from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b")

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

python

# pip install accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b", 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

python

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b", 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]))

Running the model on a GPU using different precisions

FP16

Click to expand

python

# pip install accelerate
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b", device_map="auto", torch_dtype=torch.float16)

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]))

4-bit

Click to expand

python

# pip install bitsandbytes accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b")
model = AutoModelForCausalLM.from_pretrained("tiiuae/falcon-mamba-7b", device_map="auto", quantization_config=BitsAndBytesConfig(load_in_4bit=True))

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

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 from 2,048 to 8,192. Moreover, inspired by the concept of Curriculum Learning, we carefully selected data mixtures throughout the training stages, considering both data diversity and complexity. 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 math data extracted from public sources. In particular, we used samples coming from Fineweb-edu during our last training stage.

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 \(b_{\mathrm{min}}=128\) to \(b_{\mathrm{max}}=2048\) during first 50 GT of training. In the stable phase we used maximal learning rate \(\eta_{\mathrm{max}}=6.4 \times 10^{-4}\), and decayed it to the minimal value \(\eta_{\mathrm{min}}=\frac{\eta_{\mathrm{max}}}{256}\) with exponential schedule over 500 GT. Also, we applied BatchScaling during the rampup — rescaling learning rate \(\eta\) so that the Adam noise temperature \(T_{\mathrm{noise}}\equiv\frac{\eta}{\sqrt{b}}\) is kept constant.

Speeds, Sizes, Times

The model training took roughly two months.

Evaluation

Benchmarks

We evaluate our model on all benchmarks of the new leaderboard's version using the lm-evaluation-harness package, and then normalize the evaluation results with HuggingFace score normalization.

`model name`	`IFEval`	`BBH`	`MATH LvL5`	`GPQA`	`MUSR`	`MMLU-PRO`	`Average`
Pure SSM models
`FalconMamba-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
`recurrentgemma-9b`	30.76	14.80	4.83	4.70	6.60	17.88	13.20
`Zyphra/Zamba-7B-v1`^*	24.06	21.12	3.32	3.03	7.74	16.02	12.55
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
`Meta-Llama-3.1-8B`	12.70	25.29	4.61	6.15	8.98	24.95	13.78
`Mistral-7B-v0.1`	23.86	22.02	2.49	5.59	10.68	22.36	14.50
`Mistral-Nemo-Base-2407 (12B)`	16.83	29.37	4.98	5.82	6.52	27.46	15.08
`gemma-7B`	26.59	21.12	6.42	4.92	10.98	21.64	15.28
RWKV models
`RWKV-v6-Finch-7B`^*	27.65	9.04	1.11	2.81	2.25	5.85	8.12
`RWKV-v6-Finch-14B`^*	29.81	12.89	1.13	5.01	3.16	11.3	10.55

Also, we evaluate our model on the benchmarks of the first leaderboard using lighteval.

`model name`	`ARC`	`HellaSwag`	`MMLU`	`Winogrande`	`TruthfulQA`	`GSM8K`	`Average`
Pure SSM models
`FalconMamba-7B`^*	62.03	80.82	62.11	73.64	53.42	52.54	64.09
`TRI-ML/mamba-7b-rw`^*	51.25	80.85	33.41	71.11	32.08	4.70	45.52
Hybrid SSM-attention models
`recurrentgemma-9b`^**	52.00	80.40	60.50	73.60	38.60	42.60	57.95
`Zyphra/Zamba-7B-v1`^*	56.14	82.23	58.11	79.87	52.88	30.78	60.00
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
`Meta-Llama-3.1-8B`	58.53	82.13	66.43	74.35	44.29	47.92	62.28
`Mistral-7B-v0.1`	59.98	83.31	64.16	78.37	42.15	37.83	60.97
`Mistral-Nemo-Base-2407 (12B)`^*	57.94	82.82	64.43	73.72	49.14	55.27	63.89
`gemma-7B`	61.09	82.20	64.56	79.01	44.79	50.87	63.75
RWKV models
`RWKV-v6-Finch-7B`^*	43.86	75.19	41.69	68.27	42.19	19.64	48.47
`RWKV-v6-Finch-14B`^*	47.44	78.86	52.33	71.27	45.45	38.06	55.57

Mostly, we took evaluation results from both leaderboards. For the models marked by star we evaluated the tasks internally, while for the models marked by two stars the results were taken from paper or model card.

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 the last training stages

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 on an internal distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO, high-performance Triton kernels.

Citation

You can use the following bibtex citation:

@misc{zuo2024falconmambacompetitiveattentionfree,
      title={Falcon Mamba: The First Competitive Attention-free 7B Language Model}, 
      author={Jingwei Zuo and Maksim Velikanov and Dhia Eddine Rhaiem and Ilyas Chahed and Younes Belkada and Guillaume Kunsch and Hakim Hacid},
      year={2024},
      eprint={2410.05355},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2410.05355}, 
}

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	15.04
IFEval (0-Shot)	33.36
BBH (3-Shot)	19.88
MATH Lvl 5 (4-Shot)	3.63
GPQA (0-shot)	8.05
MuSR (0-shot)	10.86
MMLU-PRO (5-shot)	14.47

tiiuae/falcon-mamba-7b

Table of Contents

TL;DR

Model Details

Model Description

Usage

Using the Pytorch model

Running the model on a CPU

Running the model on a GPU

Running the model on a GPU using torch.compile

Running the model on a GPU using different precisions

FP16

4-bit

Training Details

Training Data

Training Procedure

Training Hyperparameters

Speeds, Sizes, Times

Evaluation

Benchmarks

Throughput

Technical Specifications

Model Architecture and Objective

Compute Infrastructure

Hardware

Software

Citation

Open LLM Leaderboard Evaluation Results

tiiuae/
falcon-mamba-7b

Running the model on a GPU using `torch.compile`