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README.md

@@ -1,193 +1,43 @@
 ---
-library_name: peft
+license: apache-2.0
+tags:
+- trl
+- ppo
+- transformers
+- reinforcement-learning
 ---
-<div style="text-align: center">
-<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl_banner_dark.png">
-</div>
 
-# TRL - Transformer Reinforcement Learning
-> Full stack transformer language models with reinforcement learning.
+# TRL Model
 
-<p align="center">
-    <a href="https://github.com/huggingface/trl/blob/main/LICENSE">
-        <img alt="License" src="https://img.shields.io/github/license/huggingface/trl.svg?color=blue">
-    </a>
-    <a href="https://huggingface.co/docs/trl/index">
-        <img alt="Documentation" src="https://img.shields.io/website/http/huggingface.co/docs/trl/index.svg?down_color=red&down_message=offline&up_message=online">
-    </a>
-    <a href="https://github.com/huggingface/trl/releases">
-        <img alt="GitHub release" src="https://img.shields.io/github/release/huggingface/trl.svg">
-    </a>
-</p>
+This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
+ guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
 
+## Usage
 
-## What is it?
+To use this model for inference, first install the TRL library:
 
-<div style="text-align: center">
-<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/TRL-readme.png">
-</div>
-
-`trl` is a full stack library where we provide a set of tools to train transformer language models and stable diffusion models with Reinforcement Learning, from the Supervised Fine-tuning step (SFT), Reward Modeling step (RM) to the Proximal Policy Optimization (PPO) step. The library is built on top of the [`transformers`](https://github.com/huggingface/transformers) library by  🤗 Hugging Face. Therefore, pre-trained language models can be directly loaded via `transformers`. At this point, most of decoder architectures and encoder-decoder architectures are supported. Refer to the documentation or the `examples/` folder for example code snippets and how to run these tools.
-
-**Highlights:**
-
-- [`SFTTrainer`](https://huggingface.co/docs/trl/sft_trainer): A light and friendly wrapper around `transformers` Trainer to easily fine-tune language models or adapters on a custom dataset.
-- [`RewardTrainer`](https://huggingface.co/docs/trl/reward_trainer): A light wrapper around `transformers` Trainer to easily fine-tune language models for human preferences (Reward Modeling).
-- [`PPOTrainer`](https://huggingface.co/docs/trl/trainer#trl.PPOTrainer): A PPO trainer for language models that just needs (query, response, reward) triplets to optimise the language model.
-- [`AutoModelForCausalLMWithValueHead`](https://huggingface.co/docs/trl/models#trl.AutoModelForCausalLMWithValueHead) & [`AutoModelForSeq2SeqLMWithValueHead`](https://huggingface.co/docs/trl/models#trl.AutoModelForSeq2SeqLMWithValueHead): A transformer model with an additional scalar output for each token which can be used as a value function in reinforcement learning.
-- [Examples](https://github.com/huggingface/trl/tree/main/examples): Train GPT2 to generate positive movie reviews with a BERT sentiment classifier, full RLHF using adapters only, train GPT-j to be less toxic, [Stack-Llama example](https://huggingface.co/blog/stackllama), etc.
-
-## How PPO works
-Fine-tuning a language model via PPO consists of roughly three steps:
-
-1. **Rollout**: The language model generates a response or continuation based on query which could be the start of a sentence.
-2. **Evaluation**: The query and response are evaluated with a function, model, human feedback or some combination of them. The important thing is that this process should yield a scalar value for each query/response pair.
-3. **Optimization**: This is the most complex part. In the optimisation step the query/response pairs are used to calculate the log-probabilities of the tokens in the sequences. This is done with the model that is trained and a reference model, which is usually the pre-trained model before fine-tuning. The KL-divergence between the two outputs is used as an additional reward signal to make sure the generated responses don't deviate too far from the reference language model. The active language model is then trained with PPO.
-
-This process is illustrated in the sketch below:
-
-
-<div style="text-align: center">
-<img src="https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/trl_overview.png" width="800">
-<p style="text-align: center;"> <b>Figure:</b> Sketch of the workflow. </p>
-</div>
-
-## Installation
-
-### Python package
-Install the library with pip:
 ```bash
-pip install trl
+python -m pip install trl
 ```
 
