metadata
language:
- en
- es
- pt
tags:
- falcon3
Table of Contents
TL;DR
Model Details
⚠️ This is a raw, pretrained model, which should be further finetuned for most usecases.
Model Description
- Developed by: https://www.tii.ae
- Model type: Causal decoder-only
- Architecture: Transformer-base
- Language(s) (NLP): Mainly English
- License: TII Falcon-LLM License 2.0
Usage
Find below some example scripts on how to use the model in transformers (Make sure to have the latest transformers, or the one built from source):
Using the Pytorch model with 🤗 transformers
Running the model on a CPU
Click to expand
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base")
input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
Running the model on a GPU
Click to expand
# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", device_map="auto")
input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
Running the model on a GPU using torch.compile
Click to expand
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", torch_dtype=torch.bfloat16).to(0)
model = torch.compile(model)
input_text = "Question: How many hours in one day? Answer: "
input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
Training Details
Training Data
Falcon3-7B is trained on 15 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data.
Training Procedure
Falcon3-7B is trained on 256 H100 nodes (world size 2048).
Training Hyperparameters
| Hyperparameter | Value | Comment |
|---|---|---|
| Precision | bfloat16 |
|
| Optimizer | AdamW | |
| Max learning rate | 6e-4 | Following a WSD (warmup-stable-decay) |
| learning rate scheduler | ||
| Weight decay | 1e-1 | |
| z-loss | 1e-4 | |
| Batch size | Variable | Batch size was gradually increased |
| during the training |
Evaluation
| Category | Benchmark | Llama3.1-8B | Qwen2-7B | Qwen2.5-7B | Falcon3-7B-Base |
|---|---|---|---|---|---|
| General | MMLU (5-shot) | 65.2 | 70.4 | 74.2 | 67.5 |
| MMLU-PRO (5-shot) | 32.7 | 42.1 | 43.5 | 39.2 | |
| IFEval | 12.0 | 30.6 | 33.9 | 34.3 | |
| Math | GSM8K (5-shot) | 49.4 | 77.9 | 82.9 | 76.2 |
| MATH(4-shot) | 4.1 | 17.5 | 15.5 | 18.0 | |
| Reasoning | Arc Challenge (25-shot) | 53.4 | 57.4 | 59.0 | 59.6 |
| GPQA (0-shot) | 31.0 | 31.9 | 33.0 | 35.5 | |
| MUSR (0-shot) | 38.0 | 44.1 | 44.2 | 47.3 | |
| BBH (3-shot) | 46.5 | 53.3 | 54.0 | 51.0 | |
| CommonSense Understanding | PIQA (0-shot) | 80.3 | 79.8 | 78.7 | 77.7 |
| SciQ (0-shot) | 96.3 | 95.9 | 96.6 | 95.3 | |
| Winogrande (0-shot) | 74.0 | 72.1 | 72.9 | 71.0 | |
| OpenbookQA (0-shot) | 33.4 | 35.2 | 33.6 | 31.4 |