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README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: mlx
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3.5-9B
+base_model_relation: adapter
+tags:
+- mlx
+- qwen
+- qwen3.5
+- lora
+- adapter
+- sft
+- unity
+- documentation
+- downftuner
+---
+
+# Qwen3.5-9b-UnityEngine
+
+A LoRA adapter for [`Qwen/Qwen3.5-9B`](https://huggingface.co/Qwen/Qwen3.5-9B)
+fine-tuned with SFT on **Unity Engine** documentation. The base model is
+unchanged — this repo contains only the adapter weights, so you load the
+base separately and apply the adapter at inference time.
+
+## What this model does
+
+Specialises Qwen/Qwen3.5-9B for Unity Engine-specific questions, quoting API
+identifiers, configuration keys, file paths, and version-specific details
+verbatim from the official documentation. It is not a general chat model
+— for free-form conversation, the unadorned base handles that better.
+
+## How it was built
+
+Trained using **DownFTuner**, a custom local fine-tuning platform built
+by [jokernifty](https://huggingface.co/jokernifty). DownFTuner is a
+Next.js + Appwrite control plane wrapping `mlx-lm` for Apple Silicon
+training. The pipeline behind this model:
+
+1. **Scrape** — official Unity Engine docs crawled via sitemap + BFS, main
+   content extracted with trafilatura, written one page per PDF.
+2. **Synthesise** — DeepSeek-V4-Pro (Ollama Cloud) prompted with each
+   chunk under a strict JSON schema (`{examples: [{user, assistant}, …]}`)
+   to produce grounded instruction-answer pairs. An EmbeddingGemma dedup
+   + hallucination filter ran over the full set.
+3. **Train** — SFT LoRA via `mlx_lm.lora` on Apple Silicon, against a
+   4-bit MLX copy of the base (mlx-community/Qwen3.5-9B-MLX-4bit).
+4. **Eval** — internal eval suite scoring EM / Tok-F1 / BLEU / ROUGE-L /
+   chrF / Contain / Embed / BERTScore plus an LLM-as-judge verdict.
+5. **Publish** — the adapter weights and this card uploaded here.
+
+Hardware: Apple M2 Pro, 24 GB unified memory.
+
+DownFTuner is currently a private internal tool of jokernifty. If you'd
+like access or want to discuss the pipeline, open a discussion on this
+model.
+
+## Usage
+
+### With MLX (Apple Silicon, recommended)
+
+```python
+from mlx_lm import load, generate
+
+model, tokenizer = load(
+    "mlx-community/Qwen3.5-9B-MLX-4bit",
+    adapter_path="jokernifty/Qwen3.5-9b-UnityEngine",
+)
+
+print(generate(
+    model, tokenizer,
+    prompt="<your Unity Engine question here>",
+    max_tokens=400,
+))
+```
+
+### With transformers + PEFT (any platform)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+base = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen3.5-9B", dtype=torch.bfloat16, device_map="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3.5-9B")
+model = PeftModel.from_pretrained(base, "jokernifty/Qwen3.5-9b-UnityEngine")
+
+inputs = tokenizer.apply_chat_template(
+    [{"role": "user", "content": "<your Unity Engine question here>"}],
+    add_generation_prompt=True, return_tensors="pt",
+).to(model.device)
+out = model.generate(inputs, max_new_tokens=400)
+print(tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+### As a fused checkpoint
+
+If you'd rather have a single self-contained model:
+
+```bash
+python -m mlx_lm.fuse \
+  --model mlx-community/Qwen3.5-9B-MLX-4bit \
+  --adapter-path jokernifty/Qwen3.5-9b-UnityEngine \
+  --save-path ./Qwen3.5-9b-UnityEngine-fused
+```
+
+## Limitations
+
+- Knowledge is bounded by the documentation snapshot used for training.
+  Newer API additions or removals after that date are not reflected.
+- Like the base model, this adapter can confabulate confidently. Always
+  verify code examples against the current upstream docs before shipping.
+- The adapter is LoRA only — for tasks outside Unity Engine, you'll see no
+  improvement (and possibly slight regression) versus the base.
+
+## License
+
+Apache 2.0, inherited from the Qwen/Qwen3.5-9B base. Built by
+[jokernifty](https://huggingface.co/jokernifty) using DownFTuner. Please
+credit the base model and this adapter when you use it.

adapter_config.json

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+{
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+    "batch_size": 1,
+    "clear_cache_threshold": 0,
+    "config": null,
+    "data": "/Volumes/SSD1TB/DownFTuner/.downftuner/runs/6a1dc9d500277b50ec06",
+    "fine_tune_type": "lora",
+    "grad_accumulation_steps": 1,
+    "grad_checkpoint": true,
+    "iters": 2000,
+    "learning_rate": 0.0001,
+    "lora_parameters": {
+        "rank": 8,
+        "dropout": 0.0,
+        "scale": 20.0
+    },
+    "lr_schedule": null,
+    "mask_prompt": false,
+    "max_seq_length": 2048,
+    "model": "mlx-community/Qwen3.5-9B-MLX-4bit",
+    "num_layers": 16,
+    "optimizer": "adam",
+    "optimizer_config": {
+        "adam": {},
+        "adamw": {},
+        "muon": {},
+        "sgd": {},
+        "adafactor": {}
+    },
+    "project_name": null,
+    "report_to": null,
+    "resume_adapter_file": null,
+    "save_every": 100,
+    "seed": 0,
+    "steps_per_eval": 500,
+    "steps_per_report": 10,
+    "test": false,
+    "test_batches": 500,
+    "train": true,
+    "val_batches": 25
+}

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