README.md

---
base_model: lxuechen/phi-2-dpo
inference: false
language:
- en
license: other
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
license_name: microsoft-research-license
model-index:
- name: phi-2-dpo
  results:
  - dataset:
      name: AlpacaEval
      type: AlpacaEval
    metrics:
    - name: AlpacaEval
      type: AlpacaEval
      value: 81.37%
    source:
      name: AlpacaEval
      url: https://github.com/tatsu-lab/alpaca_eval
    task:
      type: text-generation
model_creator: Xuechen Li
model_name: Phi 2 DPO
model_type: phi-msft
pipeline_tag: text-generation
prompt_template: '### Human: {prompt}


  ### Assistant:

  '
quantized_by: TheBloke
tags:
- nlp
- code
---
<!-- markdownlint-disable MD041 -->

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# Phi 2 DPO - GGUF
- Model creator: [Xuechen Li](https://huggingface.co/lxuechen)
- Original model: [Phi 2 DPO](https://huggingface.co/lxuechen/phi-2-dpo)

<!-- description start -->
## Description

This repo contains GGUF format model files for [Xuechen Li's Phi 2 DPO](https://huggingface.co/lxuechen/phi-2-dpo).

These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).

<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/phi-2-dpo-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/phi-2-dpo-GGUF)
* [Xuechen Li's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/lxuechen/phi-2-dpo)
<!-- repositories-available end -->

<!-- prompt-template start -->
## Prompt template: SUS

```
### Human: {prompt}

### Assistant:

```

<!-- prompt-template end -->


<!-- compatibility_gguf start -->
## Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)

They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

## Explanation of quantisation methods

<details>
  <summary>Click to see details</summary>

The new methods available are:

* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->

<!-- README_GGUF.md-provided-files start -->
## Provided files

| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [phi-2-dpo.Q2_K.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q2_K.gguf) | Q2_K | 2 | 1.17 GB| 3.67 GB | smallest, significant quality loss - not recommended for most purposes |
| [phi-2-dpo.Q3_K_S.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q3_K_S.gguf) | Q3_K_S | 3 | 1.25 GB| 3.75 GB | very small, high quality loss |
| [phi-2-dpo.Q3_K_M.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q3_K_M.gguf) | Q3_K_M | 3 | 1.48 GB| 3.98 GB | very small, high quality loss |
| [phi-2-dpo.Q4_0.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q4_0.gguf) | Q4_0 | 4 | 1.60 GB| 4.10 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [phi-2-dpo.Q3_K_L.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q3_K_L.gguf) | Q3_K_L | 3 | 1.60 GB| 4.10 GB | small, substantial quality loss |
| [phi-2-dpo.Q4_K_S.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q4_K_S.gguf) | Q4_K_S | 4 | 1.61 GB| 4.11 GB | small, greater quality loss |
| [phi-2-dpo.Q4_K_M.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q4_K_M.gguf) | Q4_K_M | 4 | 1.79 GB| 4.29 GB | medium, balanced quality - recommended |
| [phi-2-dpo.Q5_0.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q5_0.gguf) | Q5_0 | 5 | 1.93 GB| 4.43 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [phi-2-dpo.Q5_K_S.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q5_K_S.gguf) | Q5_K_S | 5 | 1.93 GB| 4.43 GB | large, low quality loss - recommended |
| [phi-2-dpo.Q5_K_M.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q5_K_M.gguf) | Q5_K_M | 5 | 2.07 GB| 4.57 GB | large, very low quality loss - recommended |
| [phi-2-dpo.Q6_K.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q6_K.gguf) | Q6_K | 6 | 2.28 GB| 4.78 GB | very large, extremely low quality loss |
| [phi-2-dpo.Q8_0.gguf](https://huggingface.co/TheBloke/phi-2-dpo-GGUF/blob/main/phi-2-dpo.Q8_0.gguf) | Q8_0 | 8 | 2.95 GB| 5.45 GB | very large, extremely low quality loss - not recommended |

**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.



<!-- README_GGUF.md-provided-files end -->

<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files

**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.

The following clients/libraries will automatically download models for you, providing a list of available models to choose from:

* LM Studio
* LoLLMS Web UI
* Faraday.dev

### In `text-generation-webui`

Under Download Model, you can enter the model repo: TheBloke/phi-2-dpo-GGUF and below it, a specific filename to download, such as: phi-2-dpo.Q4_K_M.gguf.

Then click Download.

