初始化项目，由ModelHub XC社区提供模型

Model: GMatherne/qwen3-8b-human-sft
Source: Original Platform

README.md

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+---
+license: apache-2.0
+base_model: unsloth/Qwen3-8B
+tags:
+  - qwen3
+  - qlora
+  - unsloth
+  - educational
+  - human-sounding
+language:
+  - en
+pipeline_tag: text-generation
+---
+
+# Qwen3-8B — Human-Sounding Educational Tutor (QLoRA)
+
+A QLoRA fine-tune of **Qwen3-8B** (non-thinking) trained to answer everyday
+educational/tutoring questions in **natural, human-sounding prose** — text that an
+AI-text detector (Pangram) reads as human-written rather than AI-generated.
+
+**This is a course/research artifact demonstrating "behavior from data," not a
+production tutor.** The target behavior is *voice*, and (see Evaluation) that voice
+is bought at a real cost in factual accuracy versus the untuned base — so do **not**
+use it as a source of factual authority.
+
+## What it does
+
+Given a student's question — explain a concept, help with an essay, correct a
+mistake — it responds the way a knowledgeable human would in a forum answer:
+conversational, direct, first-person, without the padding, hedging, list-scaffolding,
+and even pacing that give default AI writing away. It answers as an AI assistant (it
+never claims to be a person) and does not cite sources or add sign-offs.
+
+## Training
+
+- **Base:** `unsloth/Qwen3-8B`, run in **non-thinking** mode (`enable_thinking=False`).
+- **Method:** QLoRA (4-bit) via [Unsloth](https://github.com/unslothai/unsloth) —
+  LoRA `r=32`, `alpha=32`, dropout 0, all 7 attention+MLP projections; 2 epochs,
+  lr `2e-4`, ChatML template, `train_on_responses_only`.
+- **Data:** ~1,800 curated, heavily-cleaned **human** answers from StackExchange and
+  Reddit (ELI5 / AskScience / AskHistorians / topic SE sites), filtered to remove
+  forum scaffolding, fabricated citations, links, markdown, and non-English text.
+  The dataset is the real deliverable; training is a downstream button-press.
+- Two variants: **SFT** (the fine-tune) and **DPO** (a preference-tuning pass on top).
+
+## Evaluation (honest)
+
+LLM-as-judge (GPT-5) on 16 held-out education prompts, base vs tuned:
+
+| Model | Accuracy /5 | Info retention | Answers w/ ≥1 error | Confident fabrications |
+|---|---|---|---|---|
+| Qwen3-8B base (untuned) | **4.56** | 65% | 3/16 | 1 |
+| This model — SFT | 3.25 | 64% | 11/16 | 12 |
+| This model — DPO | 2.88 | 56% | 15/16 | 11 |
+
+**Human-ness (Pangram AI-detector): _[fill in after scoring]_** — this is the metric
+the model is optimized for; the base reads as ~100% AI.
+
+**Read this honestly:** fine-tuning for the human voice **lowers factual accuracy and
+raises fabrication rate** relative to the strong base model. It also degrades
+instruction-following on terse/format-constrained tasks (write code, "answer in one
+word", JSON) — it tends to over-explain. The defensible result is *reliable human
+voice in a small local model*, not raw capability.
+
+## Intended use & limitations
+
+- **Use:** research/education on data-driven behavior control; generating
+  human-reading educational prose where factual precision is not critical.
+- **Do not use** for authoritative facts, math, code, or format-constrained output.
+- May still occasionally read as AI on some prompts; may state confident but wrong
+  claims (a "forum register" side-effect).
+
+## Usage
+
+**Ollama (GGUF):**
+```
+ollama create qwen3-human -f Modelfile   # Modelfile FROM ./qwen3-human*.Q8_0.gguf
+ollama run qwen3-human
+```
+
+**Transformers:** load the merged weights and apply the ChatML chat template with the
+system prompt below; sample at `temperature=0.7, top_p=0.8, top_k=20,
+repetition_penalty=1.15`.
+
+**System prompt used in training/inference:**
+> You are a helpful AI tutor. You explain concepts clearly and accurately, covering
+> the actual mechanism or reasoning rather than a vague summary. You help with writing
+> and correct mistakes, in natural, plain language. You are an AI assistant — you never
+> claim to be a human, a person, or a product. Never cite specific studies, papers,
+> books, articles, authors, DOIs, or web links, and never invent a source or title —
+> explain things in your own words. Do not end with sign-offs like "hope this helps."
+
+## License
+
+Apache-2.0, inheriting the base model (`unsloth/Qwen3-8B`).