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--- |
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language: |
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- multilingual |
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license: apache-2.0 |
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--- |
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# Model Card for Sindibad-7B |
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# Table of Contents |
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0. [TL;DR](#TL;DR) |
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1. [Model Details](#model-details) |
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2. [Usage](#usage) |
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3. [Training Details](#training-details) |
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4. [Evaluation](#evaluation) |
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# TL;DR |
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# Model Details |
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## Model Description |
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- **Developed by:** [https://www.tii.ae](https://www.tii.ae) |
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- **Model type:** Causal decoder-only |
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- **Architecture:** Mamba |
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- **Language(s) (NLP):** Mainly English |
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- **License:** TII Sindibad License 2.0 |
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### Model Source |
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- **Paper:** *coming soon*. |
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# Usage |
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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): |
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## Using the Pytorch model |
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### Running the model on a CPU |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b") |
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model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b") |
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input_text = "Question: How many hours in one day? Answer: " |
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids |
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outputs = model.generate(input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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</details> |
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### Running the model on a GPU |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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# pip install accelerate |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b") |
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model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b", device_map="auto") |
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input_text = "Question: How many hours in one day? Answer: " |
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") |
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outputs = model.generate(input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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</details> |
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### Running the model on a GPU using different precisions |
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#### FP16 |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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# pip install accelerate |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b") |
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model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b", device_map="auto", torch_dtype=torch.float16) |
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input_text = "Question: How many hours in one day? Answer: " |
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") |
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outputs = model.generate(input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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</details> |
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#### 4-bit |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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# pip install bitsandbytes accelerate |
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from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig |
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/sindibad-7b") |
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model = AutoModelForCausalLM.from_pretrained("tiiuae/sindibad-7b", device_map="auto", quantization_config=BitsAndBytesConfig(load_in_4bit=True)) |
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input_text = "Question: How many hours in one day? Answer: " |
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda") |
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outputs = model.generate(input_ids) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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</details> |
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# Training Details |
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## Training Data |
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Guillaume |
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## Training Procedure |
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Sindibad-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. |
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#### Training Hyperparameters |
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| **Hyperparameter** | **Value** | **Comment** | |
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| Precision | `bfloat16` | | |
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| Optimizer | AdamW | | |
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| Max learning rate | 6.4e-4 | Following a WSD (warmup-stable-decay) learning rate schedule | |
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| Weight decay | 1e-1 | | |
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| Z-loss | 1e-4 | | |
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| Batch size | 2048-4096 | | |
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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. |
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#### Speeds, Sizes, Times |
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The model training took roughly two months. |
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# Evaluation |
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## Benchmarks |
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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`. |
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| model_name | IFEval | BBH | MATH LvL5 | GPQA | MUSR | MMLU-PRO | **Average L2** | ARC | HellaSwag | MMLU | Winogrande | TruthfulQA | GSM8K | **Average L1** | |
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|------------------------------|--------|-------|-----------|-------|-------|----------|----------------|-------|-----------|-------|------------|------------|-------|----------------| |
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| `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 | |
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| `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** | |
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| `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 | |
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| `Zyphra/Zamba-7B-v1` | - | - | - | - | - | - | - | 46.48 | 80.24 | 57.72 | 76.4 | - | - | - | |
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| 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 | |
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## Throughput |
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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: |
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```bash |
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pip install "causal-conv1d>=1.4.0" mamba-ssm |
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``` |
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Refer to our technical report for more details about performance evaluation. |
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# Technical Specifications |
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## Model Architecture and Objective |
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Sindibad-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token). |
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The model is based on the Mamba architecture ([Gu et al., 2023](https://arxiv.org/abs/2312.00752)). |
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| **Hyperparameter** | **Value** | **Comment** | |
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|--------------------|-----------|----------------------------------------| |
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| Layers | 64 | | |
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| `d_model` | 4096 | | |
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| `d_state` | 16 | The SSM state dimension | |
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| Vocabulary | 65024 | | |
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| Sequence length | 8192 | During stages 4 and LR Decay stage | |
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## Compute Infrastructure |
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### Hardware |
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Sindibad-7B was trained on AWS SageMaker, using on average 256 H100 80GB GPUs in 32 p5 instances. |
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### Software |
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Sindibad-7B was trained an internal distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO, high-performance Triton kernels. |
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# Citation |
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*Paper coming soon* 😊. |
