license: apache-2.0
Model Card for Zamba 7B
Zamba-7B-v1 is a hybrid model between Mamba, a state-space model, and transformers. It uses a mamba backbone with a shared transformer layer every 6 blocks. Zamba was trained using next-token prediction. It uses the Mistral v0.1 tokenizer. 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 sourced from open web-datasets. Subsequently in a second phase, Zamba was annealed on a mixture of 50B high-quality tokens.
Note: the current Huggingface implementation of Zamba performs slower than our internal implementation. We are working to fix this with the Huggingface team.
Quick start
Presequities
To download Zamba, clone Zyphra's fork of transformers:
git clone https://github.com/Zyphra/transformers_zambacd transformers_zamba- Install the repository:
pip install -e .
In order to run optimized Mamba implementations on a CUDA device, you need to install mamba-ssm and causal-conv1d:
pip install mamba-ssm causal-conv1d>=1.2.0
You can run the model without using the optimized Mamba kernels, but it is not recommended as it will result in significantly higher latency.
To run on CPU, please specify use_mamba_kernels=False when loading the model using AutoModelForCausalLM.from_pretrained.
Inference
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba-7B-v1")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba-7B-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]))
Notice
Zamba is a pretrained base model and therefore does not have any moderation mechanism.