metadata
base_model: unsloth/Qwen2.5-1.5B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2.5
license: apache-2.0
language:
- en
- hi
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
- 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/
