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.
π 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.
𧩠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 tofalse
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
): Usesbfloat16
to maintain computational efficiency while ensuring high precision.
π 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.
π― 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.
π 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
, andtop_p
to control the creativity and diversity of the generated text, tailoring the model's output to specific needs.
π License
This model is open-sourced under the Apache-2.0 License.
π‘ 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
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