Triangle104/falcon-mamba-7b-Q4_K_S-GGUF

This model was converted to GGUF format from tiiuae/falcon-mamba-7b using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model.

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Model details:

Table of Contents

TL;DR
Model Details
Usage
Training Details
Evaluation

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

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 Running the model on a CPU Click to expand

Running the model on a GPU Click to expand

Running the model on a GPU using torch.compile Click to expand

Running the model on a GPU using different precisions FP16 Click to expand

4-bit Click to expand

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 bmin=128b_{\mathrm{min}}=128bmin​=128 to bmax=2048b_{\mathrm{max}}=2048bmax​=2048 during first 50 GT of training. In the stable phase we used maximal learning rate ηmax=6.4×10−4\eta_{\mathrm{max}}=6.4 \times 10^{-4}ηmax​=6.4×10−4, and decayed it to the minimal value ηmin=ηmax256\eta_{\mathrm{min}}=\frac{\eta_{\mathrm{max}}}{256}ηmin​=256ηmax​​ with exponential schedule over 500 GT. Also, we applied BatchScaling during the rampup — rescaling learning rate η\etaη so that the Adam noise temperature Tnoise≡ηbT_{\mathrm{noise}}\equiv\frac{\eta}{\sqrt{b}}Tnoise​≡b

​η​ 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}, }

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Use with llama.cpp

Install llama.cpp through brew (works on Mac and Linux)

brew install llama.cpp

Invoke the llama.cpp server or the CLI.

CLI:

llama-cli --hf-repo Triangle104/falcon-mamba-7b-Q4_K_S-GGUF --hf-file falcon-mamba-7b-q4_k_s.gguf -p "The meaning to life and the universe is"

Server:

llama-server --hf-repo Triangle104/falcon-mamba-7b-Q4_K_S-GGUF --hf-file falcon-mamba-7b-q4_k_s.gguf -c 2048

Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.

git clone https://github.com/ggerganov/llama.cpp

Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run inference through the main binary.

./llama-cli --hf-repo Triangle104/falcon-mamba-7b-Q4_K_S-GGUF --hf-file falcon-mamba-7b-q4_k_s.gguf -p "The meaning to life and the universe is"

or

./llama-server --hf-repo Triangle104/falcon-mamba-7b-Q4_K_S-GGUF --hf-file falcon-mamba-7b-q4_k_s.gguf -c 2048