Update README.md
Browse files# CALM-8B: Conversational Agentic Language Model
## Model Description
**CALM-8B** is the smallest open-source model of **CALM** (Conversational Agentic Language Model) series, designed to integrate both **Task-Oriented Dialogue (TOD) capabilities** and **Language Agent (LA) functionalities** into a unified system. By fine-tuning on **CALM-IT**, a novel dataset that interleaves multi-turn ReAct-based reasoning with complex API usage, CALM-8B achieves promising results on TOD and function-calling benchmarks.
CALM-8B is trained on a **multi-task dataset** covering dialogue state tracking, function calling, and multi-turn reasoning. The model outperforms top proprietary and domain-specific models, including **GPT-4o**, on key evaluation benchmarks: **MultiWOZ 2.4 (TOD), BFCL V3 (LA), and API-Bank (LA).**
## Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Paper [optional]:** [More Information Needed]
- **Repository:** [More Information Needed]
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## Model Details
- **Model Name:** CALM-8B
- **Developed by:** Colloboration of UIUC Conversational AI LAB and Oumi
- **License:** Apache 2.0
- **Architecture:** Fine-tuned **Llama 3.1 8B Instruct**
- **Training Data:** CALM-IT dataset
- **Fine-tuning Framework:** Oumi
- **Training Hardware:** 8 NVIDIA H100 GPUs
- **Training Duration:** ~8 hours
- **Evaluation Benchmarks:** MultiWOZ 2.4, BFCL V3, API-Bank
- **Release Date:** February 5, 2025
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## Capabilities and Features
### 🗣 Conversational Agentic Abilities
- **Multi-turn Dialogue Mastery:** Maintains coherent conversations across multiple turns with accurate state tracking.
- **Function Calling and API Integration:** Dynamically selects and calls APIs for task execution.
- **ReAct-based Reasoning:** Utilizes a structured reasoning process (User-Thought-Action-Observation-Thought-Response).
- **Zero-Shot Generalization:** Excels in previously unseen function-calling tasks.
### 🚀 Benchmark Performance
- **MultiWOZ 2.4 (TOD):** Excels in dialogue state tracking and task completion.
- **BFCL V3 (LA):** Demonstrates superior function-calling abilities over language agents.
- **API-Bank (LA):** Accurately generates API calls and integrates responses into conversation flow.
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## Training Process
### 🔧 Fine-tuning Stages
1. **TOD Fine-tuning:** Optimized for dialogue state tracking (e.g., augmented SNIPS reformatted in Alpaca-style instruction tuning).
2. **Function Calling Fine-tuning:** Trained to select and generate well-formed API calls from LA datasets.
3. **ReAct-based Fine-tuning:** Addresses multi-turn conversations with API integration using a structured reasoning framework.
### 🔍 Training Hyperparameters
- **Base Model:** Llama 3.1 8B Instruct
- **LoRA Config:** Rank = 16, Scaling Factor = 32
- **Batch Size:** 8
- **Learning Rate:** 1e-4
- **Optimizer:** AdamW (betas = 0.9, 0.999, epsilon = 1e-8)
- **Precision:** Mixed precision (bfloat16)
- **Warm-up Steps:** 0.1 ratio of total steps
- **Gradient Accumulation Steps:** 1
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## Usage
### 🏗 How to Load the Model
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("uiuc-convai/CALM-8B")
model = AutoModelForCausalLM.from_pretrained("uiuc-convai/CALM-8B")
```
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### 🛠 Example Inference
```python
TODO
```
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- **Task-Specific Calibration:** While CALM-8B generalizes well across tasks, performance can improve with domain-specific fine-tuning.
- **Scalability to Larger Models:** Future iterations (CALM-70B, CALM-405B) extend capabilities to larger-scale agentic conversations.
- **Open-Source Expansion:** All datasets, training scripts, and model checkpoints are publicly available to foster further research.
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## Citation
If you use **CALM-8B** in your research, please cite:
```
@article
{yourpaper2024,
title={CALM: Conversational Agentic Language Model},
author={Your Name and Collaborators},
journal={Your Conference/Journal},
year={2025}
}
```
For more details, visit [Project Repository](https://github.com/your-repo) or contact **[email protected]**.