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--- |
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license: cc-by-nc-4.0 |
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language: |
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- en |
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metrics: |
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- accuracy |
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base_model: |
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- meta-llama/Llama-3.1-8B-Instruct |
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--- |
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# CALM-8B: Conversational Agentic Language Model |
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## Model Description |
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**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. |
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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).** |
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## Model Sources [TODO] |
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<!-- Provide the basic links for the model. --> |
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- **Paper [optional]:** [More Information Needed] |
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- **Repository:** [More Information Needed] |
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--- |
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## Model Details |
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- **Model Name:** CALM-8B |
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- **Developed by:** Colloboration of UIUC Conversational AI LAB and Oumi |
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- **License:** Apache 2.0 |
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- **Architecture:** Fine-tuned **Llama 3.1 8B Instruct** |
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- **Training Data:** CALM-IT dataset |
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- **Fine-tuning Framework:** [Oumi](https://github.com/oumi-ai/oumi) |
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- **Training Hardware:** 8 NVIDIA H100 GPUs |
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- **Training Duration:** ~8 hours |
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- **Evaluation Benchmarks:** MultiWOZ 2.4, BFCL V3, API-Bank |
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- **Release Date:** February 5, 2025 |
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## Capabilities and Features |
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### 🗣 Conversational Agentic Abilities |
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- **Multi-turn Dialogue Mastery:** Maintains coherent conversations across multiple turns with accurate state tracking. |
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- **Function Calling and API Integration:** Dynamically selects and calls APIs for task execution. |
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- **ReAct-based Reasoning:** Utilizes a structured reasoning process (User-Thought-Action-Observation-Thought-Response). |
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- **Zero-Shot Generalization:** Excels in previously unseen function-calling tasks. |
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### 🚀 Benchmark Performance |
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- **MultiWOZ 2.4 (TOD):** Excels in dialogue state tracking and task completion. |
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- **BFCL V3 (LA):** Demonstrates superior function-calling abilities over language agents. |
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- **API-Bank (LA):** Accurately generates API calls and integrates responses into conversation flow. |
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--- |
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## Training Process |
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### 🔧 Fine-tuning Stages |
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1. **TOD Fine-tuning:** Optimized for dialogue state tracking (e.g., augmented SNIPS reformatted in Alpaca-style instruction tuning). |
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2. **Function Calling Fine-tuning:** Trained to select and generate well-formed API calls from LA datasets. |
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3. **ReAct-based Fine-tuning:** Addresses multi-turn conversations with API integration using a structured reasoning framework. |
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### 🔍 Training Hyperparameters |
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- **Base Model:** Llama 3.1 8B Instruct |
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- **LoRA Config:** Rank = 16, Scaling Factor = 32 |
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- **Batch Size:** 8 |
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- **Learning Rate:** 1e-4 |
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- **Optimizer:** AdamW (betas = 0.9, 0.999, epsilon = 1e-8) |
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- **Precision:** Mixed precision (bfloat16) |
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- **Warm-up Steps:** 0.1 ratio of total steps |
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- **Gradient Accumulation Steps:** 1 |
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--- |
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## Usage |
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### 🏗 How to Load the Model using Transformers |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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tokenizer = AutoTokenizer.from_pretrained("uiuc-convai/CALM-8B") |
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model = AutoModelForCausalLM.from_pretrained("uiuc-convai/CALM-8B") |
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``` |
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### 🛠 Example Oumi Inference |
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```bash |
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pip install oumi |
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# See oumi_infer.yaml in this model's /oumi/ directory. |
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oumi infer -i -c ./oumi_infer.yaml |
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``` |
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### 🛠 Example Oumi Fine-Tuning |
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```bash |
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pip install oumi |
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# See oumi_train.yaml in this model's /oumi/ directory. |
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oumi train -c ./oumi_train.yaml |
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``` |
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- **Task-Specific Calibration:** While CALM-8B generalizes well across tasks, performance can improve with domain-specific fine-tuning. |
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- **Scalability to Larger Models:** Future iterations (CALM-70B, CALM-405B) extend capabilities to larger-scale agentic conversations. |
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- **Open-Source Expansion:** All datasets, training scripts, and model checkpoints are publicly available to foster further research. |
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## Acknowledgements |
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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. |
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## License |
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This model is licensed under [Creative Commons NonCommercial (CC BY-NC 4.0)](https://creativecommons.org/licenses/by-nc/4.0/legalcode). |
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<!-- TODO --> |
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--- |
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## Citation |
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If you use **CALM-8B** in your research, please cite: |
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``` |
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@article{yourpaper2024, |
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title={CALM: Conversational Agentic Language Model}, |
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author={Your Name and Collaborators}, |
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journal={Your Conference/Journal}, |
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year={2025} |
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} |
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``` |
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For more details, visit [Project Repository](https://github.com/your-repo) or contact **[email protected]**. |
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