metadata

license: apache-2.0
tags:
  - merge
  - mergekit
  - lazymergekit
  - bfloat16
  - text-generation-inference
  - model_stock
  - crypto
  - finance
  - llama
language:
  - en
base_model:
  - Chainbase-Labs/Theia-Llama-3.1-8B-v1
  - EpistemeAI/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO
  - mukaj/Llama-3.1-Hawkish-8B
pipeline_tag: text-generation
library_name: transformers

ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B

ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B is an advanced language model meticulously crafted by merging three pre-trained models using the powerful mergekit framework. This fusion leverages the Model Stock merge method to combine the specialized capabilities of Theia-Llama, Fireball-Meta-Llama, and Llama-Hawkish. The resulting model excels in creative text generation, technical instruction following, financial reasoning, and dynamic conversational interactions.

🚀 Merged Models

This model merge incorporates the following:

Chainbase-Labs/Theia-Llama-3.1-8B-v1: Specializes in cryptocurrency-oriented knowledge, enhancing the model's ability to generate and comprehend crypto-related content with high accuracy and depth.
EpistemeAI/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO: Focuses on instruction-following and coding capabilities, improving the model's performance in understanding and executing user commands, as well as generating executable code snippets.
mukaj/Llama-3.1-Hawkish-8B: Enhances financial reasoning and mathematical precision, enabling the model to handle complex financial analyses, economic discussions, and quantitative problem-solving with high proficiency.

🧩 Merge Configuration

The configuration below outlines how the models are merged using the Model Stock method. This approach ensures a balanced and effective integration of the unique strengths from each source model.

# Merge configuration for ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B using Model Stock

models:
  - model: Chainbase-Labs/Theia-Llama-3.1-8B-v1
  - model: EpistemeAI/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO
  - model: mukaj/Llama-3.1-Hawkish-8B
merge_method: model_stock
base_model: mukaj/Llama-3.1-Hawkish-8B
normalize: false
int8_mask: true
dtype: bfloat16

Key Parameters

Merge Method (merge_method): Utilizes the Model Stock method, as described in Model Stock, to effectively combine multiple models by leveraging their strengths.
Models (models): Specifies the list of models to be merged:
- Chainbase-Labs/Theia-Llama-3.1-8B-v1: Enhances cryptocurrency-oriented knowledge and content generation.
- EpistemeAI/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO: Improves instruction-following and coding capabilities.
- mukaj/Llama-3.1-Hawkish-8B: Enhances financial reasoning and mathematical precision.
Base Model (base_model): Defines the foundational model for the merge, which is mukaj/Llama-3.1-Hawkish-8B in this case.
Normalization (normalize): Set to false to retain the original scaling of the model weights during the merge.
INT8 Mask (int8_mask): Enabled (true) to apply INT8 quantization masking, optimizing the model for efficient inference without significant loss in precision.
Data Type (dtype): Uses bfloat16 to maintain computational efficiency while ensuring high precision.

🏆 Performance Highlights

Cryptocurrency Knowledge: Enhanced ability to generate and comprehend crypto-related content, making the model highly effective for blockchain discussions, crypto market analysis, and related queries.
Instruction Following and Coding: Improved performance in understanding and executing user instructions, as well as generating accurate and executable code snippets, suitable for coding assistance and technical support.
Financial Reasoning and Mathematical Precision: Advanced capabilities in handling complex financial analyses, economic discussions, and quantitative problem-solving, making the model ideal for financial modeling, investment analysis, and educational purposes.
Smooth Weight Blending: Utilization of the Model Stock method ensures a harmonious integration of different model attributes, resulting in balanced performance across various specialized tasks.
Optimized Inference: INT8 masking and bfloat16 data type contribute to efficient computation, enabling faster response times without compromising quality.

🎯 Use Case & Applications

ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B is designed to excel in environments that demand a combination of creative generation, technical instruction following, financial reasoning, and dynamic conversational interactions. Ideal applications include:

Cryptocurrency Analysis and Reporting: Generating detailed reports, analyses, and summaries related to blockchain projects, crypto markets, and financial technologies.
Coding Assistance and Technical Support: Providing accurate and executable code snippets, debugging assistance, and technical explanations for developers and technical professionals.
Financial Modeling and Investment Analysis: Assisting financial analysts and investors in creating models, performing economic analyses, and making informed investment decisions through precise calculations and reasoning.
Educational Tools and Tutoring Systems: Offering detailed explanations, answering complex questions, and assisting in educational content creation across subjects like finance, economics, and mathematics.
Interactive Conversational Agents: Powering chatbots and virtual assistants with specialized knowledge in cryptocurrency, finance, and technical domains, enhancing user interactions and support.
Content Generation for Finance and Tech Blogs: Creating high-quality, contextually relevant content for blogs, articles, and marketing materials focused on finance, technology, and cryptocurrency.

📝 Usage

To utilize ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B, follow the steps below:

Installation

First, install the necessary libraries:

pip install -qU transformers accelerate

Example Code

Below is an example of how to load and use the model for text generation:

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch

# Define the model name
model_name = "ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B"

# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Load the model
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Initialize the pipeline
text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Define the input prompt
prompt = "Explain the impact of decentralized finance on traditional banking systems."

# Generate the output
outputs = text_generator(
    prompt,
    max_new_tokens=150,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)

# Print the generated text
print(outputs[0]["generated_text"])

Notes

Fine-Tuning: This merged model may require fine-tuning to optimize performance for specific applications or domains, especially in highly specialized fields like cryptocurrency and finance.
Resource Requirements: Ensure that your environment has sufficient computational resources, especially GPU-enabled hardware, to handle the model efficiently during inference.
Customization: Users can adjust parameters such as temperature, top_k, and top_p to control the creativity and diversity of the generated text, tailoring the model's output to specific needs.

📜 License

This model is open-sourced under the Apache-2.0 License.

💡 Tags

merge
mergekit
model_stock
Llama
Hawkish
Theia
Fireball
ZeroXClem/LLama3.1-Hawkish-Theia-Fireball-8B
Chainbase-Labs/Theia-Llama-3.1-8B-v1
EpistemeAI/Fireball-Meta-Llama-3.2-8B-Instruct-agent-003-128k-code-DPO
mukaj/Llama-3.1-Hawkish-8B