README.md

---
datasets:
- tiiuae/falcon-refinedweb
- HuggingFaceFW/fineweb-edu
language:
- en
license: other
license_name: falcon-mamba-7b-license
license_link: https://falconllm.tii.ae/falcon-mamba-7b-terms-and-conditions.html
base_model: tiiuae/falcon-mamba-7b-instruct
pipeline_tag: text-generation
inference: true
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/falcon-mamba-7b-instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`tiiuae/falcon-mamba-7b-instruct`](https://huggingface.co/tiiuae/falcon-mamba-7b-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/tiiuae/falcon-mamba-7b-instruct) for more details on the model.

---
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.

After pre-training, the model has been further fine-tuned on instruction data.
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

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
gemma-7B 	61.09 	82.20 	64.56 	79.01 	44.79 	50.87 	63.75

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}, 
}

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/falcon-mamba-7b-instruct-Q4_K_M-GGUF --hf-file falcon-mamba-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/falcon-mamba-7b-instruct-Q4_K_M-GGUF --hf-file falcon-mamba-7b-instruct-q4_k_m.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) 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-instruct-Q4_K_M-GGUF --hf-file falcon-mamba-7b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/falcon-mamba-7b-instruct-Q4_K_M-GGUF --hf-file falcon-mamba-7b-instruct-q4_k_m.gguf -c 2048
```