---
license: cc-by-nc-4.0
language:
- en
metrics:
- accuracy
base_model:
- meta-llama/Llama-3.3-70B-Instruct
---

# CoALM-70B: 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
**CoALM-70B** is our middle scale **Conversational Agentic Language Model**, designed to integrate **Task-Oriented Dialogue (TOD) capabilities** with **Language Agent (LA) functionalities** at a **larger scale** than its predecessor CoALM-8B. By leveraging **CoALM-IT**, a multi-task dataset interleaving **multi-turn ReAct reasoning** with **complex API usage**, CoALM-70B achieves **state-of-the-art performance** across TOD and function-calling benchmarks.

CoALM-70B has been fine-tuned on a **comprehensive multi-tasking** covering dialogue state tracking, function calling, and multi-turn reasoning, surpassing even proprietary models like **GPT-4o** on major conversational evaluation benchmarks: **MultiWOZ 2.4 (TOD), BFCL V3 (LA), and API-Bank (LA).**


## Model Sources

<!-- Provide the basic links for the model. -->

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


---
## Model Details

- **Model Name:** CoALM-70B  
- **Developed by:** Colloboration of UIUC Conversational AI LAB and Oumi 
- **License:** cc-by-nc-4.0  
- **Architecture:** Fine-tuned **Llama 3.3 70B Instruct**  
- **Parameter Count:** 70B  
- **Training Data:** CoALM-IT
- **Training Type:** Full Fine-tunning (FFT)
- **Fine-tuning Framework:** [Oumi](https://github.com/oumi-ai/oumi)
- **Training Hardware:** 8 NVIDIA H100 GPUs  
- **Training Duration:** ~24 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:** Handles long conversations with accurate state tracking.
- **Advanced Function Calling:** Dynamically selects and executes API calls for task completion.
- **Enhanced ReAct-based Reasoning:** Integrates structured reasoning (User-Thought-Action-Observation-Thought-Response).
- **Zero-Shot Generalization:** Excels in unseen function-calling and TOD tasks.

### 🚀 Benchmark Performance
- **MultiWOZ 2.4 (TOD):** Strong performance in dialogue state tracking and task success.
- **BFCL V3 (LA):** Superior function-calling abilities compared to language agents.
- **API-Bank (LA):** High accuracy in API call generation and response synthesis.

---
## Training Process
### 🔧 Fine-tuning Stages
1. **TOD Fine-tuning:** Optimized for dialogue state tracking (e.g., augmented SNIPS in instruction-tuned format).
2. **Function Calling Fine-tuning:** Trained to generate precise API calls from LA datasets.
3. **ReAct-based Fine-tuning:** Enhances multi-turn conversations with API integrations through structured reasoning.

### 🔍 Training Hyperparameters
- **Base Model:** Llama 3.3 70B Instruct
- **LoRA Config:** Rank = 16, Scaling Factor = 32
- **Batch Size:** 7
- **Learning Rate:** 4e-5
- **Optimizer:** AdamW (betas = 0.9, 0.999, epsilon = 1e-8)
- **Precision:** Mixed precision (bfloat16)
- **Warm-up Steps:** 24
- **Gradient Accumulation Steps:** 1

---


## 💡 CoALM-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 HuggingFace
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("uiuc-convai/CoALM-70B")
model = AutoModelForCausalLM.from_pretrained("uiuc-convai/CoALM-70B")
```

### 🛠 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
```


---
- **Scalability to CoALM-405B:** Next iteration will extend capabilities for even larger-scale conversations.
- **Continuous Open-Source Expansion:** Ongoing release of datasets, model weights, and training artifacts to foster community research.

---
## Acknowledgements
We'd like to thank the [Oumi AI Team](https://github.com/oumi-ai/oumi) for collaborating on training the models using the Oumi platform on [Together AI's](https://www.together.ai/) cloud.

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

---
## Citation
If you use **CoALM-70B** in your research, please cite:
```
@misc{acikgoz2025singlemodelmastermultiturn,
      title={Can a Single Model Master Both Multi-turn Conversations and Tool Use? CoALM: 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 **acikgoz2@illinois.edu**.