RinggAI/Transcript-Analytics-SLM1.5b

As calling operations scale, it becomes clear that dialing and talking is not enough. Even with a strong voice AI + telephony architecture, the real value shows up only when post-call actions are captured and executed in a robust, dependable and consistent way. Closing the loop matters more than just connecting the call.

To support that, we’re releasing our Hindi + English transcript analytics model tuned specifically for call transcripts:

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

It’s built to handle real-world Hindi, English, and mixed Hinglish calls, including noisy transcripts.

Finetuning Parameters:

rank = 64
lora_alpha = rank*2,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
                      "gate_proj", "up_proj", "down_proj",],
SFTConfig(
        dataset_text_field = "prompt",
        per_device_train_batch_size = 32,
        gradient_accumulation_steps = 1, # Use GA to mimic batch size!
        warmup_steps = 5,
        num_train_epochs = 2,
        learning_rate = 2e-4,
        logging_steps = 50,
        optim = "adamw_8bit",
        weight_decay = 0.001,
        lr_scheduler_type = "linear",
        seed = SEED,
        report_to = "wandb",
        eval_strategy="steps",
        eval_steps=200,
    )
The model was finetuned on ~100,000 curated transcripts across different domanins and language preferences

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,
        },
        "required": ["summary", "classification"],
}

• Developed by: RinggAI
• License: apache-2.0
• Finetuned from model : unsloth/Qwen2.5-1.5B-Instruct
• Parameter decision where made using Schulman, J., & Thinking Machines Lab. (2025).
  LoRA Without Regret.
  Thinking Machines Lab: Connectionism.
  DOI: 10.64434/tml.20250929
  Link: https://thinkingmachines.ai/blog/lora/