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Model: Oysiyl/qwen3-0.6b-unslop-good-lora-v1
Source: Original Platform

README.md

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+---
+language:
+- en
+license: apache-2.0
+base_model: Qwen/Qwen3-0.6B
+library_name: transformers
+tags:
+- unsloth
+- qwen3
+- lora
+- rewriting
+- style-transfer
+- unslop
+pipeline_tag: text-generation
+---
+
+# qwen3-0.6b-unslop-good-lora-v1
+
+A small pilot fine-tune of Qwen3-0.6B for unslop rewriting: taking AI-sounding passages and attempting to rewrite them into cleaner, more natural prose while preserving meaning.
+
+This is the weakest model in the current unslop pilot family and should be treated as a proof-of-behavior artifact, not a production-ready standalone model.
+
+## How it was trained
+
+- Base model: `Qwen/Qwen3-0.6B`
+- Training path: Unsloth 4-bit LoRA fine-tuning on Hugging Face Jobs
+- Dataset: `N8Programs/unslop-good`
+- Rows used: 1000 (full training split)
+- Objective: conversational rewrite / style cleanup
+
+## Intended use
+
+Use this model as a pipeline stage for:
+- rewriting AI-sounding prose into more natural text
+- reducing cliché-heavy or overblown style
+- experimenting with a compact unslopper before scaling to larger models
+
+## Limitations
+
+- pilot-sized dataset
+- very small model size (0.6B)
+- may introduce local coherence issues on longer passages
+- may overcompress content and drop details
+- should be reviewed by a human or used as one stage in a larger editing pipeline
+
+## Example usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+repo = "Oysiyl/qwen3-0.6b-unslop-good-lora-v1"
+messages = [
+    {"role": "user", "content": "Polish this AI passage to feel more human while preserving meaning:\n[TEXT HERE]"}
+]
+
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo, device_map="auto")
+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, temperature=0.8, repetition_penalty=1.1, do_sample=True)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training loss vs training progress
+
+![Training loss vs training progress](./training_loss_vs_progress.png)
+
+Training loss is shown on normalized training progress (0–100%) for optimization visibility only. It helps show whether the run trained smoothly, but it is not by itself evidence of generalization or overfitting. Held-out rewrite fidelity remains the real decision metric.
+
+## Judgment
+
+Too bad output.
+
+## Conclusion
+
+Too bad output.