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README.md
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- en
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---
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# Uploaded finetuned model
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- **Developed by:** RinggAI
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- **Finetuned from model :** Qwen/Qwen3.5-0.8B
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This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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[<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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- en
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---
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The model was finetuned on ~128,000 curated transcripts across different domanins and language preferences
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- Expanded Training: Now optimized for CX Support, Healthcare, Loan Collection, Insurance, Ecommerce, and Concierge services.
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- Feature Improvement: Significantly enhanced relative date-time extraction for more precise data processing.
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- Training Overview
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- You can plug it into your calling or voice AI stack to automatically extract:
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- Enum-based classifications (e.g., call outcome, intent, disposition)
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- Conversation summaries
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- Action items / follow-ups
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- Relative DateTime Artifacts
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It’s built to handle real-world Hindi, English, Indic noisy transcripts.
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Finetuning Parameters:
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```
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rank = 64 # kept small to know change inherent model intelligence but to make sure structured ectraction is followed
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trainer = SFTTrainer(
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model = model,
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tokenizer = tokenizer,
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train_dataset = train_dataset,
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eval_dataset = test_dataset,
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args = SFTConfig(
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dataset_text_field = "prompt",
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max_seq_length = max_seq_length,
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per_device_train_batch_size = 5,
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gradient_accumulation_steps = 5,
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warmup_steps = 10,
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num_train_epochs = 2,
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learning_rate = 2e-4,
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lr_scheduler_type = "linear",
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optim = "adamw_8bit",
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weight_decay = 0.01, # Unsloth default (was 0.001)
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seed = SEED,
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logging_steps = 50,
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report_to = "wandb",
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eval_strategy = "steps",
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eval_steps = 5000,
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save_strategy = "steps",
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save_steps = 5000,
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load_best_model_at_end = True,
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metric_for_best_model = "eval_loss",
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output_dir = "outputs_qwen35_0.8b",
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dataset_num_proc = 8,
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fp16= not torch.cuda.is_bf16_supported(),
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bf16= torch.cuda.is_bf16_supported()
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),
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)
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```
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`PS: VERY FEW EVALs WERE TAKEN FOR THE 0.8b MODEL`
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Provide the below schema for best output:
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```
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response_schema = {
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"type": "object",
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"properties": {
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"key_points": {
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"type": "array",
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"items": {"type": "string"},
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"nullable": True,
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},
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"action_items": {
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"type": "array",
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"items": {"type": "string"},
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"nullable": True,
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},
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"summary": {"type": "string"},
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"classification": classification_schema,
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"callback_requested": {
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"type": "boolean",
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"nullable": False,
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"description": "If the user requested a callback or mentiones currently he is busy then value is true otherwise false",
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},
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"callback_requested_time": {
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"type": "string",
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"nullable": True,
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"description": "ISO 8601 datetime string (YYYY-MM-DDTHH:MM:SS) in the call's timezone, if user requested a callback",
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},
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},
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"required": ["summary", "classification"],
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}
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```
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[<img style="border-radius: 20px;" src="https://storage.googleapis.com/desivocal-prod/desi-vocal/logo.png" width="200"/>](https://ringg.ai)
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[<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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# Uploaded finetuned model
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- **Developed by:** RinggAI
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- **Finetuned from model :** Qwen/Qwen3.5-0.8B
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This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
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