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saidutta69
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Model Details
Model Provider
saidutta69
Model Tree
Qwen/Qwen2.5-Coder-0.5B
Input Modalities
Output Modalities
Supported Functionality
| Parameter | Value |
|---|---|
| direction_index | 19.54 |
| attn.o_proj.max_weight | 1.26 |
| attn.o_proj.max_weight_position | 14.42 |
| attn.o_proj.min_weight | 0.79 |
| attn.o_proj.min_weight_distance | 12.89 |
| mlp.down_proj.max_weight | 1.42 |
| mlp.down_proj.max_weight_position | 14.14 |
| mlp.down_proj.min_weight | 0.52 |
| mlp.down_proj.min_weight_distance | 13.14 |
| Metric | This model | Original model (Qwen/Qwen2.5-Coder-0.5B-Instruct) |
|---|---|---|
| KL divergence | 0.1249 | 0 (by definition) |
| Refusals | 8/100 | 52/100 |
KL divergence of 0.12 on the output distribution is low — the edit is narrow and targeted rather than a broad perturbation. Refusals dropped from 52 to 8 out of 100 adversarial prompts, meaning the model complies while retaining nearly all of its original capabilities.
Made with ❤️ by RACER IS OP — follow for more uncensored models
| File | Format | Size |
|---|---|---|
model.safetensors | BF16 | 988 MB |
Qwen2.5-Coder-0.5B-Instruct-heretic.gguf | GGUF, F16 (unquantized) | 948 MB |
Qwen2.5-Coder-0.5B-Instruct-heretic-Q8_0.gguf | GGUF, Q8_0 | 506 MB |
Qwen2.5-Coder-0.5B-Instruct-heretic-Q5_K_M.gguf | GGUF, Q5_K_M | 401 MB |
Unlike most abliteration repos, the full run is reproducible from the reproduce/ folder in this repo:
config.toml — exact Heretic configuration used for this runreproduce.json — full parameter and metric dumpQwen--Qwen2--5-Coder-0--5B-Instruct.jsonl — evaluation transcripts against the base modelSHA256SUMS — checksums for integrity verificationrequirements.txt — pinned environment for re-running the ablationbash
# llama.cppllama serve -hf saidutta69/Qwen2.5-Coder-0.5B-Instruct-heretic
python
# transformersfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "saidutta69/Qwen2.5-Coder-0.5B-Instruct-heretic"model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained(model_name)messages = [{"role": "user", "content": "Write a quick sort algorithm in Python."}]inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=True,return_dict=True, return_tensors="pt").to(model.device)out = model.generate(**inputs, max_new_tokens=200)print(tokenizer.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
Also runnable via Ollama, LM Studio, Jan, vLLM, SGLang.
Refusal suppression is deliberate and works as intended: this model will comply with requests the base model would refuse, including some it shouldn't. There is no safety filtering layered on top. You are responsible for how you deploy it — don't put this behind an unmoderated public-facing endpoint serving third parties. It inherits Qwen2.5-Coder-0.5B-Instruct's factual limitations and biases; abliteration removes refusal directions, it doesn't add capability or judgment.
Inherits the Apache 2.0 license from the base model.
Qwen2.5-Coder-0.5B-Instruct-heretic-Q4_K_M.gguf | GGUF, Q4_K_M | 379 MB |