Phi-3.1-mini-128k-instruct-GGUF

💫 Community Model> Phi-3.1 mini 128k instruct by Microsoft

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Model creator: Microsoft<br> Original model: Phi-3-mini-128k-instruct<br> GGUF quantization: provided by bartowski based on llama.cpp release b3460<br>

Model Summary:

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.<br> 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.<br>

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.

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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