Hercules-4.0-Yi-34B is a fine-tuned language model derived from Yi-34B language model. It is specifically designed to excel in instruction following, function calls, and conversational interactions across various scientific and technical domains. The dataset used for fine-tuning, also named Hercules-v4.0, expands upon the diverse capabilities of OpenHermes-2.5 with contributions from numerous curated datasets. This fine-tuning has hercules-v4.0 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.
Intended Uses & Potential Bias
Hercules-4.0-Yi-34B 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.
Important Note: Although Hercules-v4.0 is carefully constructed, it's important to be aware that the underlying data sources may contain biases or reflect harmful stereotypes. Use this model with caution and consider additional measures to mitigate potential biases in its responses.
Toxicity: The dataset contains toxic or harmful examples.
Hallucinations and Factual Errors: Like other language models, Hercules-4.0-Yi-34B 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 1e-4 with an end factor of 0.001 was used.
No mixed precision was used, with the default dtype being bfloat16.
A total batch size of 64 was used.
Trained on 75,000 examples of Hercules-v4.0
I used LoRA to freeze about 97% of the model parameters. Quantization was not 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}<|endoftext|>
