Model Card: Hercules-5.0-Qwen2-7B

Model Description

Locutusque/Hercules-5.0-Qwen2-7B is a fine-tuned language model derived from Qwen2-7B. It is specifically designed to excel in instruction following, function calls, and conversational interactions across various scientific and technical domains. This fine-tuning has hercules-v5.0 with enhanced abilities in:

Complex Instruction Following: Understanding and accurately executing multi-step instructions, even those involving specialized terminology.
Function Calling: Seamlessly interpreting and executing function calls, providing appropriate input and output values.
Domain-Specific Knowledge: Engaging in informative and educational conversations about Biology, Chemistry, Physics, Mathematics, Medicine, Computer Science, and more.

This model was fine-tuned using my TPU-Alignment repository. https://github.com/Locutusque/TPU-Alignment

Join my discord server: https://discord.com/invite/vrGheTUFrm

Intended Uses & Potential Bias

Locutusque/Hercules-5.0-Qwen2-7B is well-suited to the following applications:

Specialized Chatbots: Creating knowledgeable chatbots and conversational agents in scientific and technical fields.
Instructional Assistants: Supporting users with educational and step-by-step guidance in various disciplines.
Code Generation and Execution: Facilitating code execution through function calls, aiding in software development and prototyping.

Limitations and Risks

Toxicity: The dataset contains toxic or harmful examples.
Hallucinations and Factual Errors: Like other language models, Locutusque/Hercules-5.0-Qwen2-7B may generate incorrect or misleading information, especially in specialized domains where it lacks sufficient expertise.
Potential for Misuse: The ability to engage in technical conversations and execute function calls could be misused for malicious purposes.

Training Procedure

This model was trained on 8 kaggle TPUs, using torch xla SPMD for high MXU efficiency. There was no expense on my end (meaning you can reproduce this too!)
A learning rate of 5e-6 with the Adam optimizer. A linear scheduler was used, with an end factor of 0.1.
No mixed precision was used, with the default dtype being bfloat16.
A total batch size of 64 was used.
Trained on all examples of Hercules-v5.0 for 1 epoch
No model parameters were frozen and no quantization was used.
This model was trained on OpenAI's ChatML prompt format. Because this model has function calling capabilities, the prompt format is slightly different, here's what it would look like: <|im_start|>system\n{message}<|im_end|>\n<|im_start|>user\n{user message}<|im_end|>\n<|im_start|>call\n{function call message}<|im_end|>\n<|im_start|>function\n{function response message}<|im_end|>\n<|im_start|>assistant\n{assistant message}</s>