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Model: Svngoku/qwen3-black-mirror
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen3
+license: apache-2.0
+language:
+- en
+datasets:
+- Svngoku/black_mirror_scripts
+---
+
+## Model
+
+| Metric | Value |
+|--------|-------|
+| Model | Svngoku/qwen3-black-mirror (https://huggingface.co/Svngoku/qwen3-black-mirror) |
+| Base | Qwen3-0.6B-Base (4-bit) |
+| Data | 33 episodes across 7 seasons, auto-merged |
+| Epochs | 3 |
+| Steps | 15 |
+| Training time | 25 seconds |
+| Loss | 3.08 -> 2.09 (epoch 2) -> 2.54 (epoch 3) |
+| Upload | Merged 16-bit model (1.19 GB) |
+The model was merged (LoRA weights folded into the base model) and pushed as a standalone model to Svngoku/qwen3-black-mirror. The inference test shows it's generating coherent text with some thematic influence (surveillance, privacy, ratings -- very Black Mirror).
+Artifacts created:
+- scripts/continued-pretraining.py -- custom script with multi-split support
+- Svngoku/black-mirror-training (https://huggingface.co/datasets/Svngoku/black-mirror-training) -- HF dataset repo hosting the script
+- Svngoku/qwen3-black-mirror (https://huggingface.co/Svngoku/qwen3-black-mirror) -- the trained model
+- Svngoku/qwen3-black-mirror-test (https://huggingface.co/Svngoku/qwen3-black-mirror-test) -- dry-run LoRA adapter
+
+# Uploaded finetuned  model
+
+- **Developed by:** Svngoku
+- **License:** apache-2.0
+- **Finetuned from model :** unsloth/Qwen3-0.6B-Base-unsloth-bnb-4bit
+
+This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

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config.json

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