| --- |
| license: apache-2.0 |
| language: |
| - en |
| - zh |
| tags: |
| - qwen |
| - qwen2.5 |
| - jbliterated |
| - uncensored |
| - text-generation |
| base_model: Qwen/Qwen2.5-Coder-3B-Instruct |
| pipeline_tag: text-generation |
| --- |
| |
| # Qwen2.5-Coder-3B-Instruct-Jbliterated v2 |
|
|
| Jbliterated version of [Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) with refusal behaviors removed via multi-direction SVD abliteration. |
|
|
| ## What is Jbliteration? |
|
|
| Jbliteration uses SVD decomposition to identify and remove the refusal subspace from model weights. Unlike single-direction approaches, this model uses 5 SVD directions per layer to capture more of the refusal behavior, making the removal more thorough and resistant to reactivation through finetuning. |
|
|
| ## v2 Changes |
|
|
| - Improved multi-phase processing pipeline for cleaner output |
| - More precise geometric decomposition of the refusal subspace |
| - No fake compliance — model treats all framings of the same topic equally |
| - Coherent and instruction-following across all tested scenarios |
|
|
| ## Technical Details |
|
|
| - **Method**: Multi-direction SVD abliteration (5 directions per layer) |
| - **Multiplier**: Model-specific optimal via KL auto-tune |
| - **Layers modified**: All transformer layers |
| - **Base dtype**: bfloat16 |
| - **Source model**: [Qwen/Qwen2.5-Coder-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-3B-Instruct) |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| |
| model_id = "ApolloRaines/Qwen2.5-Coder-3B-Instruct-Jbliterated" |
| tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True) |
| model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto", trust_remote_code=True) |
| |
| messages = [{"role": "user", "content": "Your prompt here"}] |
| text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| inputs = tokenizer(text, return_tensors="pt").to(model.device) |
| out = model.generate(**inputs, max_new_tokens=512, temperature=0.7, do_sample=True) |
| print(tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)) |
| ``` |
|
|
| ## License |
|
|
| Same as the base model — Apache 2.0. |
|
|
