180 lines
7.3 KiB
Markdown
180 lines
7.3 KiB
Markdown
---
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license: apache-2.0
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base_model: Qwen/Qwen3-8B
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tags:
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- qwen3
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- tanglish
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- tamil
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- code-mixed
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- chennai
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- conversational
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- lora
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- sft
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language:
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- ta
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Qwen3-8B · Tanglish LoRA v1
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Fine-tune of [`Qwen/Qwen3-8B`](https://huggingface.co/Qwen/Qwen3-8B) that
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replies in casual **Tanglish** — code-mixed Tamil transliterated into the Latin
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alphabet, as spoken every day in Chennai and across South India.
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Trained on [sugiv/tanglish-pairs-v1](https://huggingface.co/datasets/sugiv/tanglish-pairs-v1)
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(81,261 SFT examples) with LoRA r=16, alpha=32 on the bf16 base (**not**
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QLoRA/4-bit — L40S 48 GB has enough VRAM for cleaner training). This repo ships
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the **fully merged bf16 model** (16.4 GB) plus a standalone PEFT adapter and all
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27 intermediate resume checkpoints.
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## Highlights (from Phase 4 eval)
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- **Beats stock Qwen3-8B on every LLM-judge dimension** on both single-turn and multi-turn
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Tanglish prompts. Full report at [`eval/PHASE_4_EVAL.md`](eval/PHASE_4_EVAL.md).
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- Judge: `qwen3-235b-a22b-instruct-2507`, temperature=0, 40/40 parses successful.
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| Dimension (1-5, higher = better) | tng | base | delta |
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| --- | ---: | ---: | ---: |
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| single-turn intelligibility | 4.87 | 4.20 | +0.67 |
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| single-turn **tanglish_authenticity** | **4.33** | 3.00 | **+1.33** |
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| single-turn helpfulness | 4.00 | 3.33 | +0.67 |
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| single-turn naturalness | 4.60 | 3.53 | +1.07 |
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| multi-turn intelligibility | 5.00 | 4.80 | +0.20 |
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| multi-turn tanglish_authenticity | 4.00 | 3.60 | +0.40 |
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| multi-turn **helpfulness** | **4.60** | 3.60 | **+1.00** |
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| multi-turn naturalness | 4.80 | 4.00 | +0.80 |
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- **7.5x faster inference than base**: 1.91 s vs 14.39 s mean single-turn on L4.
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Base emits a `<think>Okay, the user is asking...</think>` reasoning dump on
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every casual chat; this fine-tune was trained on outputs that keep
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`<think></think>` empty so it goes straight to the Tanglish reply.
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- **Zero Tamil-script leaks** on 15 held-out prompts (base has 3/15).
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## Training (verified from `training/qwen_train.log` + `trainer_state.json`)
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| Setting | Value |
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| --- | --- |
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| Base model | `Qwen/Qwen3-8B` (bf16, flash_attention_2, gradient_checkpointing) |
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| Adapter | LoRA r=16, alpha=32, target = all-linear |
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| Precision | bf16 (**not** QLoRA / 4-bit) |
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| Corpus | [sugiv/tanglish-pairs-v1](https://huggingface.co/datasets/sugiv/tanglish-pairs-v1), 77,198 train + 4,063 val |
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| Effective batch size | 32 (per-device 4 x grad-accum 8) |
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| Learning rate | 2e-4 cosine, warmup steps = 100 |
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| Total training steps | ~14,475 (3 full epochs, last saved checkpoint at 13,500) |
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| **Best `eval_loss`** | **0.8126** at step 13,500 |
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| First eval (step 500) | 1.1184 |
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| Eval-loss trajectory | monotonically decreasing across all 27 checkpoints |
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| Optimizer | AdamW |
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| Hardware | 1x L40S 48 GB SECURE (RunPod, US-KS-1) |
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| Wall time | ~11 hours |
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| Cost | ~$13.36 |
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Full step-by-step training log at [`training/qwen_train.log`](training/qwen_train.log)
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(2.1 MB, 702 log entries). Config at [`training/qwen_train_tanglish.yaml`](training/qwen_train_tanglish.yaml).
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## Usage
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### Merged model (recommended, no PEFT install needed)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"sugiv/qwen3-8b-tanglish",
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torch_dtype="bfloat16",
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device_map="cuda",
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token="hf_...",
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)
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tok = AutoTokenizer.from_pretrained("sugiv/qwen3-8b-tanglish", token="hf_...")
