NexaAIDev
/

Octopus-v1-gemma-7B

@@ -1,233 +1,99 @@
-## Model Configuration
-### Base Model
-```json
-{
-  "base_model": "google/gemma-7b-it"
-}
-```
-### LoRA Configuration
-```json
-{
-  "lora_config": {
-    "lora_alpha": 32,
-    "lora_dropout": 0.05,
-    "lora_r": 16,
-    "lora_modulus": [
-      "q_proj",
-      "v_proj",
-      "o_proj",
-      "up_proj",
-      "down_proj"
-    ]
-  }
-}
-```
-### Experiment Tracking
-- **Weights & Biases Run**: [View Run Details](https://wandb.ai/nexaai/nexa-sft-alex/runs/2fzr2wx9)
 ---
-This format provides a clear separation between the model's base configuration, the specific LoRA configuration, and a link to the experiment tracking, making it easier to understand and navigate.
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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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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-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-### Recommendations
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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.
-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
-### Training Data
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-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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 ## Evaluation
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-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-#### Summary
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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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-## Technical Specifications [optional]
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+---
+license: apache-2.0
+language:
+- en
+tags:
+- gemma
+- function calling
+- on-device language model
+- android
+- conversational
 ---
+# Octopus V1: On-device language model for function calling of software APIs
+<p align="center">
+- <a href="https://www.nexa4ai.com/" target="_blank">Nexa AI Product</a>
+- <a href="https://nexaai.github.io/octopus" target="_blank">ArXiv</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. Further demonstrations of its capabilities are available on the [Nexa AI Research Page](https://nexaai.github.io/octopus), showcasing its adaptability and potential for on-device integration.
+🚀 **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 gemma.modeling_gemma import GemmaForCausalLM
+from transformers import AutoTokenizer
+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/android_API_10k_data"
+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
+<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>
+## 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{TODO}
+```
+## Contact
+Please [contact us]([email protected]) to reach out for any issues and comments!