Update README.md

README.md

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+
 ---
 base_model: meta-llama/Llama-2-7b-hf
 library_name: peft
 ---
 
-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for Instruction Backtranslation (Backward Model)
 
+## Model Overview
 
+This repository contains a fine-tuned version of the Llama-2-7b-hf model specifically trained for **Instruction Backtranslation**, implementing the method described in the paper "Self Alignment with Instruction Backtranslation." The model is trained as a backward model to predict original instructions given their corresponding outputs. This reverse training aims to improve model alignment and self-consistency.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
-
-
+The model utilizes the LLaMA-2 architecture fine-tuned using Low-Rank Adaptation (LoRA) techniques (PEFT library). The primary goal is to reconstruct instructions (`x`) from outputs (`y`), thus creating pairs `(y, x)` for backward prediction.
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Developed by:** Abhishek Sagar Sanda
+- **Model type:** LoRA-finetuned Causal LM
+- **Language(s):** English
+- **License:** Apache-2.0 (consistent with base LLaMA-2 model)
+- **Finetuned from:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
 
-### Model Sources [optional]
+### Model Sources
 
-<!-- Provide the basic links for the model. -->
+- **Paper:** [Self Alignment with Instruction Backtranslation](https://arxiv.org/abs/2308.06259)
+- **Base Model Repository:** [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf)
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+## Intended Uses
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+- Generating original instructions from outputs for alignment purposes.
+- Research in model alignment, self-consistency, and instruction-following behavior.
 
-### Downstream Use [optional]
+### Downstream Use
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
+- Enhancing forward instruction-following models via self-alignment methods.
+- Improving instruction tuning datasets by generating diverse instructions from desired outputs.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- 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. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
+- This model is not suited for general question-answering or generic text generation tasks.
+- Avoid using in contexts requiring high factual accuracy without additional verification.
 
-[More Information Needed]
+## Training Data
 
+The model was trained on the **OpenAssistant-Guanaco** training dataset, focusing on `(output, instruction)` pairs for backward prediction.
 
-#### Training Hyperparameters
+## Training Procedure
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+### Preprocessing
 
-#### Speeds, Sizes, Times [optional]
+- Dataset pairs were inverted to use outputs (`y`) as input and instructions (`x`) as labels.
+- Standard tokenization was applied using LLaMA's tokenizer.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+### Training Hyperparameters
 
-[More Information Needed]
+- **LoRA Rank (r):** 8
+- **LoRA Alpha:** 32
+- **LoRA Dropout:** 0.05
+- **Target Modules:** `k_proj`, `q_proj`, `v_proj`, `o_proj`
+- **Training Precision:** bf16 mixed precision
 
 ## Evaluation
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+Evaluation involved assessing the accuracy of generated instructions against the original instructions. Key metrics include BLEU, ROUGE, and qualitative human evaluations.
 
-### Testing Data, Factors & Metrics
+## Technical Specifications
 
-#### Testing Data
+### Model Architecture
 
-<!-- This should link to a Dataset Card if possible. -->
+- LLaMA-2 Transformer Architecture with PEFT LoRA Adaptation
+- **Tokenizer:** LLaMA Tokenizer (`tokenizer_class`: LlamaTokenizer)
+- **Maximum Sequence Length:** Practically unlimited (`model_max_length` is set very large)
 
-[More Information Needed]
+### Hardware
 
-#### Factors
+- GPUs: NVIDIA A100 GPUs recommended
+- Cloud Provider: AWS/GCP
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+## How to Use
 
-[More Information Needed]
+Here's a quick start guide:
 
-#### Metrics
+```python
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+base_model_name = "meta-llama/Llama-2-7b-hf"
+peft_model_name = "your_hf_model_path"
 
-[More Information Needed]
+config = PeftConfig.from_pretrained(peft_model_name)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+model = AutoModelForCausalLM.from_pretrained(base_model_name)
+model = PeftModel.from_pretrained(model, peft_model_name)
 
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+inputs = tokenizer("Output text goes here", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
 ## Environmental Impact
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-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).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
 
-[More Information Needed]
+- **Hardware Type:** NVIDIA GeForce RTX 4070 GPU
+- **Hours Used:** 3hrs
 
-#### Hardware
 
-[More Information Needed]
+## Citation
 
-#### Software
+If you use this model, please cite:
 
-[More Information Needed]
+```bibtex
+@article{xu2023selfalignment,
+  title={Self Alignment with Instruction Backtranslation},
+  author={Xu, et al.},
+  journal={arXiv preprint arXiv:2308.06259},
+  year={2023}
+}
+```
 
-## Citation [optional]
+## Model Card Author
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+- Abhishek Sagar Sanda
 
 ## Model Card Contact
 
-[More Information Needed]
+- [email protected]
+
 ### Framework versions
 
-- PEFT 0.15.1
+- PEFT 0.15.1
+- Transformers 4.38.1