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