Phi 3 Mini 4k Instruct Graph

🚀 Model Card for Phi-3-mini-4k-instruct-graph

This model is a fine - tuned version of Microsoft's Phi - 3 - mini - 4k - instruct. It's specifically designed for entity relationship extraction from general text data. It aims to match GPT - 4's quality and accuracy in generating entity relationship graphs while enhancing efficiency for large - scale processing.

🚀 Quick Start

The following code snippets show how to quickly start running the model on a GPU:

import torch 
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline 

torch.random.manual_seed(0) 
model = AutoModelForCausalLM.from_pretrained( 
    "EmergentMethods/Phi-3-mini-4k-instruct-graph",  
    device_map="cuda",  
    torch_dtype="auto",  
    trust_remote_code=True,  
) 

tokenizer = AutoTokenizer.from_pretrained("EmergentMethods/Phi-3-mini-4k-instruct-graph") 

messages = [ 
    {"role": "system", "content": """
A chat between a curious user and an artificial intelligence Assistant. The Assistant is an expert at identifying entities and relationships in text. The Assistant responds in JSON output only.

The User provides text in the format:

-------Text begin-------
<User provided text>
-------Text end-------

The Assistant follows the following steps before replying to the User:

1. **identify the most important entities** The Assistant identifies the most important entities in the text. These entities are listed in the JSON output under the key "nodes", they follow the structure of a list of dictionaries where each dict is:

"nodes":[{"id": <entity N>, "type": <type>, "detailed_type": <detailed type>}, ...]

where "type": <type> is a broad categorization of the entity. "detailed type": <detailed_type>  is a very descriptive categorization of the entity.

2. **determine relationships** The Assistant uses the text between -------Text begin------- and -------Text end------- to determine the relationships between the entities identified in the "nodes" list defined above. These relationships are called "edges" and they follow the structure of:

"edges":[{"from": <entity 1>, "to": <entity 2>, "label": <relationship>}, ...]

The <entity N> must correspond to the "id" of an entity in the "nodes" list.

The Assistant never repeats the same node twice. The Assistant never repeats the same edge twice.
The Assistant responds to the User in JSON only, according to the following JSON schema:

{"type":"object","properties":{"nodes":{"type":"array","items":{"type":"object","properties":{"id":{"type":"string"},"type":{"type":"string"},"detailed_type":{"type":"string"}},"required":["id","type","detailed_type"],"additionalProperties":false}},"edges":{"type":"array","items":{"type":"object","properties":{"from":{"type":"string"},"to":{"type":"string"},"label":{"type":"string"}},"required":["from","to","label"],"additionalProperties":false}}},"required":["nodes","edges"],"additionalProperties":false}
     """}, 
    {"role": "user", "content": """
-------Text begin-------
OpenAI is an American artificial intelligence (AI) research organization founded in December 2015 and headquartered in San Francisco, California. Its mission is to develop "safe and beneficial" artificial general intelligence, which it defines as "highly autonomous systems that outperform humans at most economically valuable work".[4] As a leading organization in the ongoing AI boom,[5] OpenAI is known for the GPT family of large language models, the DALL - E series of text - to - image models, and a text - to - video model named Sora.[6][7] Its release of ChatGPT in November 2022 has been credited with catalyzing widespread interest in generative AI.
-------Text end-------
"""}
] 

pipe = pipeline( 
    "text-generation", 
    model=model, 
    tokenizer=tokenizer, 
) 

generation_args = { 
    "max_new_tokens": 500, 
    "return_full_text": False, 
    "temperature": 0.0, 
    "do_sample": False, 
} 

output = pipe(messages, **generation_args) 
print(output[0]['generated_text'])

