Create README.md

README.md

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+---
+license: other
+license_name: falcon-mamba-license
+license_link: https://falconllm.tii.ae/falcon-mamba-7b-terms-and-conditions.html
+base_model: tiiuae/falcon-mamba-7b-instruct
+language:
+- en
+datasets:
+- tiiuae/falcon-refinedweb
+---
+
+<img src="https://huggingface.co/datasets/tiiuae/documentation-images/resolve/main/falcon_mamba/thumbnail.png" alt="drawing" width="800"/>
+
+**GGUF quantization of [`falcon-mamba-7b-instruct`](https://huggingface.co/tiiuae/falcon-mamba-7b-instruct) in the format `BF16`**
+
+#  Table of Contents
+
+0. [TL;DR](#TL;DR)
+1. [Model Details](#model-details)
+2. [Usage](#usage)
+3. [Training Details](#training-details)
+4. [Evaluation](#evaluation)
+
+
+# TL;DR
+
+# Model Details
+
+## Model Description
+
+- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
+- **Model type:** Causal decoder-only
+- **Architecture:** Mamba
+- **Language(s) (NLP):** Mainly English
+- **License:** TII Falcon-Mamba License 2.0
+
+<br>
+
+# Usage
+
+Refer to the documentation of [`llama.cpp`](https://github.com/ggerganov/llama.cpp) to understand how to run this model locally on your machine.
+
+Download the GGUF weights with the command below:
+
+```bash
+huggingface-cli download tiiuae/falcon-mamba-7b-instruct-Q4_K_M-GGUF --include falcon-mamba-instruct-Q4_K_M.gguf --local-dir ./
+```
+Then you can run it with:
+```bash
+./llama-cli -m falcon-mamba-instruct-Q4_K_M.gguf -p "Hello how are you?"
+```
+
+# Training Details
+
+## Training Data
+
+Falcon-Mamba has been trained with ~ 5,500 GT mainly coming from [Refined-Web](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a large volume web-only dataset filtered and deduplicated.
+Similar to the others [Falcon](https://huggingface.co/tiiuae/falcon-11B) 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](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) during our last training stage.
+
+The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7B)/[11B](https://huggingface.co/tiiuae/falcon-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. 
+
+<br>
+
+# 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`<sup>*</sup>| 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`<sup>*</sup>      | 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`<sup>*</sup>          | 62.03 |   80.82   | 62.11 |   73.64    |  53.42  | 52.54 |  **64.09**       |
+| `TRI-ML/mamba-7b-rw`<sup>*</sup>         | 51.25  | 80.85     | 33.41 | 71.11      | 32.08      | 4.70  | 45.52            |
+|***Hybrid SSM-attention models***|     |           |       |            |            |       |                  |
+| `recurrentgemma-9b`<sup>**</sup>          |52.00   |   80.40   | 60.50 |   73.60    |   38.60    | 42.60 |  57.95           |
+| `Zyphra/Zamba-7B-v1`<sup>*</sup>         | 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.
+
+# 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](https://arxiv.org/abs/2312.00752)).
+
+| **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.
+
+<br>
+
+# Citation
+
+*Paper coming soon* 😊.
+