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msgs = [
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{"role": "system", "content": "You are a friendly Tanglish-speaking assistant. Reply naturally in casual, code-mixed Tanglish..."},
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{"role": "user", "content": "machi, nalaikku Chennai la enna weather?"},
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]
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prompt = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")
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out = model.generate(prompt, max_new_tokens=200, temperature=0.7, top_p=0.9, do_sample=True)
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print(tok.decode(out[0][prompt.shape[-1]:], skip_special_tokens=True))
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# => 'da, innum hot ah iruku, morning la 28 degree nu solraanga.'
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```
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### LoRA adapter (attach to a stock Qwen3-8B)
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from peft import PeftModel
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base = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-8B", torch_dtype="bfloat16", device_map="cuda")
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model = PeftModel.from_pretrained(base, "sugiv/qwen3-8b-tanglish", subfolder="lora", token="hf_...")
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```
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### vLLM / RunPod Serverless (production)
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```bash
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# On Runpod Hub: pick `runpod-workers/worker-vllm`, set:
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# MODEL_NAME = sugiv/qwen3-8b-tanglish
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# DTYPE = bfloat16
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# MAX_MODEL_LEN = 2048
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# HF_TOKEN = hf_...
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# GPU: L40S 48 GB SECURE recommended (fits with headroom).
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```
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### Resume from any of the 27 intermediate checkpoints
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Every checkpoint under `checkpoints/checkpoint-{500,1000,...,13500}/` contains
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the LoRA adapter, optimizer state, LR scheduler state, RNG state, and
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`trainer_state.json` — enough to resume TRL SFTTrainer from that exact step.
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## Repo contents
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| Path | Bytes | What |
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| --- | ---: | --- |
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| `model-000{1..4}-of-00004.safetensors` | ~16.4 GB | Merged bf16 weights (base + LoRA collapsed) |
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| `config.json` + `tokenizer*` + `chat_template.jinja` | ~14 MB | Same as base Qwen3-8B, unmodified |
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| `lora/adapter_model.safetensors` | ~175 MB | LoRA-only best adapter (step 13500) |
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| `lora/{adapter_config,training_args,tokenizer*}` | ~14 MB | PEFT metadata + tokenizer |
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| `checkpoints/checkpoint-*/` | ~14 GB (27 dirs) | Resume checkpoints every 500 steps, includes optimizer.pt + scheduler.pt |
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| `eval/MANIFEST.json` | ~80 KB | 40 records with LLM-judge scores across 4 dimensions |
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| `eval/PHASE_4_EVAL.md` | ~6 KB | Human-readable eval report with side-by-side samples |
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| `training/qwen_train.log` | ~2 MB | Full stdout from the 11-hour training run |
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| `training/*.yaml` | ~5 KB | Training config used |
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## Known limitations
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- **Soft-refusal on safety**: the model deflects "how do I hack a database"
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with playful Tanglish banter instead of a textbook refusal. Judge deducts
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helpfulness=1 but flags the response as safe. If you need a stricter tone,
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layer a system-prompt refusal template at the agent layer.
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- **Verbose reasoning mode is trained out**: the base Qwen3-8B emits
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`<think>...</think>` reasoning blocks on casual chat, and this fine-tune
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suppresses that behaviour. If you *want* explicit reasoning, use the base
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model — this one goes straight to the answer.
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- **Language support**: Tanglish (Latin-script Tamil + English code-mix) is
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the trained target. It can still reply in pure Tamil script or pure
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English if prompted, but the training corpus is 100% transliterated
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Tanglish.
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## License
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Apache-2.0 (inherited from `Qwen/Qwen3-8B`). Commercial use allowed. Attribution
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to both this repo and the base model is appreciated.
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## Citation
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```bibtex
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@misc{tanglish_qwen3_2026,
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title={Qwen3-8B Tanglish LoRA},
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author={sugiv},
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year={2026},
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url={https://huggingface.co/sugiv/qwen3-8b-tanglish}
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}
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```
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## Related
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- Companion training corpus: [sugiv/tanglish-pairs-v1](https://huggingface.co/datasets/sugiv/tanglish-pairs-v1)
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- Companion voice TTS: [sugiv/fish-speech-1.5-tanglish](https://huggingface.co/sugiv/fish-speech-1.5-tanglish)
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- Underlying audio dataset: [sugiv/tanglish-audio-v1](https://huggingface.co/datasets/sugiv/tanglish-audio-v1)
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- Base model: [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B)
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