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