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  ---
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  library_name: transformers
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- tags: []
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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-
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- [More Information Needed]
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- [More Information Needed]
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- [More Information Needed]
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- [More Information Needed]
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- [More Information Needed]
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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- [More Information Needed]
 
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  ---
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  library_name: transformers
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+ license: mit
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+ base_model: microsoft/phi-2
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+ datasets:
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+ - teknium/OpenHermes-2.5
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+ - ContextualAI/ultrafeedback_clair_32k
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+ pipeline_tag: text-generation
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  ---
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+ # phi-2-instruct-apo
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+
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+ 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.
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+
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+ - Supervised Fine-Tuning
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+ - SFT Model: [phi-2-sft](https://huggingface.co/rasyosef/phi-2-sft-openhermes-128k-v2)
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+ - Used 128,000 instruction, response pairs from the [teknium/OpenHermes-2.5](https://huggingface.co/datasets/teknium/OpenHermes-2.5) dataset
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+
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+ - Anchored Preference Optimization (APO)
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+ - LoRA Adapter: [phi-2-apo](https://huggingface.co/rasyosef/phi-2-apo)
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+ - Used 10,000 preference pairs from the [ContextualAI/ultrafeedback_clair_32k](https://huggingface.co/datasets/ContextualAI/ultrafeedback_clair_32k) dataset
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+
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+ ## How to use
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+ ### Chat Format
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+ Given the nature of the training data, the phi-2 instruct model is best suited for prompts using the chat format as follows.
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+ You can provide the prompt as a question with a generic template as follows:
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+ ```markdown
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+ <|im_start|>system
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+ You are a helpful assistant.<|im_end|>
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+ <|im_start|>user
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+ Question?<|im_end|>
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+ <|im_start|>assistant
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+ ```
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+
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+ For example:
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+ ```markdown
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+ <|im_start|>system
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+ You are a helpful assistant.<|im_end|>
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+ <|im_start|>user
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+ How to explain Internet for a medieval knight?<|im_end|>
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+ <|im_start|>assistant
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+ ```
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+ where the model generates the text after `<|im_start|>assistant` .
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+
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+ ### Sample inference code
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+ This code snippets show how to get quickly started with running the model on a GPU:
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+ ```python
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+ import torch
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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+
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+ torch.random.manual_seed(0)
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+ model_id = "rasyosef/phi-2-instruct-apo"
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+ model = AutoModelForCausalLM.from_pretrained(
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+ model_id,
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+ device_map="cuda",
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+ torch_dtype="auto"
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+ )
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+
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+ messages = [
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+ {"role": "system", "content": "You are a helpful AI assistant."},
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+ {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
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+ {"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."},
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+ {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
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+ ]
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+
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+ pipe = pipeline(
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+ "text-generation",
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+ model=model,
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+ tokenizer=tokenizer,
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+ )
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+ generation_args = {
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+ "max_new_tokens": 256,
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+ "return_full_text": False,
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+ "temperature": 0.0,
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+ "do_sample": False,
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+ }
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+ output = pipe(messages, **generation_args)
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+ print(output[0]['generated_text'])
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+ ```
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+ Note: If you want to use flash attention, call _AutoModelForCausalLM.from_pretrained()_ with _attn_implementation="flash_attention_2"_