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MRaviteja
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Model Details
Model Provider
MRaviteja
Model Tree
Qwen/Qwen2.5-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
| Property | Value |
|---|---|
| Base Model | Qwen2.5-3B-Instruct |
| Fine-tuning | SFT + QLoRA |
| Framework | PyTorch |
| Libraries | Transformers, PEFT, TRL |
| Intended Use | Tool Calling, Business Analytics, KPI Reasoning |
| Quantization | 4-bit inference supported |
Large language models frequently struggle with structured business data because real-world datasets are rarely clean.
Common failure modes include:
This project fine-tunes Qwen2.5-3B to improve reliability on these tasks while maintaining low inference latency.
Training data was generated to simulate real-world business datasets by introducing:
The objective was to improve robustness against inputs that commonly cause tool-calling failures.
Evaluation was performed on a held-out adversarial benchmark containing noisy business documents and malformed tabular schemas.
| Metric | Base Model | Fine-Tuned |
|---|---|---|
| Function-calling Schema Compliance | 81.2% | 99.6% |
| Reasoning / Output Failures | Baseline | 76% Reduction |
| Inference Latency | — | <3.2 s |
This model is designed for applications involving:
The model is trained to generate structured JSON-RPC calls for tools such as:
calculate_kpisgenerate_chartExample:
json
{ "tool": "calculate_kpis", "arguments": { "metrics": [ "Revenue", "Profit Margin", "YoY Growth" ] }}python
from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "MRaviteja/qwen2.5-3b-toolcalling" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto")This model was developed as the reasoning engine for an MCP-powered analytics platform.
It integrates directly with:
services/mcp_service.pyservices/tool_service.pyIf the fine-tuned weights are unavailable, the application gracefully falls back to an Ollama-hosted model without requiring changes to the surrounding tool pipeline.
text
config.jsongeneration_config.jsonmodel.safetensorstokenizer.jsontokenizer_config.jsonchat_template.jinjaREADME.mdAlthough optimized for structured tool calling, this model:
If you use this model in research or production, please cite this repository.
text
@misc{qwen-business-toolcalling, title={Qwen2.5-3B Business Tool Calling}, author={Raviteja}, year={2026}, publisher={Hugging Face}}{ "tool": "calculate_kpis", "arguments": { "metrics": [ "Revenue", "Profit Margin", "YoY Growth" ] }}from transformers import AutoTokenizer, AutoModelForCausalLM model_name = "MRaviteja/qwen2.5-3b-toolcalling" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto")config.jsongeneration_config.jsonmodel.safetensorstokenizer.jsontokenizer_config.jsonchat_template.jinjaREADME.md@misc{qwen-business-toolcalling, title={Qwen2.5-3B Business Tool Calling}, author={Raviteja}, year={2026}, publisher={Hugging Face}}