Update README.md

README.md

@@ -1,24 +1,43 @@
 ---
-license: apache-2.0
+inference: false
+license: mit
+license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
 tags:
 - moe
-- merge
-- mergekit
-- lazymergekit
+- nlp
+- code
 - cognitivecomputations/dolphin-2_6-phi-2
 - lxuechen/phi-2-dpo
+- Yhyu13/phi-2-sft-dpo-gpt4_en-ep1
+- mrm8488/phi-2-coder
 ---
 
 ![](https://i.imgur.com/UOb2fvh.jpg)
 
-# phixtral-2x2.8
+# phixtral-4x2_8
 
-phixtral-2x2.8 is a Mixure of Experts (MoE) made with the following models using a custom version of mergekit:
-* [cognitivecomputations/dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)
-* [lxuechen/phi-2-dpo](https://huggingface.co/lxuechen/phi-2-dpo)
+phixtral-4x2_8 is the first Mixure of Experts (MoE) made with four [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.
+
+## 🏆 Evaluation
+
+|                             Model                              |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
+|----------------------------------------------------------------|------:|------:|---------:|-------:|------:|
+|[**phixtral-4x2_8**](https://huggingface.co/mlabonne/phixtral-2x2_8)|   TBD|  TBD|     TBD|   TBD|  TBD|
+|[dolphin-2_6-phi-2](https://huggingface.co/cognitivecomputations/dolphin-2_6-phi-2)|  33.12|  69.85|     47.39|    37.2|  46.89|
+|[phi-2-dpo](https://huggingface.co/lxuechen/phi-2-dpo)|  30.39|  71.68|     50.75|    34.9|  46.93|
+|[phi-2-sft-dpo-gpt4_en-ep1](https://huggingface.co/Yhyu13/phi-2-sft-dpo-gpt4_en-ep1)|  30.61|  71.13|     48.74|   35.23|  46.43|
+|[phi-2-coder](https://huggingface.co/mrm8488/phi-2-coder)|   TBD|  TBD|     TBD|   TBD|  TBD|
+|[phi-2](https://huggingface.co/microsoft/phi-2)|  27.98|   70.8|     44.43|   35.21|  44.61|
+
+Check [YALL - Yet Another LLM Leaderboard](https://huggingface.co/spaces/mlabonne/Yet_Another_LLM_Leaderboard) to compare it with other models.
 
 ## 🧩 Configuration
 
+The model has been made with a custom version of the [mergekit](https://github.com/cg123/mergekit) library (mixtral branch) and the following configuration:
+
 ```yaml
 base_model: cognitivecomputations/dolphin-2_6-phi-2
 gate_mode: cheap_embed
@@ -27,8 +46,67 @@ experts:
     positive_prompts: [""]
   - source_model: lxuechen/phi-2-dpo
     positive_prompts: [""]
+  - source_model: Yhyu13/phi-2-sft-dpo-gpt4_en-ep1
+    positive_prompts: [""]
+  - source_model: mrm8488/phi-2-coder
+    positive_prompts: [""]
 ```
 
 ## 💻 Usage
 
-This architecture is not compatible with the transformers library. I'm working on hacking something to run it. Contact me if you're interested!
+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.
+
+```python
+!pip install -q --upgrade transformers einops accelerate bitsandbytes
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "phixtral-4x2_8"
+instruction = '''
+    def print_prime(n):
+        """
+        Print all primes between 1 and n
+        """
+'''
+
+torch.set_default_device("cuda")
+
+# Load the model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    f"mlabonne/{model_name}", 
+    torch_dtype="auto", 
+    load_in_4bit=True, 
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    f"mlabonne/{model_name}", 
+    trust_remote_code=True
+)
+
+# Tokenize the input string
+inputs = tokenizer(
+    instruction, 
+    return_tensors="pt", 
+    return_attention_mask=False
+)
+
+# Generate text using the model
+outputs = model.generate(**inputs, max_length=200)
+
+# Decode and print the output
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+```
+
+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-4x2_8/blob/main/config.json#L26-L27) file (2 and 4 by default). This configuration is automatically loaded in `configuration.py`.
+
+[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-4x2_8/blob/main/modeling_phi.py#L293-L317).
+
+## 🤝 Acknowledgments
+
+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.
+
+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/).
+
+Thanks to [ehartford](https://huggingface.co/ehartford), [lxuechen](https://huggingface.co/lxuechen), [Yhyu13](https://huggingface.co/Yhyu13), and [mrm8488](https://huggingface.co/mrm8488) for their fine-tuned phi-2 models.