rasyosef
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phi-2-instruct-apo

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 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+license: mit
+base_model: microsoft/phi-2
+datasets:
+- teknium/OpenHermes-2.5
+- ContextualAI/ultrafeedback_clair_32k
+pipeline_tag: text-generation
 ---
+# phi-2-instruct-apo
+This is a finetuned version of Microsoft's 2.7B parameter [phi-2](https://huggingface.co/microsoft/phi-2) transfromer model that has underwent a post-training process that incorporates both **supervised fine-tuning** and **anchored preference optimization** for instruction following. I used the [trl](https://huggingface.co/docs/trl/en/index) library and a single **A100 40GB** GPU during both the SFT and APO steps.
+- Supervised Fine-Tuning
+  - SFT Model: [phi-2-sft](https://huggingface.co/rasyosef/phi-2-sft-openhermes-128k-v2)
+  - Used 128,000 instruction, response pairs from the [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) dataset
+- Anchored Preference Optimization (APO)
+  - LoRA Adapter: [phi-2-apo](https://huggingface.co/rasyosef/phi-2-apo)
+  - Used 10,000 preference pairs from the [ContextualAI/ultrafeedback_clair_32k](https://huggingface.co/datasets/ContextualAI/ultrafeedback_clair_32k) dataset
+## How to use
+### Chat Format
+Given the nature of the training data, the phi-2 instruct model is best suited for prompts using the chat format as follows.
+You can provide the prompt as a question with a generic template as follows:
+```markdown
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+Question?<|im_end|>
+<|im_start|>assistant
+```
+For example:
+```markdown
+<|im_start|>system
+You are a helpful assistant.<|im_end|>
+<|im_start|>user
+How to explain Internet for a medieval knight?<|im_end|>
+<|im_start|>assistant
+```
+where the model generates the text after `<|im_start|>assistant` .
+### Sample inference code
+This code snippets show how to get quickly started with running the model on a GPU:
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+torch.random.manual_seed(0)
+model_id = "rasyosef/phi-2-instruct-apo"
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    device_map="cuda",
+    torch_dtype="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant."},
+    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
+    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
+    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
+]
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+generation_args = {
+    "max_new_tokens": 256,
+    "return_full_text": False,
+    "temperature": 0.0,
+    "do_sample": False,
+}
+output = pipe(messages, **generation_args)
+print(output[0]['generated_text'])
+```
+Note: If you want to use flash attention, call _AutoModelForCausalLM.from_pretrained()_ with _attn_implementation="flash_attention_2"_