| --- |
| license: apache-2.0 |
| base_model: google/gemma-3-270m-it |
| tags: |
| - sales |
| - coaching |
| - gemma |
| - fine-tuned |
| - conversational |
| language: |
| - en |
| pipeline_tag: text-generation |
| --- |
| |
| # Gemma 3 Sales Coach 🎯 |
|
|
| Fine-tuned **Gemma 3 270M IT** model for sales conversation coaching. |
|
|
| ## Model Description |
|
|
| This model analyzes sales conversations and provides detailed coaching feedback, identifying: |
| - **Sales rep mistakes** with specific turn numbers |
| - **Why each mistake is problematic** |
| - **Better alternative responses** |
| - **Impact on conversion probability** |
|
|
| ## Training Data |
|
|
| - **Dataset**: 99,056 synthetic sales conversations with coaching annotations |
| - **Industries**: 60+ industries covered |
| - **Personas**: 20+ buyer personas |
| - **Outcomes**: Lost deals, stalled deals, weak commitments |
|
|
| ## Usage |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| import torch |
| |
| model = AutoModelForCausalLM.from_pretrained( |
| "convaiinnovations/gemma3-sales-coach", |
| torch_dtype=torch.float16, |
| device_map="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained("convaiinnovations/gemma3-sales-coach") |
| |
| # Format your conversation as instruction |
| conversation = '''[Turn 1] Sales Rep: Hi Sarah, this is Mike from CloudTech Solutions. How are you today? |
| [Turn 2] Prospect: I'm busy. What's this about? |
| [Turn 3] Sales Rep: Great! I wanted to tell you about our amazing cloud platform. It's the best in the market! |
| [Turn 4] Prospect: We already have AWS. Why would we switch? |
| [Turn 5] Sales Rep: Well, our platform is much better than AWS in every way. |
| [Turn 6] Prospect: That's a bold claim. Can you be more specific? |
| [Turn 7] Sales Rep: We have better uptime and support. Anyway, pricing starts at $10,000 per month. |
| [Turn 8] Prospect: That's expensive. What's included? |
| [Turn 9] Sales Rep: Everything you need. Trust me, it's worth it. |
| [Turn 10] Prospect: I'd need to see some case studies or ROI data. |
| [Turn 11] Sales Rep: I can send those later. So, are you ready to schedule a demo? |
| [Turn 12] Prospect: I don't think so. Send me some materials and I'll review when I have time. |
| |
| Where did I make mistakes and how can I improve?''' |
| |
| # Apply chat template (instruction format) |
| messages = [{"role": "user", "content": conversation}] |
| prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
| |
| inputs = tokenizer(prompt, return_tensors="pt").to(model.device) |
| outputs = model.generate( |
| **inputs, |
| max_new_tokens=512, |
| temperature=0.7, |
| do_sample=True |
| ) |
| print(tokenizer.decode(outputs[0], skip_special_tokens=True)) |
| ``` |
|
|
| ## Training Details |
|
|
| - **Base Model**: google/gemma-3-270m-it |
| - **Method**: LoRA fine-tuning |
| - **LoRA Rank**: 32 |
| - **Epochs**: 5 |
| - **Hardware**: 2x NVIDIA T4 GPUs |
|
|
| ## Limitations |
|
|
| - Optimized for B2B sales conversations |
| - Best results with 10+ turn conversations |
| - English language only |
|
|
| ## License |
|
|
| Apache 2.0 (same as base Gemma model) |
|
|
