-
Notifications
You must be signed in to change notification settings - Fork 27.3k
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
Showing
3 changed files
with
766 additions
and
0 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Original file line number | Diff line number | Diff line change |
---|---|---|
@@ -0,0 +1,56 @@ | ||
# Zamba | ||
|
||
Zamba is a large language model (LLM) trained by Zyphra, and made available under an Apache 2.0 license. Please see the [Zyphra Hugging Face](https://huggingface.co/collections/zyphra/) repository for model weights. | ||
|
||
|
||
## Model details | ||
|
||
Zamba-7B-v1 is a hybrid between state-space models (Specifically [Mamba](https://github.com/state-spaces/mamba)) and transformer, and was trained using next-token prediction. Zamba uses a shared transformer layer after every 6 mamba blocks. It uses the [Mistral v0.1 tokenizer](https://huggingface.co/mistralai/Mistral-7B-v0.1). We came to this architecture after a series of ablations at small scales. Zamba-7B-v1 was pre-trained on 1T tokens of text and code data. | ||
|
||
<img src="zamba-arch.png" width=40% height=40% /> | ||
|
||
|
||
## Quick start | ||
|
||
### Presequities | ||
|
||
Jamba requires you use `transformers` version 4.39.0 or higher: | ||
```bash | ||
pip install transformers>=4.39.0 | ||
``` | ||
|
||
In order to run optimized Mamba implementations, you first need to install `mamba-ssm` and `causal-conv1d`: | ||
```bash | ||
pip install mamba-ssm causal-conv1d>=1.2.0 | ||
``` | ||
You also have to have the model on a CUDA device. | ||
|
||
You can run the model not using the optimized Mamba kernels, but it is **not** recommended as it will result in significantly lower latencies. In order to do that, you'll need to specify `use_mamba_kernels=False` when loading the model. | ||
|
||
## Inference | ||
|
||
```python | ||
from transformers import AutoTokenizer, AutoModelForCausalLM | ||
import torch | ||
|
||
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba-v1") | ||
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba-v1", device_map="auto", torch_dtype=torch.bfloat16) | ||
|
||
input_text = "A funny prompt would be " | ||
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") | ||
|
||
outputs = model.generate(**input_ids, max_new_tokens=100) | ||
print(tokenizer.decode(outputs[0])) | ||
``` | ||
|
||
## Model card | ||
|
||
The model cards can be found at: | ||
* [Zamba-7B](MODEL_CARD_ZAMBA-7B-v1.md) | ||
|
||
## Issues | ||
For issues with model output, or community discussion, please use the Hugging Face community [forum](https://huggingface.co/zyphra/zamba-7b) | ||
|
||
## License | ||
|
||
The model weights are open-sourced via an Apache 2.0 license. |
Empty file.
Oops, something went wrong.