--- library_name: peft base_model: shpotes/codegen-350M-mono datasets: - flytech/python-codes-25k pipeline_tag: text-generation tags: - code - text-generation-inference --- # Model Card for Model ID ## Model Details ### Model Description - **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] ### Model Sources [optional] - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses ### Direct Use [More Information Needed] ### Downstream Use [optional] [More Information Needed] ### Out-of-Scope Use [More Information Needed] ## Bias, Risks, and Limitations [More Information Needed] ### Recommendations 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 import torch from transformers import AutoModelForCausalLM from peft import PeftModel, PeftConfig from transformers import AutoModelForCausalLM config = PeftConfig.from_pretrained("yamete4/codegen-350M-mono-QLoRa-flytech") model = AutoModelForCausalLM.from_pretrained("shpotes/codegen-350M-mono") model = PeftModel.from_pretrained(model, "yamete4/codegen-350M-mono-QLoRa-flytech") input_ids = tokenizer( context, truncation=True, padding=True, return_tensors='pt', pad_token_id=pad_token_id, ).input_ids input_ids_len = input_ids.shape[1] with torch.no_grad(): input_ids = input_ids tokens = model.generate( input_ids, do_sample=True, num_return_sequences=num_return_sequences, temperature=temp, max_length=input_ids_len + max_length_sample, top_p=top_p, use_cache=True, ) text = tokenizer.batch_decode(tokens[:, input_ids_len:, ...]) [More Information Needed] ## Training Details ### Training Data [More Information Needed] ### Training Procedure #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed] #### Speeds, Sizes, Times [optional] [More Information Needed] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data [More Information Needed] #### Factors [More Information Needed] #### Metrics [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] [More Information Needed] ## Environmental Impact 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 [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional] [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.9.0