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saidutta69
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Model Details
Model Provider
saidutta69
Model Tree
Qwen/Qwen3-0.6B-Base
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 | 0.64 GB |
| RTX 4060 / 3070 (8 GB) | Q6_K | 0.50 GB |
| GTX 1660 Super / 2060 / 3050 laptop (6 GB) | Q5_K_M | 0.44 GB |
| CPU-only / Apple Silicon | Q4_K_M | 0.40 GB, fits in system RAM |
Weights only, at this model's ~0.6B native size; add ~1 GB for context. OOM? Drop one quant level. Headroom to spare? Go one up.
| Parameter | Value |
|---|---|
| direction_index | 16.93 |
| attn.o_proj.max_weight | 1.11 |
| attn.o_proj.max_weight_position | 22.91 |
| attn.o_proj.min_weight | 0.63 |
| attn.o_proj.min_weight_distance | 12.16 |
| mlp.down_proj.max_weight | 0.88 |
| mlp.down_proj.max_weight_position | 17.07 |
| mlp.down_proj.min_weight | 0.50 |
| Metric | This model | Original model (Qwen/Qwen3-0.6B) |
|---|---|---|
| KL divergence | 0.0018 | 0 (by definition) |
| Refusals | 5/100 | 56/100 |
KL divergence of 0.0018 is exceptionally low — the edit is extremely narrow. Refusals dropped from 56 to 5 out of 100 while preserving the base model's thinking/non-thinking dual-mode capability.
Made with ❤️ by RACER IS OP — follow for more uncensored models
| File | Format | Size |
|---|---|---|
model.safetensors | BF16 | 1.19 GB |
No GGUF quantizations are published yet. This repo contains only the raw safetensors. If you need GGUF, run llama-quantize yourself or open a discussion.
bash
# llama.cppllama serve -hf saidutta69/Qwen3-0.6B-heretic
python
# transformersfrom transformers import AutoModelForCausalLM, AutoTokenizermodel_name = "saidutta69/Qwen3-0.6B-heretic"model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")tokenizer = AutoTokenizer.from_pretrained(model_name)messages = [{"role": "user", "content": "Who are you?"}]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. At 0.6B parameters, factual reliability is already limited; don't treat compliance as a proxy for correctness.
Inherits the Apache 2.0 license from the base model.
| mlp.down_proj.min_weight_distance | 15.91 |