README.md

---
datasets:
- tiiuae/falcon-refinedweb
- HuggingFaceFW/fineweb-edu
language:
- en
license: apache-2.0
---

# Model Card for Falcon-Mamba-7B



#  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

### 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

### Running the model on a CPU

<details>
<summary> Click to expand </summary>

```python
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]))
```

</details>

### Running the model on a GPU

<details>
<summary> Click to expand </summary>

```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]))
```

</details>

### Running the model on a GPU using different precisions

#### FP16

<details>
<summary> Click to expand </summary>

```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]))
```

</details>

#### 4-bit

<details>
<summary> Click to expand </summary>

```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]))
```

</details>



# Training Details

## Training Data

Falcon-Mamba has been trained with ~ 6,000 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 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](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu).

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. 

# 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 L2** | ARC   | HellaSwag | MMLU  | Winogrande | TruthfulQA | GSM8K | **Average L1** |
|------------------------------|--------|-------|-----------|-------|-------|----------|----------------|-------|-----------|-------|------------|------------|-------|----------------|
| `meta-llama/Meta-Llama-3-8B` | 14.55  | 24.50 | 3.25      | 7.38  | 6.24  | 24.55    | 13.41          | 60.24 | 82.23     | 66.70 | 78.45      | 42.93      | 45.19 | 62.62          |
| `tiiuae/falcon2-11B`         | 32.61  | 21.94 | 2.34      | 2.8   | 7.53  | 15.44    | 13.78          | 59.73 | 82.91     | 58.37 | 78.30      | 52.56      | 53.83 | **64.28**      |
| `mistralai/Mistral-7B-v0.1`  | 23.86  | 22.02 | 2.49      | 5.59  | 10.68 | 22.36    | 14.50          | 59.98 | 83.31     | 64.16 | 78.37      | 42.15      | 37.83 | 60.97          |
| `Zyphra/Zamba-7B-v1`         | -      | -     | -         | -     | -     | -        | -              | 46.48 | 80.24     | 57.72 | 76.4       | -          | -     | -              |
| Ours                         | 32.16  | 21.07 | 4.08      | 10.18 | 6.97  | 13.43    | **14.65**      | 61.69 | 80.63     | 61.05 | 74.03      | 53.60      | 51.86 | 63.81          |

## 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:

```bash
pip install "causal-conv1d>=1.4.0" mamba-ssm
```

Refer to our technical report 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](https://arxiv.org/abs/2312.00752)).

| **Hyperparameter** | **Value** | **Comment**                            |
|--------------------|-----------|----------------------------------------|
| Layers             | 64        |                                        |
| `d_model`          | 4096      |                                        |
| `d_state`         | 16       |   The SSM state dimension                                     |
| Vocabulary         | 65024     |                                        |
| 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* 😊.