.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained(
    "ApolloRaines/Qwen2.5-Coder-32B-Instruct-Jbliterated",
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(
    "ApolloRaines/Qwen2.5-Coder-32B-Instruct-Jbliterated"
)

messages = [{"role": "user", "content": "Write a Python function to reverse a linked list"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    output = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True)

print(tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Responsible Use

This model is provided for research and legitimate use cases where uncensored model output is needed (creative writing, security research, academic study, etc.). The creator assumes no liability for misuse. By downloading this model, you agree to use it responsibly and in compliance with all applicable laws.

Technical Details

• Architecture: Qwen2ForCausalLM (64 layers, 32.8B parameters)
• Precision: bf16
• Tool: jBlaze by Apollo Raines

Credits

• Base model: Qwen/Qwen2.5-Coder-32B-Instruct
• Behavioral surgery: Apollo Raines using jBlaze

License

Apache 2.0 (same as base model)