| | --- |
| | base_model: Qwen/Qwen3.5-2B |
| | 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 ~128,000 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. |
| | [test out our even smaller SLM](https://huggingface.co/RinggAI/Transcript-Analytics-Qwen3.5-0.8B) |
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| |  |
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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_2b", |
| | 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/Qwen3.5-2B |
| |
|
| | This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. |
| | |
