File size: 3,983 Bytes
9f90d91 83e40a8 9f90d91 83e40a8 9f90d91 79afe69 d6dd707 79afe69 e741ddf 30f5067 79afe69 9f90d91 | 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 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 | ---
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
It’s built to handle real-world Hindi, English, Indic noisy transcripts.
`PS: VERY FEW EVALs WERE TAKEN FOR THE 0.8b MODEL`
[test out our even smatter SLM](https://huggingface.co/RinggAI/Transcript-Analytics-Qwen3.5-2B)
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"],
}
```
[<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)
# Uploaded finetuned model
- **Developed by:** RinggAI
- **License:** apache-2.0
- **Finetuned from model :** Qwen/Qwen3.5-0.8B
This qwen3_5 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
|