Builder123/tinyllama-revops-finetuned

RevOpsLM

A language model trained on Salesforce Agentforce, NetSuite AI, and SaaS Revenue Recognition (ASC 606) concepts using a LoRA fine-tuned adapter for TinyLlama-1.1B-Chat.

Model Description

This is a proof-of-concept project demonstrating LoRA fine-tuning techniques applied to a language model. The adapter was trained on 50 curated examples covering:

• Salesforce Agentforce (20 examples): Agent types, RAG, topics, guardrails, triggers, and analytics
• NetSuite AI Features (15 examples): Text Enhancer, Analytics Warehouse, Smart Alerts, and automation capabilities
• SaaS Revenue Recognition (15 examples): ASC 606 compliance, performance obligations, deferred revenue, and contract accounting

Important: This is a learning exercise with limited training data. The model demonstrates fine-tuning methodology and is not meant for production use.

Resources

• 📂 GitHub Repository
• 📓 Training Notebook

Training Details

• Base Model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
• Fine-Tuning Method: LoRA (Low-Rank Adaptation)
• Training Examples: 50
• Epochs: 3
• Hardware: Google Colab T4 GPU
• Training Time: ~8 minutes
• LoRA Parameters: r=8, alpha=16, dropout=0.05

How to Use

To use this model, you'll need to load both the base model and this LoRA adapter:

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
adapter_model = "Builder123/tinyllama-revops-finetuned"

tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
model = PeftModel.from_pretrained(model, adapter_model)

# Generate response
prompt = "User: What is ASC 606?\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

• Trained on limited data - responses may be inaccurate or incomplete
• Not suitable for production use without additional training
• May hallucinate or provide outdated information
• Should be verified against official documentation

Intended Use

This model is intended for:

• Educational purposes and learning about fine-tuning techniques
• Demonstrating LoRA methodology
• Portfolio/project showcase

License

This model follows the license of the base TinyLlama model (Apache 2.0).

Author

Created by Vladimir Parfenov.

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