💫 Community Model> Phi-3 mini 4k instruct by Microsoft
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Model creator: Microsoft
Original model: Phi-3-mini-4k-instruct
GGUF quantization: provided by bartowski based on llama.cpp release b2717
Model Summary:
Phi-3 Mini 4k instruct is Microsoft's 3.8B parameter instruct tuned model based on their brand new Phi-3 dataset.
Phi-3 models are unique in that a large portion of their training data is sourced from purely synthetic generation from GPT-3.5, with additional filtered publicly available data.
These models were tuned with common sense, language understanding, math, code, long conteext, and logical reasoning as the primary focus.
Prompt template:
Choose the Phi 3 preset in your LM Studio.
Under the hood, the model will see a prompt that's formatted like so:
<s><|system|> You are a helpful AI assistant.<|end|><|user|> {prompt}<|end|><|assistant|>
Use case and examples
Phi-3 is a great model for anyone who wants a powerful but extremely lightweight model. Especially compressed, this model can run on extremely low power hardware while still exceeding models significantly larger in most tasks.
Creativity
Coding
Logic solving
Technical Details
Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
Context length: 4K tokens
This model was trained on 3.3T tokens of data and is a combination:
- publicly available documents that were filtered rigorously for quality, selected high-quality educational data, and code
- Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.)
- High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
Find more details on the arXiv report here
Special thanks
🙏 Special thanks to Georgi Gerganov and the whole team working on llama.cpp for making all of this possible.
🙏 Special thanks to Kalomaze for his dataset (linked here) that was used for calculating the imatrix for the IQ1_M and IQ2_XS quants, which makes them usable even at their tiny size!
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