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--- |
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inference: false |
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license: mit |
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license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE |
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language: |
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- en |
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pipeline_tag: text-generation |
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tags: |
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- moe |
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- nlp |
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- code |
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- cognitivecomputations/dolphin-2_6-phi-2 |
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- lxuechen/phi-2-dpo |
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--- |
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# phixtral-2x2_8 |
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phixtral-2x2_8 is the first Mixure of Experts (MoE) made with two [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) models, inspired by the [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) architecture. It performs better than each individual expert. |
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You can try it out using this [Space](https://huggingface.co/spaces/mlabonne/phixtral-chat). |
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## 🏆 Evaluation |
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The evaluation was performed using [LLM AutoEval](https://github.com/mlabonne/llm-autoeval) on Nous suite. |
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| Model |AGIEval|GPT4All|TruthfulQA|Bigbench|Average| |
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|----------------------------------------------------------------|------:|------:|---------:|-------:|------:| |
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|[**phixtral-2x2_8**](https://huggingface.co/mlabonne/phixtral-2x2_8)| **34.1**| **70.44**| **48.78**| **37.82**| **47.78**| |
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|[dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)| 33.12| 69.85| 47.39| 37.2| 46.89| |
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|[phi-2-dpo](https://huggingface.co/lxuechen/phi-2-dpo)| 30.39| 71.68| 50.75| 34.9| 46.93| |
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|[phi-2](https://huggingface.co/microsoft/phi-2)| 27.98| 70.8| 44.43| 35.21| 44.61| |
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Check [YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard) to compare it with other models. |
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## 🧩 Configuration |
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The model has been made with a custom version of the [mergekit](https://github.com/cg123/mergekit) library (mixtral branch) and the following configuration: |
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```yaml |
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base_model: cognitivecomputations/dolphin-2_6-phi-2 |
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gate_mode: cheap_embed |
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experts: |
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- source_model: cognitivecomputations/dolphin-2_6-phi-2 |
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positive_prompts: [""] |
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- source_model: lxuechen/phi-2-dpo |
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positive_prompts: [""] |
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``` |
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## 💻 Usage |
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Here's a [Colab notebook](https://colab.research.google.com/drive/1k6C_oJfEKUq0mtuWKisvoeMHxTcIxWRa?usp=sharing) to run Phixtral in 4-bit precision on a free T4 GPU. |
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```python |
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!pip install -q --upgrade transformers einops accelerate bitsandbytes |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "phixtral-2x2_8" |
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instruction = ''' |
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def print_prime(n): |
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""" |
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Print all primes between 1 and n |
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""" |
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''' |
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torch.set_default_device("cuda") |
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# Load the model and tokenizer |
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model = AutoModelForCausalLM.from_pretrained( |
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f"mlabonne/{model_name}", |
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torch_dtype="auto", |
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load_in_4bit=True, |
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trust_remote_code=True |
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) |
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tokenizer = AutoTokenizer.from_pretrained( |
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f"mlabonne/{model_name}", |
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trust_remote_code=True |
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) |
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# Tokenize the input string |
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inputs = tokenizer( |
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instruction, |
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return_tensors="pt", |
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return_attention_mask=False |
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) |
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# Generate text using the model |
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outputs = model.generate(**inputs, max_length=200) |
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# Decode and print the output |
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text = tokenizer.batch_decode(outputs)[0] |
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print(text) |
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``` |
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Inspired by [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1), you can specify the `num_experts_per_tok` and `num_local_experts` in the [`config.json`](https://huggingface.co/mlabonne/phixtral-2x2_8/blob/main/config.json#L26-L27) file (2 for both by default). This configuration is automatically loaded in `configuration.py`. |
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[vince62s](https://huggingface.co/vince62s) implemented the MoE inference code in the `modeling_phi.py` file. In particular, see the [MoE class](https://huggingface.co/mlabonne/phixtral-2x2_8/blob/main/modeling_phi.py#L293-L317). |
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## 🤝 Acknowledgments |
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A special thanks to [vince62s](https://huggingface.co/vince62s) for the inference code and the dynamic configuration of the number of experts. He was very patient and helped me to debug everything. |
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Thanks to [Charles Goddard](https://github.com/cg123) for the [mergekit](https://github.com/cg123/mergekit) library and the implementation of the [MoE for clowns](https://goddard.blog/posts/clown-moe/). |
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Thanks to [ehartford](https://huggingface.co/ehartford) and [lxuechen](https://huggingface.co/lxuechen) for their fine-tuned phi-2 models. |