# Output:

# {
#     "nodes": [
#         {
#             "id": "OpenAI",
#             "type": "organization",
#             "detailed_type": "ai research organization"
#         },
#         {
#             "id": "GPT family",
#             "type": "technology",
#             "detailed_type": "large language models"
#         },
#         {
#             "id": "DALL-E series",
#             "type": "technology",
#             "detailed_type": "text-to-image models"
#         },
#         {
#             "id": "Sora",
#             "type": "technology",
#             "detailed_type": "text-to-video model"
#         },
#         {
#             "id": "ChatGPT",
#             "type": "technology",
#             "detailed_type": "generative ai"
#         },
#         {
#             "id": "San Francisco",
#             "type": "location",
#             "detailed_type": "city"
#         },
#         {
#             "id": "California",
#             "type": "location",
#             "detailed_type": "state"
#         },
#         {
#             "id": "December 2015",
#             "type": "date",
#             "detailed_type": "foundation date"
#         },
#         {
#             "id": "November 2022",
#             "type": "date",
#             "detailed_type": "release date"
#         }
#     ],
#     "edges": [
#         {
#             "from": "OpenAI",
#             "to": "San Francisco",
#             "label": "headquartered in"
#         },
#         {
#             "from": "San Francisco",
#             "to": "California",
#             "label": "located in"
#         },
#         {
#             "from": "OpenAI",
#             "to": "December 2015",
#             "label": "founded in"
#         },
#         {
#             "from": "OpenAI",
#             "to": "GPT family",
#             "label": "developed"
#         },
#         {
#             "from": "OpenAI",
#             "to": "DALL-E series",
#             "label": "developed"
#         },
#         {
#             "from": "OpenAI",
#             "to": "Sora",
#             "label": "developed"
#         },
#         {
#             "from": "OpenAI",
#             "to": "ChatGPT",
#             "label": "released"
#         },
#         {
#             "from": "ChatGPT",
#             "to": "November 2022",
#             "label": "released in"
#         }
#     ]
# }

✨ Features

• Generate structured JSON data representing entity relationships in general text data.
• Enhance information retrieval in various text databases.
• Explore temporal relationships and evolving narratives across different types of documents.
• Conduct advanced predictive modeling for trend analysis in diverse textual sources.
• Particularly useful for high - throughput processing of large - volume text data in applications like content aggregation platforms, research databases, and comprehensive text analysis systems.

📦 Installation

No installation steps are provided in the original document, so this section is skipped.

📚 Documentation

Model Details

For more information, see our blog post: 📰 Blog

Uses

This model is designed for generating structured JSON data representing entity relationships in general text data. It can be applied in the following scenarios:

• Enhanced information retrieval in various text databases.
• Exploration of temporal relationships and evolving narratives across different types of documents.
• Advanced predictive modeling for trend analysis in diverse textual sources.

It is especially useful for applications that require high - throughput processing of large volumes of text data, such as content aggregation platforms, research databases, and comprehensive text analysis systems.

Bias, Risks, and Limitations

Although the goal of the dataset is to reduce bias and improve diversity, it is still biased towards western languages and countries. This limitation stems from the capabilities of Llama2 for translation and summary generations. Additionally, any bias present in Llama2 training data will also be in this dataset since Llama2 was used to summarize open - web articles. Moreover, any biases in Microsoft Phi - 3 will be present in the current dataset.

Training Details

• Training Data: Over 7,000 stories and updates from AskNews, curated to avoid topic overlap.
• Training Procedure: Fine - tuned using the Transformers library, SFTTrainer, PEFT, and QLoRA.

Evaluation Results

Compared to GPT - 4o (ground truth), Claude Sonnet 3.5, and the base Phi - 3 model:

Environmental Impact

• Hardware Type: 1x A100 SXM
• Hours used: 3 hours
• Carbon Emitted: 0.44 kg (According to Machine Learning Impact calculator)

Ethical Considerations

Users should note that this model is designed for entity relationship extraction from general text data and may not be suitable for other domains without further fine - tuning. The model's outputs should be reviewed and validated, especially when used in applications that may impact decision - making or public information dissemination.

📄 License

The model is licensed under Creative Commons Attribution Non Commercial Share Alike 4.0.