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library_name: transformers
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---
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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##
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library_name: transformers
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model-index:
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- name: TinyAgent-ToolRAG
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results: []
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tags:
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- function calling
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- on-device language model
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inference: false
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space: false
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spaces: false
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language:
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- en
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# TinyAgent: Function Calling at the Edge
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<p align="center">
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<a href="https://github.com/SqueezeAILab/TinyAgent/raw/main/TinyAgent.zip">Get the desktop app</a>
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<a href="https://bair.berkeley.edu/blog/2024/05/28/tiny-agent">Read the blog post</a>
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</p>
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![Thumbnail](https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/a1YuQosFiJQJ_7Ejribrd.png)
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TinyAgent aims to enable complex reasoning and function calling capabilities in Small Language Models (SLMs) that can be deployed securely and privately at the edge. Traditional Large Language Models (LLMs) like GPT-4 and Gemini-1.5, while powerful, are often too large and resource-intensive for edge deployment, posing challenges in terms of privacy, connectivity, and latency. TinyAgent addresses these challenges by training specialized SLMs with high-quality, curated data, and focusing on function calling with [LLMCompiler](https://github.com/SqueezeAILab/LLMCompiler). As a driving application, TinyAgent can interact with various MacOS applications, assisting users with day-to-day tasks such as composing emails, managing contacts, scheduling calendar events, and organizing Zoom meetings.
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When faced with challenging tasks, SLM agents require appropriate tools and in-context examples to guide them. If the model sees irrelevant examples, it can hallucinate. Likewise, if the model sees the descriptions of the tools that it doesn’t need, it usually gets confused, and these tools take up unnecessary prompt space. To tackle this, TinyAgent uses ToolRAG to retrieve the best tools and examples suited for a given query. This process has minimal latency and increases the accuracy of TinyAgent substantially. Please take a look at our blog post for more details.
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**Model Developers:** Squeeze AI Lab at University of California, Berkeley.
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**Variations:** TinyAgent models come in 2 sizes: TinyAgent-1.1B and TinyAgent-7B
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**License:** MIT
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## Demo
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<a href="https://youtu.be/0GvaGL9IDpQ" target="_blank" rel="noopener noreferrer">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/648903e1ce7b9a2abe3511aa/BpN-zPzfqa8wcRuJiYOYC.png" alt="TinyAgent Demo" width="700">
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</a>
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## How to Use
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Please see our [Github](https://github.com/SqueezeAILab/TinyAgent) for details on how to use TinyAgent models. TinyAgent models can be used programmatically or through our user interface.
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## Training Details
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**Dataset:**
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We curated a [dataset](https://huggingface.co/datasets/squeeze-ai-lab/TinyAgent-dataset) of **40,000** real-life use cases. We use GPT-3.5-Turbo to generate real-world instructions. These are then used to obtain synthetic execution plans using GPT-4-Turbo. Please check out our blog post for more details on our dataset.
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**Fine-tuning Procedure:**
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TinyAgent models are fine-tuned from base models. Below is a table of each TinyAgent model with its base counterpart
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| Model | Success Rate |
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| ----------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------ |
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| GPT-3.5-turbo | 65.04% |
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| GPT-4-turbo | 79.08% |
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| [TinyLLama-1.1B-32K-Instruct](https://huggingface.co/Doctor-Shotgun/TinyLlama-1.1B-32k-Instruct) | 12.71% |
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| [WizardLM-2-7b](https://huggingface.co/MaziyarPanahi/WizardLM-2-7B-GGUF) | 41.25% |
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| TinyAgent-1.1B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-1.1B-GGUF)] | **80.06%** |
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| TinyAgent-7B + ToolRAG / [[hf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B)] [[gguf](https://huggingface.co/squeeze-ai-lab/TinyAgent-7B-GGUF)] | **84.95%** |
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Using the synthetic data generation process described above, we use parameter-efficient fine-tuning with LoRA to fine-tune the base models for 3 epochs. Please check out our blog post for more details on our fine-tuning procedure.
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## Links
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**Blog Post**:
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**Github:** https://github.com/SqueezeAILab/TinyAgent
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