necrozma-llama-2-7b

Model Details

Model Name: necrozma-llama-2-7b

Model Version: 1.0 Created by: Necrozma Date Created: September 9, 2023 Framework: Hugging Face Transformers Task: Chat-based Language Modeling (LLM) Model Description The necrozma-llama-2-7b, fine-tuned by Necrozma, is a specialized variant of the Llama-2-7b-chat-hf model designed to cater to specific requirements and use cases within the company. This model is named after the guanaco, a South American camelid, in keeping with the naming convention of the base Llama models.

Intended Use

Necrozma's fine-tuned necrozma-llama-2-7b model is intended for solving real-world problems within the company. It is particularly well-suited for use cases that involve natural language understanding and generation. Potential applications include:

Customer Support: Automating responses to customer inquiries and resolving issues efficiently. Information Retrieval: Extracting relevant information from large textual datasets. Content Generation: Creating human-like text for marketing materials, reports, or other documents. Conversational Agents: Developing chatbots or virtual assistants for interacting with users. The model's adaptability and performance can be tailored to meet specific project and departmental needs.

Training Data

The necrozma-llama-2-7b model has been fine-tuned on a curated dataset, which includes publicly available text data up to the knowledge cutoff date of September 2021. It may also incorporate proprietary data relevant to Necrozma's domain and use cases. Specifically, the "guanaco-llama2-1k" dataset is used for fine-tuning.

Model Performance

The performance of this fine-tuned model is assessed through various natural language processing metrics, including:

Accuracy: Evaluating the model's ability to provide accurate responses to user queries. Coherence: Ensuring that generated text is coherent and fluent. Relevance: Measuring the relevance of responses to the given context. Robustness: Assessing how well the model handles a wide range of user inputs and scenarios. Comprehensive performance metrics and benchmarks are available upon request.

Ethical Considerations Necrozma is committed to ethical AI usage and adheres to ethical guidelines, including:

Fairness: Ensuring that the model's responses are unbiased and do not discriminate against any group or individual. Privacy: Safeguarding user data and handling sensitive information responsibly. Transparency: Providing clear information about the use of AI when users interact with the model. Accountability: Monitoring and addressing any potential ethical concerns that may arise from model usage.

Direct Use

pip install transformers accelerate einops langchain from transformers import AutoTokenizer

model = "Abhinav7/necrozma-llama-2-7b" tokenizer = AutoTokenizer.from_pretrained(model) import torch import transformers

pipeline = transformers.pipeline( "text-generation", model=model, tokenizer=tokenizer, torch_dtype=torch.bfloat16, trust_remote_code=True, device_map="auto", max_length=100, do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.eos_token_id, )

from langchain import HuggingFacePipeline

llm = HuggingFacePipeline(pipeline=pipeline)

question = "Write a story about a ai robot." result = llm(question) print(result)

Limitations

Acknowledging the limitations of the Guanaco-LLAMA2-1k model:

Knowledge Cutoff: The model's knowledge is based on data available up to September 2021 and may not be aware of events or developments beyond that date. Context Sensitivity: While the model provides context-sensitive responses, it may not always generate contextually perfect replies. Bias: Despite mitigation efforts, the model may generate biased or offensive content. It's essential to review and filter generated responses when necessary. Contact Information For inquiries, feedback, or to report any issues related to the Guanaco-LLAMA2-1k model fine-tuned by Necrozma, please contact:

Necrozma AI Team: [email protected] Necrozma is dedicated to enhancing the model's performance and ensuring responsible AI usage within the organization.

Acknowledgments Necrozma would like to acknowledge the open-source community and Hugging Face for their contributions to the development of the base Llama models and their support in fine-tuning.