| --- |
| base_model: Qwen/Qwen3.5-0.8B |
| tags: |
| - text-generation-inference |
| - transformers |
| - unsloth |
| - qwen3_5 |
| license: apache-2.0 |
| language: |
| - hi |
| - en |
| - ta |
| - te |
| - kn |
| - bn |
| - mr |
| --- |
| |
|
|
| The model was finetuned on ~76,000 curated transcripts across different domanins and language preferences |
| - Expanded Training: Now optimized for CX Support, Healthcare, Loan Collection, Insurance, Ecommerce, and Concierge services. |
| - Feature Improvement: Significantly enhanced relative date-time extraction for more precise data processing. |
| - Training Overview |
| - You can plug it into your calling or voice AI stack to automatically extract: |
| - Enum-based classifications (e.g., call outcome, intent, disposition) |
| - Conversation summaries |
| - Action items / follow-ups |
| - Relative DateTime Artifacts |
|
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| It’s built to handle real-world Hindi, English, Indic noisy transcripts. |
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|  |
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| `PS: VERY FEW EVALs WERE TAKEN FOR THE 0.8b MODEL` |
|
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| [test out our even smatter SLM](https://huggingface.co/RinggAI/Transcript-Analytics-Qwen3.5-2B) |
|
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| Finetuning Parameters: |
| ``` |
| rank = 64 # kept small to know change inherent model intelligence but to make sure structured ectraction is followed |
| trainer = SFTTrainer( |
| model = model, |
| tokenizer = tokenizer, |
| train_dataset = train_dataset, |
| eval_dataset = test_dataset, |
| args = SFTConfig( |
| dataset_text_field = "prompt", |
| max_seq_length = max_seq_length, |
| per_device_train_batch_size = 5, |
| gradient_accumulation_steps = 5, |
| |
| warmup_steps = 10, |
| num_train_epochs = 2, |
| learning_rate = 2e-4, |
| lr_scheduler_type = "linear", |
| |
| optim = "adamw_8bit", |
| weight_decay = 0.01, # Unsloth default (was 0.001) |
| seed = SEED, |
| |
| logging_steps = 50, |
| report_to = "wandb", |
| |
| eval_strategy = "steps", |
| eval_steps = 5000, |
| save_strategy = "steps", |
| save_steps = 5000, |
| load_best_model_at_end = True, |
| metric_for_best_model = "eval_loss", |
| |
| output_dir = "outputs_qwen35_0.8b", |
| dataset_num_proc = 8, |
| fp16= not torch.cuda.is_bf16_supported(), |
| bf16= torch.cuda.is_bf16_supported() |
| ), |
| ) |
| ``` |
|
|
| Provide the below schema for best output: |
| ``` |
| response_schema = { |
| "type": "object", |
| "properties": { |
| "key_points": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "nullable": True, |
| }, |
| "action_items": { |
| "type": "array", |
| "items": {"type": "string"}, |
| "nullable": True, |
| }, |
| "summary": {"type": "string"}, |
| "classification": classification_schema, |
| "callback_requested": { |
| "type": "boolean", |
| "nullable": False, |
| "description": "If the user requested a callback or mentiones currently he is busy then value is true otherwise false", |
| }, |
| "callback_requested_time": { |
| "type": "string", |
| "nullable": True, |
| "description": "ISO 8601 datetime string (YYYY-MM-DDTHH:MM:SS) in the call's timezone, if user requested a callback", |
| }, |
| }, |
| "required": ["summary", "classification"], |
| } |
| ``` |
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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) |
| [<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 |
|
|
| - **Developed by:** RinggAI |
| - **License:** apache-2.0 |
| - **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. |
| |
