SlimHercules-4.0-Mistral-7B-v0.2

Model Card: SlimHercules-4.0-Mistral-v0.2-7B

Model Description

SlimHercules-4.0-Mistral-v0.2-7B is a fine-tuned language model derived from Mistralai/Mistral-7B-v0.2. 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.

This model is different in the sense that the dataset was shrunk and not shuffled, that way every dataset could be incorporated, without performance loss. This, in theory, should have much better performance in comparison to it's predecessors.

I will be evaluating this in the future.

Quants

EXL2 @bartowski

• https://huggingface.co/bartowski/SlimHercules-4.0-Mistral-7B-v0.2-exl2

GGUF @bartowski

• https://huggingface.co/bartowski/SlimHercules-4.0-Mistral-7B-v0.2-GGUF

AWQ @solidrust

• https://huggingface.co/solidrust/SlimHercules-4.0-Mistral-7B-v0.2-AWQ

Intended Uses & Potential Bias

SlimHercules-4.0-Mistral-v0.2-7B 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-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.

Limitations and Risks

• Toxicity: The dataset contains toxic or harmful examples.
• Hallucinations and Factual Errors: Like other language models, SlimHercules-4.0-Mistral-v0.2-7B 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 4e-06 with the Adam optimizer. A linear scheduler was used, with an end factor of 0.1. A low learning rate was used to prevent exploding gradients.
• No mixed precision was used, with the default dtype being bfloat16.
• A total batch size of 128 was used.
• Trained for two epochs on a shrunk and unshuffled version of Hercules-v4.0
• 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>

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

Evaluation

AGIEval