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README.md

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+GGUF quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+Octopus-v2 - GGUF
+- Model creator: https://huggingface.co/NexaAIDev/
+- Original model: https://huggingface.co/NexaAIDev/Octopus-v2/
+
+
+| Name | Quant method | Size |
+| ---- | ---- | ---- |
+| [Octopus-v2.Q2_K.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q2_K.gguf) | Q2_K | 1.08GB |
+| [Octopus-v2.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.IQ3_XS.gguf) | IQ3_XS | 1.16GB |
+| [Octopus-v2.IQ3_S.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.IQ3_S.gguf) | IQ3_S | 1.2GB |
+| [Octopus-v2.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q3_K_S.gguf) | Q3_K_S | 1.2GB |
+| [Octopus-v2.IQ3_M.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.IQ3_M.gguf) | IQ3_M | 1.22GB |
+| [Octopus-v2.Q3_K.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q3_K.gguf) | Q3_K | 1.29GB |
+| [Octopus-v2.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q3_K_M.gguf) | Q3_K_M | 1.29GB |
+| [Octopus-v2.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q3_K_L.gguf) | Q3_K_L | 1.36GB |
+| [Octopus-v2.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.IQ4_XS.gguf) | IQ4_XS | 1.4GB |
+| [Octopus-v2.Q4_0.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q4_0.gguf) | Q4_0 | 1.44GB |
+| [Octopus-v2.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.IQ4_NL.gguf) | IQ4_NL | 1.45GB |
+| [Octopus-v2.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q4_K_S.gguf) | Q4_K_S | 1.45GB |
+| [Octopus-v2.Q4_K.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q4_K.gguf) | Q4_K | 1.52GB |
+| [Octopus-v2.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q4_K_M.gguf) | Q4_K_M | 1.52GB |
+| [Octopus-v2.Q4_1.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q4_1.gguf) | Q4_1 | 1.56GB |
+| [Octopus-v2.Q5_0.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q5_0.gguf) | Q5_0 | 1.68GB |
+| [Octopus-v2.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q5_K_S.gguf) | Q5_K_S | 1.68GB |
+| [Octopus-v2.Q5_K.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q5_K.gguf) | Q5_K | 1.71GB |
+| [Octopus-v2.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q5_K_M.gguf) | Q5_K_M | 1.71GB |
+| [Octopus-v2.Q5_1.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q5_1.gguf) | Q5_1 | 1.79GB |
+| [Octopus-v2.Q6_K.gguf](https://huggingface.co/RichardErkhov/NexaAIDev_-_Octopus-v2-gguf/blob/main/Octopus-v2.Q6_K.gguf) | Q6_K | 1.92GB |
+
+
+
+	Original model description:
+	---
+license: apache-2.0
+base_model: google/gemma-2b
+model-index:
+- name: Octopus-V2-2B
+  results: []
+tags:
+- function calling
+- on-device language model
+- android
+inference: false
+space: false
+spaces: false
+language:
+- en
+---
+# Octopus V2: On-device language model for super agent
+<p align="center">
+- <a href="https://www.nexa4ai.com/" target="_blank">Nexa AI Product</a>
+- <a href="https://arxiv.org/abs/2404.01744" target="_blank">ArXiv</a>
+- <a href="https://www.youtube.com/watch?v=jhM0D0OObOw&ab_channel=NexaAI" target="_blank">Video Demo</a>
+</p>
+
+<p align="center" width="100%">
+  <a><img src="Octopus-logo.jpeg" alt="nexa-octopus" style="width: 40%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+## Introducing Octopus-V2-2B
+
+Octopus-V2-2B, an advanced open-source language model with 2 billion parameters, represents Nexa AI's research breakthrough in the application of large language models (LLMs) for function calling, specifically tailored for Android APIs. Unlike Retrieval-Augmented Generation (RAG) methods, which require detailed descriptions of potential function arguments—sometimes needing up to tens of thousands of input tokens—Octopus-V2-2B introduces a unique **functional token** strategy for both its training and inference stages. This approach not only allows it to achieve performance levels comparable to GPT-4 but also significantly enhances its inference speed beyond that of RAG-based methods, making it especially beneficial for edge computing devices.
+
+📱 **On-device Applications**:  Octopus-V2-2B is engineered to operate seamlessly on Android devices, extending its utility across a wide range of applications, from Android system management to the orchestration of multiple devices. 
