Make sure to install bitsandbytes
and have a GPU compatible with bitsandbytes
to run this model
Table of Contents
TL;DR
Model Details
Model Description
- Developed by: https://www.tii.ae
- Model type: Causal decoder-only
- Architecture: Mamba
- Language(s) (NLP): Mainly English
- License: TII Falcon-Mamba License 2.0
Model Source
- Paper: coming soon.
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
This checkpoint will only run on a GPU device with bitsandbytes
installed. See below for more details on how to load it
Click to expand
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b-4bit")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b-4bit")
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]))
You can also dequantize the model with model.dequantize()
method:
Click to expand
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b-4bit")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b-4bit")
model = model.dequantize()
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]))
Training Details
Training Data
Falcon-Mamba has been trained with ~ 5,500 GT mainly coming from Refined-Web, a large volume web-only dataset filtered and deduplicated. Similar to the others Falcon suite models, Falcon-Mamba has been trained leveraging a multi-stage training strategy to increase the context-length training from 2,048 up to 8,192. Note that at inference the context-length is not relevant as the Mamba architecture has no limit on long range dependency. At the last training stage, small portion of high-quality curated data was used to further enhance performance.
Overall, the data sources included RefinedWeb-English, high quality technical data, code data and conversational data extracted from public sources. In particular, we used samples coming from Fineweb-edu.
The data was tokenized with the Falcon-7B/11B tokenizer.
Training Procedure
Falcon-Mamba-7B was trained on 256 H100 80GB GPUs for the majority of the training, using a 3D parallelism strategy (TP=1, PP=1, DP=256) combined with ZeRO.
Training Hyperparameters
Hyperparameter | Value | Comment |
---|---|---|
Precision | bfloat16 |
|
Optimizer | AdamW | |
Max learning rate | 6.4e-4 | Following a WSD (warmup-stable-decay) learning rate schedule |
Weight decay | 1e-1 | |
Batch size | 2048 |
The model was trained AdamW optimizer, WSD (warmup-stable-decay) learning rate schedule, and a batch size rampup from to during first 50 GT of training. In the stable phase we used maximal learning rate , and decayed it to the minimal value with exponential schedule over 500 GT. Also, we applied BatchScaling during the rampup — rescaling learning rate so that the Adam noise temperature is kept constant.
Speeds, Sizes, Times
The model training took roughly two months.
Evaluation
Benchmarks
We evaluate our model on all benchmarks of the leaderboard's version 2 using the lm-evaluation-harness
package, and we evaluate it on the benchmarks of version 1 using lighteval
.
model name |
IFEval |
BBH |
MATH LvL5 |
GPQA |
MUSR |
MMLU-PRO |
Average |
---|---|---|---|---|---|---|---|
Pure SSM models | |||||||
Falcon-Mamba-7B |
33.36 | 19.88 | 3.63 | 8.05 | 10.86 | 14.47 | 15.04 |
TRI-ML/mamba-7b-rw |
22.46 | 6.71 | 0.45 | 1.12 | 5.51 | 1.69 | 6.25 |
Hybrid SSM-attention models | |||||||
Zamba-7B-v1 |
24.06 | 21.12 | 3.32 | 3.03 | 7.74 | 16.02 | 12.55 |
recurrentgemma-9b |
30.76 | 14.80 | 4.83 | 4.70 | 6.60 | 17.88 | 13.20 |
Transformer models | |||||||
Falcon2-11B |
32.61 | 21.94 | 2.34 | 2.80 | 7.53 | 15.44 | 13.78 |
Meta-Llama-3-8B |
14.55 | 24.50 | 3.25 | 7.38 | 6.24 | 24.55 | 13.41 |
gemma-7B |
26.59 | 21.12 | 6.42 | 4.92 | 10.98 | 21.64 | 15.28 |
Mistral-7B-v0.1 |
23.86 | 22.02 | 2.49 | 5.59 | 10.68 | 22.36 | 14.50 |
Mistral-Nemo-Base |
16.83 | 29.37 | 4.98 | 5.82 | 6.52 | 27.46 | 15.08 |
model name |
ARC |
HellaSwag |
MMLU |
Winogrande |
TruthfulQA |
GSM8K |
Average |
---|---|---|---|---|---|---|---|
Pure SSM models | |||||||
Falcon-Mamba-7B |
62.03 | 80.82 | 62.11 | 73.64 | 53.42 | 52.54 | 64.09 |
TRI-ML/mamba-7b-rw |
46.48 | 80.24 | 57.72 | 76.40 | - | 4.70 | - |
Hybrid SSM-attention models | |||||||
recurrentgemma-9b |
52.00 | 80.40 | 60.50 | 73.60 | 38.60 | 42.60 | 57.95 |
Zyphra/Zamba-7B-v1 |
46.48 | 80.24 | 57.72 | 76.40 | - | 30.78 | - |
Transformer models | |||||||
Falcon2-11B |
59.73 | 82.91 | 58.37 | 78.30 | 52.56 | 53.83 | 64.28 |
Meta-Llama-3-8B |
60.24 | 82.23 | 66.70 | 78.45 | 42.93 | 45.19 | 62.62 |
gemma-7B |
61.09 | 82.20 | 64.56 | 79.01 | 44.79 | 50.87 | 63.75 |
Mistral-7B-v0.1 |
59.98 | 83.31 | 64.16 | 78.37 | 42.15 | 37.83 | 60.97 |
Throughput
This model can achieve comparable throughput and performance compared to other transformer based models that use optimized kernels such as Flash Attention 2. Make sure to install the optimized Mamba kernels with the following commands:
pip install "causal-conv1d>=1.4.0" mamba-ssm
Refer to our FalconMamba blogpost for more details about performance evaluation.
Technical Specifications
Model Architecture and Objective
Falcon-Mamba-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The model is based on the Mamba architecture (Gu et al., 2023).
Hyperparameter | Value | Comment |
---|---|---|
Layers | 64 | Number of layers |
d_model |
4096 | Hidden dimension |
d_state |
16 | The SSM state dimension |
Vocabulary | 65024 | Vocabulary Size |
Sequence length | 8192 | During stages 4 and LR Decay stage |
Compute Infrastructure
Hardware
Falcon-Mamba-7B was trained on AWS SageMaker, using on average 256 H100 80GB GPUs in 32 p5 instances.
Software
Falcon-Mamba-7B was trained an internal distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO, high-performance Triton kernels.
Citation
Paper coming soon 😊. In the meanwhile, you can use the following information to cite:
@article{falconmamba,
title={Falcon Mamba: The First Competitive Attention-free 7B Language Model},
author={Zuo, Jingwei and Velikanov, Maksim and Rhaiem, Dhia Eddine and Chahed, Ilyas and Belkada, Younes and Kunsch, Guillaume and Hacid, Hakim},
year={2024}
}
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