### On the command line, including multiple files at once

I recommend using the `huggingface-hub` Python library:

```shell
pip3 install huggingface-hub
```

Then you can download any individual model file to the current directory, at high speed, with a command like this:

```shell
huggingface-cli download TheBloke/phi-2-dpo-GGUF phi-2-dpo.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```

<details>
  <summary>More advanced huggingface-cli download usage (click to read)</summary>

You can also download multiple files at once with a pattern:

```shell
huggingface-cli download TheBloke/phi-2-dpo-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```

For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).

To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:

```shell
pip3 install hf_transfer
```

And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:

```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/phi-2-dpo-GGUF phi-2-dpo.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```

Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->

<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command

Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.

```shell
./main -ngl 35 -m phi-2-dpo.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Human: {prompt}\n\n### Assistant:"
```

Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.

If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

## How to run in `text-generation-webui`

Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).

## How to run from Python code

You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.

### How to load this model in Python code, using llama-cpp-python

For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).

#### First install the package

Run one of the following commands, according to your system:

```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python

# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```

#### Simple llama-cpp-python example code

```python
from llama_cpp import Llama

# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
  model_path="./phi-2-dpo.Q4_K_M.gguf",  # Download the model file first
  n_ctx=2048,  # The max sequence length to use - note that longer sequence lengths require much more resources
  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
  n_gpu_layers=35         # The number of layers to offload to GPU, if you have GPU acceleration available
)

# Simple inference example
output = llm(
  "### Human: {prompt}\n\n### Assistant:", # Prompt
  max_tokens=512,  # Generate up to 512 tokens
  stop=["</s>"],   # Example stop token - not necessarily correct for this specific model! Please check before using.
  echo=True        # Whether to echo the prompt
)

# Chat Completion API

llm = Llama(model_path="./phi-2-dpo.Q4_K_M.gguf", chat_format="llama-2")  # Set chat_format according to the model you are using
llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "You are a story writing assistant."},
        {
            "role": "user",
            "content": "Write a story about llamas."
        }
    ]
)
```

## How to use with LangChain

Here are guides on using llama-cpp-python and ctransformers with LangChain:

* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)

<!-- README_GGUF.md-how-to-run end -->

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## Discord

For further support, and discussions on these models and AI in general, join us at:

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## Thanks, and how to contribute

Thanks to the [chirper.ai](https://chirper.ai) team!

Thanks to Clay from [gpus.llm-utils.org](llm-utils)!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

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**Special thanks to**: Aemon Algiz.

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Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

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<!-- original-model-card start -->
# Original model card: Xuechen Li's Phi 2 DPO


## Model Summary

`phi-2-dpo` is an instruction-tuned model from an earlier SFT model [`phi-2-sft`](https://huggingface.co/lxuechen/phi-2-sft). Direct preference optimization (DPO) is used for fine-tuning on a 10k subset of the [UltraFeedback dataset](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized).

The purpose of the experiment is to understand the quality of the pre-trained Phi-2 model. The good news is that `phi-2-dpo` can follow open-ended user instructions well.

## Decoding

Format your prompt as
```
"""### Human: {instruction}

### Assistant:"""
```
where `instruction` is your query.


Here's a full-fledged example:

```
import torch
import transformers

model: transformers.PreTrainedModel = transformers.AutoModelForCausalLM.from_pretrained(
    "lxuechen/phi-2-dpo",
    low_cpu_mem_usage=True,
    device_map="auto",
    trust_remote_code=True,
    torch_dtype=torch.float16
)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name_or_path)

input_text = "### Human: Give me a good recipe for a chinese dish\n\n### Assistant:"

outputs = model.generate(
    tokenizer(input_text, return_tensors="pt").to(model.device)['input_ids'],
    max_length=1024,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.pad_token_id,
    eos_token_id=tokenizer.eos_token_id,
    max_new_tokens=1024
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training

The model was fine-tuned on a 10k subset of the binarized version of UltraFeedback with DPO.

Hyperparameters:
- learning rate: 3% linear warmup, with a peak of 3e-5 and cosine decay
- epochs: 2
- batch size: 64
- context length: 1024
- DPO beta: 0.1

## Limitations of `phi-2-dpo`

* Generate Inaccurate Code and Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.

* Limited Scope for code: Majority of Phi-2 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.

* Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.

* Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.

* Potential Societal Biases: Phi-2 is not entirely free from societal biases despite efforts in assuring trainig data safety. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.

* Toxicity: Despite being trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model for research purposes only -- We hope to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.

* Verbosity: Phi-2 being a base model often produces irrelevant or extra text and responses following its first answer to user prompts within a single turn. This is due to its training dataset being primarily textbooks, which results in textbook-like responses.

<!-- original-model-card end -->