|
--- |
|
license: apache-2.0 |
|
datasets: |
|
- togethercomputer/RedPajama-Data-V2 |
|
- stingning/ultrachat |
|
language: |
|
- fr |
|
- en |
|
metrics: |
|
- accuracy |
|
- perplexity |
|
--- |
|
|
|
# Mambaoutai 1.6B |
|
|
|
Mambaoutai is the result of all the experiments and training runs described in the [following blog post](https://www.lighton.ai/fr/blog/blog-4/passing-the-torch-training-a-mamba-model-for-smooth-handover-54), where all details about the model series is shared. Mambaoutai is series of small mamba checkpoints released for the community to explore, trained on French, English and code. We run two different decay phases with the WSD-scheduler, and release model checkpoints pretrained both with and without instruction data. |
|
|
|
## Usage |
|
|
|
You need to install `transformers` from `main` until `transformers=4.39.0` is released. |
|
|
|
```bash |
|
pip install git+https://github.com/huggingface/transformers@main |
|
``` |
|
|
|
We also recommend you to install both `causal-conv1d` and `mamba-ssm` using: |
|
|
|
```bash |
|
pip install causal-conv1d>=1.2.0 |
|
pip install mamba-ssm>=1.2.0 |
|
``` |
|
|
|
If any of these two is not installed, the "eager" implementation will be used(not recommended). Otherwise the more optimised `CUDA` kernels will be used. |
|
|
|
### Generation |
|
|
|
Use this snippet of code to generate text from the model: |
|
|
|
```python |
|
from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer |
|
import torch |
|
|
|
if model_has_instruct_data: |
|
# use chat tokens |
|
prompt = ”<start_user>Tell me something about Paris.<end_message><start_assistant>” |
|
else: |
|
# prompt the non-instructed tuned model gently |
|
prompt = ”This is a text about Paris. Paris is” |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("lightonai/mambaoutai") |
|
model = MambaForCausalLM.from_pretrained("lightonai/mambaoutai") |
|
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"] |
|
|
|
out = model.generate(input_ids, max_new_tokens=10) |
|
print(tokenizer.batch_decode(out)) |
|
``` |
|
|
|
### Training checkpoints |
|
|
|
You can find some of the training checkpoints in the repo branch. On branch corresponding to the model at some point in time during training. |
|
|
|
You can do inference with these training checkpoints by adding the `revision` parameter to the `from_pretrained` method. |
|
For example, to load the model checkpoint after 30000 steps of pretraining, you can use the following code: |
|
|
|
```python |
|
from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer |
|
import torch |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("lightonai/mambaoutai", revision="pre-30000") |
|
model = MambaForCausalLM.from_pretrained("lightonai/mambaoutai", revision="pre-30000") |
|
input_ids = tokenizer("What is a mamba?", return_tensors="pt")["input_ids"] |
|
|
|
out = model.generate(input_ids, max_new_tokens=10) |
|
print(tokenizer.batch_decode(out)) |
|
``` |
|
|
|
### On-device Inference |
|
|
|
Since Mambaoutai is only 1.6B parameters, it can be run on a CPU with reasonable speed. |
|
|
|
Here is an example of how to run it on llama.cpp: |
|
|
|
```bash |
|
# Clone llama.cpp repository and compile it from source |
|
git clone https://github.com/ggerganov/llama.cpp\ |
|
cd llama.cpp |
|
make |
|
|
|
# Create a venv and install dependencies |
|
conda create -n mamba-cpp python=3.10 |
|
conda activate mamba-cpp |
|
pip install -r requirements/requirements-convert-hf-to-gguf.txt |
|
|
|
# Download the weights, tokenizer, config, tokenizer_config and special_tokens_map from this repo and |
|
# put them in a directory 'Mambaoutai/' |
|
mkdir Mambaoutai |
|
|
|
# Convert the weights to GGUF format |
|
python convert-hf-to-gguf.py Mambaoutai |
|
|
|
# Run inference with a prompt |
|
./main -m Mambaoutai/ggml-model-f16.gguf -p "Building a website can be done in 10 simple steps:\nStep 1:" -n 400 -e -ngl 1 |
|
``` |
|
|
|
### Training Hardware |
|
|
|
The model checkpoints with no instruction data have been fully trained on an NVIDIA DGX H100 provided by OVH Cloud, whereas the decay phases with instruction data have been carried out on an HPE Cray with 8xH100 on Orange Cloud Avenue. |
|
The ablation experiments were conducted on 16 nodes(4xA100-40GB) on MeluXina. |
|
|
|
### Model hyperparameters |
|
|
|
More details about the model hyperparameters are given in the table below : |
|
|
|
| Parameter | Value | |
|
|-----------------------|----------| |
|
| d_model | 2688 | |
|
| n_layer | 28 | |
|
| vocab_size | 65024 | |
|
| context_len | 4096 | |
|
| rms_norm | true | |
|
| residual_in_fp32 | true | |
|
| fused_add_norm | true | |
|
| conv_kernel | 4 | |
|
| d_inner | 5376 | |
|
| state_size | 16 | |
|
| dtype | bfloat16 | |
|
| tie_word_embeddings | false | |
|
| non embeddings params | 1.27B | |
