Model Card for suzall/Llama-2-7b-chat-finetune-link-box

This model is a fine-tuned version of the Llama-2 7B model, specifically designed for chat applications with a focus on "link box" related contexts.

Model Details

Model Description

• Developed by: suzall
• Model type: Fine-tuned Language Model for Conversational AI
• Language(s) (NLP): English (primary), with understanding of technical terms related to "link box"
• License: [Specify License, e.g., MIT, Apache 2.0]
• Finetuned from model: Llama-2 7B (Meta AI)

Model Sources

• Repository: https://huggingface.co/suzall/Llama-2-7b-chat-finetune-link-box
• Demo: TODO: Insert Demo Link if Available

Uses

Direct Use

This model is intended for direct use in chatbot applications, particularly those requiring in-depth understanding and discussion of "link box" related topics.

Downstream Use

Fine-tuning this model for more specialized "link box" domains (e.g., networking, telecommunications) can enhance its performance in those areas.

Out-of-Scope Use

• Misuse in generating harmful or misleading content related to "link box" technologies.
• Use in highly sensitive or secure environments without proper security clearances.

Bias, Risks, and Limitations

Technical Limitations

• Domain Adaptation: Performance may degrade with highly specialized or niche "link box" topics.
• Emotional Intelligence: Empathetic responses might not always meet human expectations.

Recommendations

Users should be aware of the model's technical limitations and biases. For critical applications, human oversight is recommended.

How to Get Started with the Model

Inference (Running the Model)

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load pre-trained model tokenizer (vocabulary)
tokenizer = AutoTokenizer.from_pretrained("suzall/Llama-2-7b-chat-finetune-link-box")

# Load pre-trained model (weights)
model = AutoModelForSeq2SeqLM.from_pretrained("suzall/Llama-2-7b-chat-finetune-link-box")

# Your input query
query = "What is the primary use of a link box in networking?"

# Preprocessing input
inputs = tokenizer(query, return_tensors="pt")

# Generate response
outputs = model.generate(**inputs)

# Print response
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

API Usage (for Deployment)

Consider using the Hugging Face Transformers API or deploy via frameworks like Flask or FastAPI.

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Training Details

Training Data

• Dataset: Fine-tuned on a curated "link box" related conversational dataset (proprietary/custom).

Training Procedure

Training Hyperparameters

• Training regime: TODO: Specify Training Regime

Evaluation

Metrics

Metric	Value
Perplexity on Fine-tuning Dataset	TODO: Insert
Conversational Flow Rating (Human Evaluation)	TODO: Insert

Environmental Impact

• TODO: Calculate and Insert Environmental Impact Details

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Technical Specifications

Model Architecture and Objective

• Architecture: Based on Llama-2 7B, fine-tuned for conversational AI with a "link box" focus.
• Objective: Generate contextually relevant and informative responses.

Compute Infrastructure

• TODO: Insert Compute Infrastructure Details

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Citation

@misc{suzall/Llama-2-7b-chat-finetune-link-box,
  author = {suzall},
  title = {{Llama-2-7B Chat Finetune Link Box}},
  year = {2023},
  publisher = {Hugging Face},
  journal = {https://huggingface.co/suzall/Llama-2-7b-chat-finetune-link-box}
}

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Model Card Authors

• TODO: List Model Card Authors

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Model Card Contact

For any issues, suggestions, or general support, please open an issue on this repository or reach out to [[email protected]].