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README.md

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 ---
-license: apache-2.0
+license: bigscience-openrail-m
+language:
+- en
+inference: false
+tags:
+- trl
+- transformers
+- rlhf
+datasets:
+- lvwerra/stack-exchange-paired
 ---
+
+![pull_figure](https://huggingface.co/datasets/trl-internal-testing/example-images/resolve/main/images/stack-llama.png)
+
+# Llama-se-rl-peft
+Adapter weights of a Reinforcement Learning fine-tuned model based on the LLaMA model (see [Meta's LLaMA release](https://ai.facebook.com/blog/large-language-model-llama-meta-ai) for the original LLaMA model). 
+The model is designed to generate human-like responses to questions in Stack Exchange domains of programming, mathematics, physics, and more.
+For more info check out the [blog post](https://huggingface.co/blog/stackllama) and [github example](https://github.com/lvwerra/trl/tree/main/examples/stack_llama/scripts).
+
+## Model Details
+
+### Model Description
+**Developed by:** Hugging Face
+
+**Model type:** An auto-regressive language model based on the transformer architecture, and fine-tuned with [Stack Exchange datasets](https://huggingface.co/datasets/lvwerra/stack-exchange-paired). 
+
+**Languages:** Predominantly English, with additional data from languages with the following ISO codes: 
+
+| bg | ca | cs | da | de | es | fr | hr | hu | it | nl | pl | pt | ro | ru | sl | sr | sv | uk |
+| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | 
+
+
+**License:** [bigscience-openrail-m](https://drive.google.com/file/d/16NqKiAkzyZ55NClubCIFup8pT2jnyVIo/view?usp=sharing)
+
+**Finetuned from:** [LLaMA](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md)
+
+### Model Sources
+**Repository:** [https://huggingface.co/trl-lib/llama-7b-se-rl-peft/tree/main](https://huggingface.co/trl-lib/llama-7b-se-rl-peft/tree/main)
+
+**Base Model Repository:** [https://github.com/facebookresearch/llama](https://github.com/facebookresearch/llama)
+
+**Demo:** [https://huggingface.co/spaces/trl-lib/stack-llama](https://huggingface.co/spaces/trl-lib/stack-llama)
+
+## Uses
+
+### Direct Use
+- Long-form question-answering on topics of programming, mathematics, and physics
+- Demonstrating a Large Language Model's ability to follow target behavior of generating answers to a question that would be highly rated on [Stack Exchange](https://stackexchange.com).
+
+### Out of Scope Use
+- Replacing human expertise
+
+
+## Bias, Risks, and Limitations
+- Inherits bias, risks, and limitations from the LLaMA model, as described in the [LLaMA Model Card Bias Evaluation](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#quantitative-analysis) and [Ethical Considerations](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#ethical-considerations). 
+- Retains biases present in the Stack Exchange dataset. Per the [latest developer survey for Stack Overflow](https://survey.stackoverflow.co/2022/),
+which constitutes a significant part of the StackExchange data,
+most users who answered the survey identified themselves as [White or European, men, between 25 and 34 years old, and based in the US (with a significant part of responders from India).](https://survey.stackoverflow.co/2022/#developer-profile-demographics)
+- May generate answers that are incorrect or misleading.
+- May copy answers from the training data verbatim.
+- May generate language that is hateful or promotes discrimination ([example](https://huggingface.co/trl-lib/llama-7b-se-rl-peft/discussions/7#64376083369f6f907f5bfe4c)).
+- May generate language that is offensive to direct or indirect users or to people or groups mentioned.
+
+
+### Recommendations
+- Answers should be validated through the use of external sources.
+- Disparities between the data contributors and the direct and indirect users of the technology should inform developers in assessing what constitutes an appropriate use case.
+- Further research is needed to attribute model generations to sources in the training data, especially in cases where the model copies answers from the training data. 
+
+## Training Details
+
+### Training Data
+Original datasets are described in [the LLaMA Model Card](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md#training-dataset).
+Fine-tuning datasets for this model are based on [Stack Exchange Paired](https://huggingface.co/datasets/lvwerra/stack-exchange-paired), which consists of questions and answers from various domains in Stack Exchange, such as programming, mathematics, physics, and more. Specifically:
+
+**Traditional Fine-tuning:** [https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main/data/finetune](https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main/data/finetune)
+
+**RL Fine-tuning:** [https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main/data/rl](https://huggingface.co/datasets/lvwerra/stack-exchange-paired/tree/main/data/rl)
+
+**Reward Model:** [https://huggingface.co/trl-lib/llama-7b-se-rm-peft](https://huggingface.co/trl-lib/llama-7b-se-rm-peft)
+
+### Training Procedure
+The model was first fine-tuned on the Stack Exchange question and answer pairs and then RL fine-tuned using a Stack Exchange Reward Model. 
+It is trained to respond to prompts with the following template:
+
+```
+Question: <Query> 
+
+Answer: <Response>
+```
+
+
+## Citation
+
+**BibTeX:**
+```
+@misc {beeching2023stackllama,
+ author = { Edward Beeching and
+ Younes Belkada and
+ Kashif Rasul and
+ Lewis Tunstall and
+ Leandro von Werra and
+ Nazneen Rajani and
+ Nathan Lambert
+ },
+ title = { StackLLaMa: An RL Fine-tuned LLaMa Model for Stack Exchange Question and Answering },
+ year = 2023,
+ url = { https://huggingface.co/trl-lib/llama-7b-se-rl-peft },
+ doi = { 10.57967/hf/0513 },
+ publisher = { Hugging Face Blog }
+}
+```
+
+## Model Card Authors
+[Nathan Lambert](https://huggingface.co/natolambert), [Leandro von Werra](https://huggingface.co/lvwerra), [Edward Beeching](https://huggingface.co/edbeeching), [Kashif Rasul](https://huggingface.co/kashif), [Younes Belkada](https://huggingface.co/ybelkada), [Margaret Mitchell](https://huggingface.co/meg)

adapter_config.json

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+ "modules_to_save": null,
+ "peft_type": "LORA",
+ "r": 16,
+ "target_modules": [
+ "q_proj",
+ "v_proj"
+ ],
+ "task_type": "CAUSAL_LM"
+}

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special_tokens_map.json

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tokenizer_config.json

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+}