README.md

---
license: cc-by-nc-4.0
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.1-8B-Instruct
datasets:
- uiuc-convai/CALM-IT
pipeline_tag: text-generation
library_name: transformers
---



# CALM-8B: Conversational Agentic Language Model

[![Made with Oumi](https://badgen.net/badge/Made%20with/Oumi/%23085CFF?icon=https%3A%2F%2Foumi.ai%2Flogo_dark.svg)](https://github.com/oumi-ai/oumi)



## 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 domain-specific models on key evaluation benchmarks: **MultiWOZ 2.4 (TOD), BFCL V3 (LA), and API-Bank (LA).**

## Model Sources

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- 📝 **Paper:** https://arxiv.org/abs/2502.08820
- 💻 **Repository:** https://github.com/oumi-ai/oumi/tree/main/configs/projects/calm
- 💎 **Dataset:** https://huggingface.co/datasets/uiuc-convai/CALM-IT



---
## Model Details

- **Model Name:** CALM-8B  
- **Developed by:** Colloboration of UIUC Conversational AI LAB and Oumi 
- **License:** cc-by-nc-4.0  
- **Architecture:** Fine-tuned **Llama 3.1 8B Instruct**  
- **Training Data:** CALM-IT dataset
- **Fine-tuning Framework:** [Oumi](https://github.com/oumi-ai/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  

---
## 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.

---
## 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

---
## 💡 CALM-IT Dataset
<img src="table.png" alt="CALM-IT Dataset Statistics" width="800"/>


---
## 📊 Benchmark Performance

<img src="results.png" alt="CALM-IT Dataset Statistics" width="1000"/>
---


## Usage
### 🏗 How to Load the Model using Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("uiuc-convai/CALM-8B")
model = AutoModelForCausalLM.from_pretrained("uiuc-convai/CALM-8B")
```

### 🛠 Example Oumi Inference
```bash
pip install oumi

# See oumi_infer.yaml in this model's /oumi/ directory.
oumi infer -i -c ./oumi_infer.yaml
```

### 🛠 Example Oumi Fine-Tuning
```bash
pip install oumi

# See oumi_train.yaml in this model's /oumi/ directory.
oumi train -c ./oumi_train.yaml
```

---
- **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.

## Acknowledgements
We'd like to thank the [Oumi AI Team](https://github.com/oumi-ai/oumi) for collaborating on training the models, as well as [Together AI](https://www.together.ai/) for providing the compute resources necessary to train CALM 405B.

## License
This model is licensed under [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode).

<!-- TODO -->
---
## Citation
If you use **CALM-8B** in your research, please cite:
```
@misc{acikgoz2025singlemodelmastermultiturn,
      title={Can a Single Model Master Both Multi-turn Conversations and Tool Use? CALM: A Unified Conversational Agentic Language Model}, 
      author={Emre Can Acikgoz and Jeremiah Greer and Akul Datta and Ze Yang and William Zeng and Oussama Elachqar and Emmanouil Koukoumidis and Dilek Hakkani-Tür and Gokhan Tur},
      year={2025},
      eprint={2502.08820},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2502.08820}, 
}
```

For more details, visit [Project Repository](https://github.com/oumi-ai/oumi/tree/main/configs/projects/calm) or contact **[email protected]**.