初始化项目，由ModelHub XC社区提供模型

Model: sugiv/qwen3-8b-tanglish Source: Original Platform
2026-09-08 15:34:20 +08:00
commit 5734ffb7af
437 changed files with 4525344 additions and 0 deletions
--- a/README.md
+++ b/README.md
@@ -0,0 +1,179 @@
+---
+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 `<think>Okay, the user is asking...</think>` reasoning dump on
+  every casual chat; this fine-tune was trained on outputs that keep
+  `<think></think>` 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
+  `<think>...</think>` 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)