+
+🚀 **Inference Speed**: When benchmarked, Octopus-V2-2B demonstrates a remarkable inference speed, outperforming the combination of "Llama7B + RAG solution" by a factor of 36X on a single A100 GPU. Furthermore, compared to GPT-4-turbo (gpt-4-0125-preview), which relies on clusters A100/H100 GPUs, Octopus-V2-2B is 168% faster. This efficiency is attributed to our **functional token** design.
+
+🐙 **Accuracy**: Octopus-V2-2B not only excels in speed but also in accuracy, surpassing the "Llama7B + RAG solution" in function call accuracy by 31%. It achieves a function call accuracy comparable to GPT-4 and RAG + GPT-3.5, with scores ranging between 98% and 100% across benchmark datasets.
+
+💪 **Function Calling Capabilities**: Octopus-V2-2B is capable of generating individual, nested, and parallel function calls across a variety of complex scenarios.
+
+## Example Use Cases
+
+
+<p align="center" width="100%">
+<a><img src="tool-usage-compressed.png" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+You can run the model on a GPU using the following code. 
+```python
+from transformers import AutoTokenizer, GemmaForCausalLM
+import torch
+import time
+
+def inference(input_text):
+    start_time = time.time()
+    input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
+    input_length = input_ids["input_ids"].shape[1]
+    outputs = model.generate(
+        input_ids=input_ids["input_ids"], 
+        max_length=1024,
+        do_sample=False)
+    generated_sequence = outputs[:, input_length:].tolist()
+    res = tokenizer.decode(generated_sequence[0])
+    end_time = time.time()
+    return {"output": res, "latency": end_time - start_time}
+
+model_id = "NexaAIDev/Octopus-v2"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = GemmaForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+input_text = "Take a selfie for me with front camera"
+nexa_query = f"Below is the query from the users, please call the correct function and generate the parameters to call the function.\n\nQuery: {input_text} \n\nResponse:"
+start_time = time.time()
+print("nexa model result:\n", inference(nexa_query))
+print("latency:", time.time() - start_time," s")
+```
+
+## Evaluation
+
+The benchmark result can be viewed in [this excel](android_benchmark.xlsx), which is manually verified. All the queries in the benchmark test are sampled by Gemini. 
+
+<p align="center" width="100%">
+<a><img src="latency_plot.jpg" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto; margin-bottom: 20px;"></a>
+<a><img src="accuracy_plot.jpg" alt="ondevice" style="width: 80%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+
+**Note**: One can notice that the query includes all necessary parameters used for a function. It is expected that query includes all parameters during inference as well.
+
+## Training Data
+We wrote 20 Android API descriptions to used to train the models, see [this file](android_functions.txt) for details. The Android API implementations for our demos, and our training data will be published later. Below is one Android API description example
+```
+def get_trending_news(category=None, region='US', language='en', max_results=5):
+    """
+    Fetches trending news articles based on category, region, and language.
+
+    Parameters:
+    - category (str, optional): News category to filter by, by default use None for all categories. Optional to provide.
+    - region (str, optional): ISO 3166-1 alpha-2 country code for region-specific news, by default, uses 'US'. Optional to provide.
+    - language (str, optional): ISO 639-1 language code for article language, by default uses 'en'. Optional to provide.
+    - max_results (int, optional): Maximum number of articles to return, by default, uses 5. Optional to provide.
+
+    Returns:
+    - list[str]: A list of strings, each representing an article. Each string contains the article's heading and URL.
+    """
+```
+
+
+## License
+This model was trained on commercially viable data and is under the [Nexa AI community disclaimer](https://www.nexa4ai.com/disclaimer). 
+
+
+## References
+We thank the Google Gemma team for their amazing models!
+```
+@misc{gemma-2023-open-models,
+  author = {{Gemma Team, Google DeepMind}},
+  title = {Gemma: Open Models Based on Gemini Research and Technology},
+  url = {https://goo.gle/GemmaReport},  
+  year = {2023},
+}
+```
+
+## Citation
+```
+@misc{chen2024octopus,
+      title={Octopus v2: On-device language model for super agent}, 
+      author={Wei Chen and Zhiyuan Li},
+      year={2024},
+      eprint={2404.01744},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```
+
+## Contact
+Please [contact us](mailto:[email protected]) to reach out for any issues and comments!
+