Model Card for Model ID

Our finetuned Mistral LLM is a large language model specialized for natural language processing tasks, delivering enhanced performance for a wide array of applications, including text classification, question-answering, chatbot services, and more.

Model Details

Model Description

• Developed by: Basel Anaya, Osama Awad, Yazeed Mshayekh
• Funded by [optional]: Basel Anaya, Osama Awad, Yazeed Mshayekh
• Model type: Autoregressive Language Model
• Language(s) (NLP): English
• License: MIT License
• Finetuned from model: MistralAI's Mistral-7B

Model Sources [optional]

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• Paper [optional]: [More Information Needed]
• Demo [optional]: [More Information Needed]

Uses

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Direct Use

Users can leverage the finetuned Mistral LLM for various NLP tasks right out-of-the-box. Simply interact with the API or load the model locally to experience superior language understanding and generation capabilities. Ideal for developers seeking rapid prototyping and deployment of conversational AI applications.

Downstream Use [optional]

Integrate the finetuned Mistral LLM effortlessly into custom applications and pipelines. Utilize the model as a starting point for further refinement, targeting industry-specific lingo, niches, or particular use cases. Seamless compatibility ensures smooth collaboration with adjacent technologies and services.

Out-of-Scope Use

Limitations exist concerning controversial topics, sensitive data, and scenarios demanding real-time responses. Users should exercise caution when deploying the model in safety-critical situations or regions with strict compliance regulations. Avoid sharing confidential or personally identifiable information with the model.

Bias, Risks, and Limitations

Address both technical and sociotechnical limitations.

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Further recommendations include cautious assessment of ethical implications, ongoing maintenance, periodic evaluations, and responsible reporting practices.

How to Get Started with the Model

Use the code below to get started with the model.

import torch
from transformers import pipeline, AutoTokenizer

# Load the finetuned Mistral LLM
model_name = "Reverb/Mistral-7B-LoreWeaver"
tokenizer = AutoTokenizer.from_pretrained(model_name)
generator = pipeline("text-generation", model=model_name, tokenizer=tokenizer)

# Example usage
input_text = "Once upon a time,"
num_generated_tokens = 50

response = generator(input_text, max_length=num_generated_tokens, num_return_sequences=1)
print(f"Generated text:\n{response[0]['generated_text']}")

# Alternatively, for fine-grained control over the generation process
inputs = tokenizer(input_text, return_tensors="pt")
outputs = generator.generate(
    inputs["input_ids"].to("cuda"),
    max_length=num_generated_tokens,
    num_beams=5,
    early_stopping=True,
    temperature=1.2,
)
generated_sentence = tokenizer.decode(outputs[0])
print(f"\nGenerated text with beam search and custom params:\n{generated_sentence}")

Training Details

Training Data

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Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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Framework versions

• PEFT 0.7.1

Open LLM Leaderboard Evaluation Results

Detailed results can be found here