File size: 2,990 Bytes
15076e6 2790428 15076e6 2790428 15076e6 2790428 0ee95be 2790428 0ee95be 2790428 f840a48 15076e6 fe346ad 15076e6 f840a48 15076e6 2790428 15076e6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 |
---
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
```
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/
[<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)
|