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-library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-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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 ---
+license: cc-by-nc-4.0
+base_model: Qwen/Qwen2-7B-Instruct
+model-index:
+- name: Dolphin
+  results: []
+tags:
+- RAG
+- on-device language model
+- Retrieval Augmented Generation
+inference: false
+space: false
+spaces: false
+language:
+- en
 ---
+# Dolphin: Long Context as a New Modality for Energy-Efficient On-Device Language Models
+<p align="center">
+- <a href="https://www.nexaai.com/models" target="_blank">Nexa Model Hub</a>
+- <a href="https://arxiv.org/abs/2404.01744" target="_blank">ArXiv</a>
+</p>
+<p align="center" width="100%">
+  <a><img src="logo.png" alt="nexa-octopus" style="width: 30%; min-width: 300px; display: block; margin: auto;"></a>
+</p>
+## Overview
+Dolphin is a novel approach to accelerate language model inference by treating long context as a new modality, similar to image, audio, and video modalities in vision-language models. This innovative method incorporates a language encoder model to encode context information into embeddings, applying multimodal model concepts to enhance the efficiency of language model inference。 Below are model highlights:
+- 🧠 Context as a distinct modality
+- 🗜️ Language encoder for context compression
+- 🔗 Multimodal techniques applied to language processing
+- ⚡ Optimized for energy efficiency and on-device use
+- 📜 Specialized for long context understanding
+## Model Architecture
+Dolphin employs a decoder-decoder framework with two main components:
+1. A smaller decoder (0.5B parameters) for transforming information from extensive contexts
+2. A larger decoder (7B parameters) for comprehending and generating responses to current queries
+3. The architecture also includes a projector to align embeddings between the text encoder and the main decoder.
+![Model Architecture](modelstructure.jpg)
+## Running the Model
+```python
+from transformers import AutoTokenizer
+from configuration_dolphin import DolphinForCausalLM
+import time
+tokenizer = AutoTokenizer.from_pretrained('nexa-collaboration/dolphin_instruct_1M_0805', trust_remote_code=True)
+model = DolphinForCausalLM.from_pretrained('nexa-collaboration/dolphin_instruct_1M_0805', trust_remote_code=True)
+def inference(input_text):
+    inputs = tokenizer(input_text, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=100)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+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()
+result = inference(nexa_query)
+print("Dolphin model result:\n", result)
+print("Latency:", time.time() - start_time, "s")
+```
+## Training Process
+Dolphin's training involves three stages:
+1. Restoration Training: Reconstructing original context from compressed embeddings
+2. Continual Training: Generating context continuations from partial compressed contexts
+3. Instruction Fine-tuning: Generating responses to queries given compressed contexts
+This multi-stage approach progressively enhances the model's ability to handle long contexts and generate appropriate responses.
+## Citation
+If you use Dolphin in your research, please cite our paper:
+```bibtex
+@article{dolphin2024,
+  title={Dolphin: Long Context as a New Modality for Energy-Efficient On-Device Language Models},
+  author={[Author Names]},
+  journal={arXiv preprint arXiv:[paper_id]},
+  year={2024}
+}
+```
+## Contact
+For questions or feedback, please [contact us]([email protected])

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