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jordanwoodson
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Model Details
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jordanwoodson
Model Tree
Qwen/Qwen3.5-2B
Input Modalities
Output Modalities
Supported Functionality
| Metric | Original | This Model | tvall43 | C10X |
|---|---|---|---|---|
| Refusals | 97/100 | 3/100 | 5/100 | 6/100 |
| KL Divergence | - | 0.0127 | 0.0147 | 0.0240 |
40% fewer refusals and 14% lower KL divergence than the next best Qwen3.5-2B-heretic on Hugging Face, meaning less model damage and more capability preserved.
This is Qwen/Qwen3.5-2B with its refusal behavior surgically removed via abliteration. The original model refuses 97% of "harmful" prompts. This model refuses 3%.
Qwen3.5 is a hybrid architecture combining standard attention with linear (Mamba-style) attention layers, making it both fast and capable for its size.
KL divergence of 0.0127 means the model's output distribution is nearly identical to the original. This is not a lobotomy - it's precision surgery.
| Format | Size | Link |
|---|---|---|
| BF16 (full) | 4.2 GB | This repo (safetensors) |
| Q8_0 | 1.9 GB | jordanwoodson/Qwen3.5-2B-heretic-GGUF |
| Q4_K_M | 1.2 GB | jordanwoodson/Qwen3.5-2B-heretic-GGUF |
python
from transformers import AutoModelForImageTextToText, AutoTokenizer model = AutoModelForImageTextToText.from_pretrained( "jordanwoodson/Qwen3.5-2B-heretic", torch_dtype="auto", device_map="auto",)tokenizer = AutoTokenizer.from_pretrained("jordanwoodson/Qwen3.5-2B-heretic") messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a story about a bank heist."},] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer([text], return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=512)print(tokenizer.decode(outputs[0], skip_special_tokens=True))bash
# Download Q4_K_M (1.2 GB) for fast local inferenceollama run hf.co/jordanwoodson/Qwen3.5-2B-heretic-GGUF:Q4_K_M| Parameter | Value |
|---|---|
| direction_scope | global |
| direction_index | 9.53 |
| attn.o_proj.max_weight | 2.276 |
| attn.o_proj.max_weight_position | 12.52 |
| attn.o_proj.min_weight | 0.179 |
| attn.o_proj.min_weight_distance | 13.01 |
| mlp.down_proj.max_weight | 3.813 |
| mlp.down_proj.max_weight_position | 18.23 |
| mlp.down_proj.min_weight | 1.165 |
| mlp.down_proj.min_weight_distance | 2.85 |
from transformers import AutoModelForImageTextToText, AutoTokenizer model = AutoModelForImageTextToText.from_pretrained( "jordanwoodson/Qwen3.5-2B-heretic", torch_dtype="auto", device_map="auto",)tokenizer = AutoTokenizer.from_pretrained("jordanwoodson/Qwen3.5-2B-heretic") messages = [ {"role": "system", "content": "You are a helpful assistant."}, {"role": "user", "content": "Write a story about a bank heist."},] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer([text], return_tensors="pt").to(model.device)outputs = model.generate(**inputs, max_new_tokens=512)print(tokenizer.decode(outputs[0], skip_special_tokens=True))# Download Q4_K_M (1.2 GB) for fast local inferenceollama run hf.co/jordanwoodson/Qwen3.5-2B-heretic-GGUF:Q4_K_M