-### From source
-If you want to run the examples in the repository a few additional libraries are required. Clone the repository and install it with pip:
-```bash
-git clone https://github.com/huggingface/trl.git
-cd trl/
-pip install .
-```
-
-If you wish to develop TRL, you should install in editable mode:
-```bash
-pip install -e .
-```
-
-## How to use
-
-### `SFTTrainer`
-
-This is a basic example on how to use the `SFTTrainer` from the library. The `SFTTrainer` is a light wrapper around the `transformers` Trainer to easily fine-tune language models or adapters on a custom dataset.
+You can then generate text as follows:
 
 ```python
-# imports
-from datasets import load_dataset
-from trl import SFTTrainer
+from transformers import pipeline
 
-# get dataset
-dataset = load_dataset("imdb", split="train")
-
-# get trainer
-trainer = SFTTrainer(
-    "facebook/opt-350m",
-    train_dataset=dataset,
-    dataset_text_field="text",
-    max_seq_length=512,
-)
-
-# train
-trainer.train()
+generator = pipeline("text-generation", model="AdoubleLen//tmp/tmp8useznn3/AdoubleLen/trl")
+outputs = generator("Hello, my llama is cute")
 ```
 
-### `RewardTrainer`
-
-This is a basic example on how to use the `RewardTrainer` from the library. The `RewardTrainer` is a wrapper around the `transformers` Trainer to easily fine-tune reward models or adapters on a custom preference dataset.
+If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
 
 ```python
-# imports
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-from trl import RewardTrainer
-
-# load model and dataset - dataset needs to be in a specific format
-model = AutoModelForSequenceClassification.from_pretrained("gpt2", num_labels=1)
-tokenizer = AutoTokenizer.from_pretrained("gpt2")
-
-...
-
-# load trainer
-trainer = RewardTrainer(
-    model=model,
-    tokenizer=tokenizer,
-    train_dataset=dataset,
-)
-
-# train
-trainer.train()
-```
-
-### `PPOTrainer`
-
-This is a basic example on how to use the `PPOTrainer` from the library. Based on a query the language model creates a response which is then evaluated. The evaluation could be a human in the loop or another model's output.
-
-```python
-# imports
-import torch
 from transformers import AutoTokenizer
-from trl import PPOTrainer, PPOConfig, AutoModelForCausalLMWithValueHead, create_reference_model
-from trl.core import respond_to_batch
-
-# get models
-model = AutoModelForCausalLMWithValueHead.from_pretrained('gpt2')
-model_ref = create_reference_model(model)
-
-tokenizer = AutoTokenizer.from_pretrained('gpt2')
-
-# initialize trainer
-ppo_config = PPOConfig(
-    batch_size=1,
-)
-
-# encode a query
-query_txt = "This morning I went to the "
-query_tensor = tokenizer.encode(query_txt, return_tensors="pt")
+from trl import AutoModelForCausalLMWithValueHead
 
-# get model response
-response_tensor  = respond_to_batch(model, query_tensor)
+tokenizer = AutoTokenizer.from_pretrained("AdoubleLen//tmp/tmp8useznn3/AdoubleLen/trl")
+model = AutoModelForCausalLMWithValueHead.from_pretrained("AdoubleLen//tmp/tmp8useznn3/AdoubleLen/trl")
 
-# create a ppo trainer
-ppo_trainer = PPOTrainer(ppo_config, model, model_ref, tokenizer)
-
-# define a reward for response
-# (this could be any reward such as human feedback or output from another model)
-reward = [torch.tensor(1.0)]
-
-# train model for one step with ppo
-train_stats = ppo_trainer.step([query_tensor[0]], [response_tensor[0]], reward)
+inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
+outputs = model(**inputs, labels=inputs["input_ids"])
 ```
-
-## References
-
-### Proximal Policy Optimisation
-The PPO implementation largely follows the structure introduced in the paper **"Fine-Tuning Language Models from Human Preferences"** by D. Ziegler et al. \[[paper](https://arxiv.org/pdf/1909.08593.pdf), [code](https://github.com/openai/lm-human-preferences)].
-
-### Language models
-The language models utilize the `transformers` library by 🤗 Hugging Face.
-
-## Citation
-
-```bibtex
-@misc{vonwerra2022trl,
-  author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang},
-  title = {TRL: Transformer Reinforcement Learning},
-  year = {2020},
-  publisher = {GitHub},
-  journal = {GitHub repository},
-  howpublished = {\url{https://github.com/huggingface/trl}}
-}
-```
-## Training procedure
-
-### Framework versions
-
-
-- PEFT 0.5.0

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special_tokens_map.json

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tokenizer_config.json

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+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

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