Though the name was left as Phi-3 in Microsoft's release, this is NOT the same model as before! It features massive improvements across a range of benchmarks, so we've renamed it as Phi-3.1 for clarity.
This update used additional post-training data which improved instruction following, structuring output, multi-turn conversations, explicitly support <|system|> tag, and significantly improve reasoning capability.
This model is 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:
<|system|>
You are a helpful AI assistant.<|end|>
<|user|>
{prompt}<|end|>
<|assistant|>
Technical Details
Phi-3.1 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: 128K tokens through Microsoft's longrope technique
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.
Phi 3.1 features additional tuning based on customer feedback, emphasizing instruction following, structure output, multi-turn conversation quality, explicitly support <|system|> tag, and significantly improve reasoning capability.
🙏 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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