README.md

---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- nlp
- code
inference:
  parameters:
    temperature: 0.0
widget:
  - messages:
      - role: user
        content: Can you provide ways to eat combinations of bananas and dragonfruits?
quantized_by: bartowski
lm_studio:
  param_count: 4b
  use_case: chat
  release_date: 02-07-2024
  model_creator: Microsoft
  prompt_template: Phi 3
  system_prompt: You are a helpful AI assistant.
  base_model: Phi
  original_repo: microsoft/Phi-3-mini-4k-instruct
base_model: microsoft/Phi-3-mini-4k-instruct
---
## 💫 Community Model> Phi-3.1 mini 4k instruct by Microsoft

*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.

**Model creator:** [Microsoft](https://huggingface.co/microsoft)<br>
**Original model**: [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3278](https://github.com/ggerganov/llama.cpp/releases/tag/b3278)<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: 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.

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](https://arxiv.org/abs/2404.14219)

## Special thanks

🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.

🙏 Special thanks to [Kalomaze](https://github.com/kalomaze) for his dataset (linked [here](https://github.com/ggerganov/llama.cpp/discussions/5263)) that was used for calculating the imatrix for the IQ1_M and IQ2_XS quants, which makes them usable even at their tiny size!

## Disclaimers

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