Model Card for LeoPARD 0.27

LeoPARD 0.27 is a fine-tuned version of LLaMA 3.1 8B, developed by AxisSmart | Labs. It incorporates reasoning thinking and chain-of-thought (CoT) capabilities (beta), making it suitable for tasks requiring logical reasoning and step-by-step problem-solving.

Model Details

Model Description

This model is a fine-tuned version of LLaMA 3.1 8B, optimized for improved reasoning and chain-of-thought capabilities. It is designed to handle complex tasks that require logical thinking, structured reasoning, and multi-step problem-solving.

Developed by: AxisSmart | Labs
Model type: Fine-tuned language model
Language(s) (NLP): Primarily English (multilingual capabilities may vary)
License: Creative Commons Attribution-NonCommercial 2.0 (CC BY-NC 2.0)
Finetuned from model: LLaMA 3.1 8B

License Details

The CC BY-NC 4.0 license allows users to:

Share: Copy and redistribute the model in any medium or format.
Adapt: Remix, transform, and build upon the model for non-commercial purposes.

Under the following terms:

Attribution: Users must give appropriate credit to AxisSmart | Labs, provide a link to the license, and indicate if changes were made.
NonCommercial: The model cannot be used for commercial purposes.

For commercial use, explicit permission from AxisSmart | Labs is required.

Uses

Direct Use

LeoPARD 0.27 can be used directly for tasks requiring reasoning and chain-of-thought capabilities, such as:

Logical problem-solving
Step-by-step reasoning tasks
Educational applications (e.g., math, science)
Decision support systems

Downstream Use [optional]

The model can be fine-tuned further for specific applications, such as:

Custom reasoning pipelines
Domain-specific problem-solving (e.g., finance, healthcare)
Integration into larger AI systems

Out-of-Scope Use

Tasks requiring real-time, low-latency responses without proper optimization
Applications involving highly sensitive or unethical use cases
Tasks outside the scope of its reasoning and language capabilities

Bias, Risks, and Limitations

Bias: The model may inherit biases present in the training data or the base LLaMA model.
Risks: Potential for incorrect or misleading reasoning outputs if not properly validated.
Limitations: The chain-of-thought capability is still in beta and may produce incomplete or suboptimal reasoning paths.

Recommendations

Users should validate the model's outputs, especially for critical applications. Fine-tuning on domain-specific data may improve performance and reduce biases.

How to Get Started with the Model

Use the code below to load and use LeoPARD 0.27:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "model_name"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

input_text = "Explain the reasoning behind the solution to this problem: ..."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Details

Training Data

The model was fine-tuned on a curated dataset designed to enhance reasoning and chain-of-thought capabilities. The dataset includes:

Logical reasoning problems
Step-by-step solutions
General-purpose language data

Training Procedure

Training time: 6 hours
Training regime: Mixed precision (bf16)
Hardware: [Confidential]

Training Hyperparameters

Learning rate: 2e-4
Batch size: 2
Epochs: 4

Evaluation

Testing has not yet been conducted. Evaluation metrics and results will be added in future updates.

Model Card Authors

AxisSmart | Labs
VortexHunter(Alvin)

Model Card Contact

Contact Comming Soon

VortexHunter23
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LeoPARD-0.27-4bit