--- language: - en library_name: transformers tags: - gpt - llm - large language model - h2o-llmstudio inference: false thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico --- # Model Card ## Summary This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio). - Base model: [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) - Trained on 168 prompts that generate in order to generate a multiple question choices responses (https://huggingface.co/datasets/fbellame/pdf_to_quizz_llama_13B) ``` You are a teacher preparing questions for a quiz. Given the following document, please generate 1 multiple-choice questions (MCQs) with 4 options and a corresponding answer letter based on the document. Example question: Question: question here CHOICE_A: choice here CHOICE_B: choice here CHOICE_C: choice here CHOICE_D: choice here Answer: A or B or C or D These questions should be detailed and solely based on the information provided in the document. In 1229, the King had to struggle with a long lasting strike at the University of Paris. The Quartier Latin was strongly hit by these strikes. " question: What was the cause of the strike at the University of Paris in 1229? A: The King's interference in university affairs B: A shortage of resources for the university C: A disagreement between faculty members D: The Quartier Latin being strongly hit by a natural disaster reponse: B ``` ## Training: You can find a youtube video here explaining the fine tuning process: https://youtu.be/gXXkLVfiBVQ?si=b-RNVykuOLDPaTHb ## Source code You can find a github project here using this model https://github.com/fbellame/pdf-to-quizz/tree/feature/local_model (branch local_model and https://github.com/fbellame/pdf-to-quizz/tree/feature/tgi) ## Usage To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed. ```bash pip install transformers==4.31.0 ``` Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo. - Either leave `token=True` in the `pipeline` and login to hugginface_hub by running ```python import huggingface_hub huggingface_hub.login() ``` - Or directly pass your to `token` in the `pipeline` ```python from transformers import pipeline generate_text = pipeline( model="fbellame/llama2-pdf-to-quizz-13b", torch_dtype="auto", trust_remote_code=True, use_fast=True, device_map={"": "cuda:0"}, token=True, ) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer: ```python print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"]) ``` ```bash <|prompt|>Why is drinking water so healthy?<|answer|> ``` Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`. ```python from h2oai_pipeline import H2OTextGenerationPipeline from transformers import AutoModelForCausalLM, AutoTokenizer tokenizer = AutoTokenizer.from_pretrained( "fbellame/llama2-pdf-to-quizz-13b", use_fast=True, padding_side="left", trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( "fbellame/llama2-pdf-to-quizz-13b", torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer) res = generate_text( "Why is drinking water so healthy?", min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True ) print(res[0]["generated_text"]) ``` You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps: ```python from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "fbellame/llama2-pdf-to-quizz-13b" # either local folder or huggingface model name # Important: The prompt needs to be in the same format the model was trained with. # You can find an example prompt in the experiment logs. prompt = "<|prompt|>How are you?<|answer|>" tokenizer = AutoTokenizer.from_pretrained( model_name, use_fast=True, trust_remote_code=True, ) model = AutoModelForCausalLM.from_pretrained( model_name, torch_dtype="auto", device_map={"": "cuda:0"}, trust_remote_code=True, ) model.cuda().eval() inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda") # generate configuration can be modified to your needs tokens = model.generate( input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], min_new_tokens=2, max_new_tokens=256, do_sample=False, num_beams=1, temperature=float(0.3), repetition_penalty=float(1.2), renormalize_logits=True )[0] tokens = tokens[inputs["input_ids"].shape[1]:] answer = tokenizer.decode(tokens, skip_special_tokens=True) print(answer) ``` ## Quantization and sharding You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```. ## Model Architecture ``` LlamaForCausalLM( (model): LlamaModel( (embed_tokens): Embedding(32000, 5120, padding_idx=0) (layers): ModuleList( (0-39): 40 x LlamaDecoderLayer( (self_attn): LlamaAttention( (q_proj): Linear(in_features=5120, out_features=5120, bias=False) (k_proj): Linear(in_features=5120, out_features=5120, bias=False) (v_proj): Linear(in_features=5120, out_features=5120, bias=False) (o_proj): Linear(in_features=5120, out_features=5120, bias=False) (rotary_emb): LlamaRotaryEmbedding() ) (mlp): LlamaMLP( (gate_proj): Linear(in_features=5120, out_features=13824, bias=False) (up_proj): Linear(in_features=5120, out_features=13824, bias=False) (down_proj): Linear(in_features=13824, out_features=5120, bias=False) (act_fn): SiLUActivation() ) (input_layernorm): LlamaRMSNorm() (post_attention_layernorm): LlamaRMSNorm() ) ) (norm): LlamaRMSNorm() ) (lm_head): Linear(in_features=5120, out_features=32000, bias=False) ) ``` ## Model Configuration This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models. ## Disclaimer Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions. - Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints. - Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion. - Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model. - Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities. - Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. 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