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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
Full GGUF ladder included — pick the quant that fits your card:
| Your GPU | Recommended quant | Weights |
|---|---|---|
| RTX 3060 / 4070 / 5070 (12 GB) | Q8_0 | 0.53 GB |
| RTX 4060 / 3070 (8 GB) | Q6_K | 0.51 GB |
| GTX 1660 Super / 2060 / 3050 laptop (6 GB) | Q5_K_M | 0.42 GB |
| CPU-only / Apple Silicon | Q4_K_M | 0.40 GB, fits in system RAM |
Weights only, at this model's ~0.5B native size; add ~1 GB for context. OOM? Drop one quant level. Headroom to spare? Go one up.
| 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 |
| 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 |
bash
# 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.
| mlp.down_proj.min_weight_distance | 13.14 |
Qwen2.5-Coder-0.5B-Instruct-heretic-Q4_K_M.gguf | GGUF, Q4_K_M | 379 MB |