Hercules-3.0-Mistral-7B is a fine-tuned language model derived from Mistralai/Mistral-7B-v0.1. 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-v3.0, expands upon the diverse capabilities of OpenHermes-2.5 with contributions from numerous curated datasets. This fine-tuning has hercules-v3.0 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-3.0-Mistral-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.
Important Note: Although Hercules-v3.0 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.
Limitations and Risks
Toxicity: The dataset contains toxic or harmful examples.
Hallucinations and Factual Errors: Like other language models, Hercules-3.0-Mistral-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 Data
Hercules-3.0-Mistral-7B is fine-tuned from the following sources:
cognitivecomputations/dolphin
Evol Instruct 70K & 140K
teknium/GPT4-LLM-Cleaned
jondurbin/airoboros-3.2
AlekseyKorshuk/camel-chatml
CollectiveCognition/chats-data-2023-09-22
Nebulous/lmsys-chat-1m-smortmodelsonly
glaiveai/glaive-code-assistant-v2
glaiveai/glaive-code-assistant
glaiveai/glaive-function-calling-v2
garage-bAInd/Open-Platypus
meta-math/MetaMathQA
teknium/GPTeacher-General-Instruct
GPTeacher roleplay datasets
BI55/MedText
pubmed_qa labeled subset
Unnatural Instructions
M4-ai/LDJnr_combined_inout_format
CollectiveCognition/chats-data-2023-09-27
CollectiveCognition/chats-data-2023-10-16
NobodyExistsOnTheInternet/sharegptPIPPA
yuekai/openchat_sharegpt_v3_vicuna_format
ise-uiuc/Magicoder-Evol-Instruct-110K
Squish42/bluemoon-fandom-1-1-rp-cleaned
sablo/oasst2_curated
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 2e-06 with the Adam optimizer. A linear scheduler was used, with an end factor of 0.3. A low learning rate was used to prevent exploding gradients.
No mixed precision was used, with the default dtype being bfloat16.
Trained on 1,400,000 examples of Hercules-v3.0
No model parameters were frozen.
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>
A dataset used in the Hercules-v3.0 was found to be causing performance degradation issues. I would recommend using Hercules-2.5 over this model, until Hercules-3.1 is released.
