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--- |
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license: other |
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language: |
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- en |
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library_name: transformers |
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pipeline_tag: text-generation |
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tags: |
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- falcon |
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- falcon-7b |
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- prompt answering |
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- peft |
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--- |
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## Model Card for Model ID |
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This repository contains further fine-tuned Falcon-7B model on conversations and question answering prompts. |
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**I used falcon-7b (https://huggingface.co/tiiuae/falcon-7b) as a base model, so this model has the same license with Falcon-7b model (Apache-2.0)** |
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## Model Details |
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Anyone can use (ask prompts) and play with the model using the pre-existing Jupyter Notebook in the **noteboooks** folder. The Jupyter Notebook contains example code to load the model and ask prompts to it as well as example prompts to get you started. |
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### Model Description |
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The tiiuae/falcon-7b model was finetuned on conversations and question answering prompts. |
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**Developed by:** [More Information Needed] |
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**Shared by:** [More Information Needed] |
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**Model type:** Causal LM |
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**Language(s) (NLP):** English, multilingual |
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**License:** Apache-2.0 |
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**Finetuned from model:** tiiuae/falcon-7b |
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## Model Sources [optional] |
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**Repository:** [More Information Needed] |
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**Paper:** [More Information Needed] |
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**Demo:** [More Information Needed] |
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## Uses |
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The model can be used for prompt answering |
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### Direct Use |
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The model can be used for prompt answering |
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### Downstream Use |
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Generating text and prompt answering |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Usage |
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## Creating prompt |
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The model was trained on the following kind of prompt: |
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```python |
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def generate_prompt(prompt: str) -> str: |
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return f""" |
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<human>: {prompt} |
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<assistant>: |
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""".strip() |
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``` |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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1. You can git clone the repo, which contains also the artifacts for the base model for simplicity and completeness, and run the following code snippet to load the mode: |
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```python |
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import torch |
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from peft import PeftConfig, PeftModel |
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from transformers import GenerationConfig, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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MODEL_NAME = "." |
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config = PeftConfig.from_pretrained(MODEL_NAME) |
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compute_dtype = getattr(torch, "float16") |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=compute_dtype, |
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bnb_4bit_use_double_quant=True, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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config.base_model_name_or_path, |
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quantization_config=bnb_config, |
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device_map="auto", |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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model = PeftModel.from_pretrained(model, MODEL_NAME) |
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generation_config = model.generation_config |
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generation_config.top_p = 0.7 |
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generation_config.num_return_sequences = 1 |
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generation_config.max_new_tokens = 32 |
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generation_config.use_cache = False |
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generation_config.pad_token_id = tokenizer.eos_token_id |
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generation_config.eos_token_id = tokenizer.eos_token_id |
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model.eval() |
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if torch.__version__ >= "2": |
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model = torch.compile(model) |
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``` |
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### Example of Usage |
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```python |
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prompt = "What is the capital city of Greece and with which countries does Greece border?" |
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prompt = generate_prompt(prompt) |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids |
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input_ids = input_ids.to(model.device) |
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with torch.no_grad(): |
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outputs = model.generate( |
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input_ids=input_ids, |
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generation_config=generation_config, |
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return_dict_in_generate=True, |
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output_scores=True, |
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) |
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response = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True) |
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print(response) |
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>>> The capital city of Greece is Athens and it borders Albania, Bulgaria, Macedonia, and Turkey. |
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``` |
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2. You can also directly call the model from HuggingFace using the following code snippet: |
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```python |
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import torch |
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from peft import PeftConfig, PeftModel |
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from transformers import GenerationConfig, AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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MODEL_NAME = "Sandiago21/falcon-7b-prompt-answering" |
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BASE_MODEL = "tiiuae/falcon-7b" |
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compute_dtype = getattr(torch, "float16") |
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bnb_config = BitsAndBytesConfig( |
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load_in_4bit=True, |
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bnb_4bit_quant_type="nf4", |
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bnb_4bit_compute_dtype=compute_dtype, |
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bnb_4bit_use_double_quant=True, |
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) |
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model = AutoModelForCausalLM.from_pretrained( |
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BASE_MODEL, |
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quantization_config=bnb_config, |
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device_map="auto", |
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trust_remote_code=True, |
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) |
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tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) |
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model = PeftModel.from_pretrained(model, MODEL_NAME) |
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generation_config = model.generation_config |
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generation_config.top_p = 0.7 |
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generation_config.num_return_sequences = 1 |
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generation_config.max_new_tokens = 32 |
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generation_config.use_cache = False |
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generation_config.pad_token_id = tokenizer.eos_token_id |
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generation_config.eos_token_id = tokenizer.eos_token_id |
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model.eval() |
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if torch.__version__ >= "2": |
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model = torch.compile(model) |
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``` |
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### Example of Usage |
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```python |
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prompt = "What is the capital city of Greece and with which countries does Greece border?" |
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prompt = generate_prompt(prompt) |
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids |
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input_ids = input_ids.to(model.device) |
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with torch.no_grad(): |
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outputs = model.generate( |
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input_ids=input_ids, |
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generation_config=generation_config, |
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return_dict_in_generate=True, |
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output_scores=True, |
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) |
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response = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True) |
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print(response) |
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>>> The capital city of Greece is Athens and it borders Albania, Bulgaria, Macedonia, and Turkey. |
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``` |
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## Training Details |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 4 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 8 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 50 |
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- num_epochs: 2 |
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- mixed_precision_training: Native AMP |
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### Framework versions |
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- Transformers 4.28.1 |
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- Pytorch 2.0.0+cu117 |
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- Datasets 2.12.0 |
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- Tokenizers 0.12.1 |
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### Training Data |
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The tiiuae/falcon-7b was finetuned on conversations and question answering data |
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### Training Procedure |
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The tiiuae/falcon-7b model was further trained and finetuned on question answering and prompts data for 1 epoch (approximately 10 hours of training on a single GPU) |
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## Model Architecture and Objective |
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The model is based on tiiuae/falcon-7b model and finetuned adapters on top of the main model on conversations and question answering data. |
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