---
license: apache-2.0
---
# Model Card for Zamba2-2.7B-instruct
Zamba2-2.7B-instruct is obtained from Zamba2-2.7B by fine-tuning on instruction-following and chat datasets.
Zamba2-2.7B is a hybrid model composed of state-space and transformer blocks. It broadly follows the [Zamba architecture](https://arxiv.org/abs/2405.16712) which consists of a Mamba backbone alternating with shared transformer blocks (see diagram in [Model Details](#model-details)). Zamba2-2.7B possesses three major improvements over Zamba1:
1.) Mamba1 blocks have been replaced with Mamba2 blocks.
2.) Instead of a single shared attention block, we utilize two shared attention blocks which are interleaved in an ABAB pattern throughout the network.
3.) We apply a LoRA projector to each shared MLP block, which allows the network to specialize the MLPs at each invocation of the shared layer across depth. LoRA enables us to add depth-specialization for only a minimal increase in total parameter count.
Zamba2-2.7B uses the Mistral v0.1 tokenizer and was pre-trained on 3T tokens of text and code data sourced from open web-datasets, including [Zyda](https://arxiv.org/abs/2406.01981). Subsequently, in a second phase, Zamba2-2.7B was annealed on a mixture of 100B high-quality tokens.
Note: this is a temporary HuggingFace implementation of Zamba2-2.7B. It may not yet be fully compatible with all frameworks and tools intended to interface with HuggingFace models.
A standalone Pytorch implementation of Zamba2-2.7B may be found [here](https://github.com/Zyphra/Zamba2).
## Quick start
### Prerequisites
To download Zamba2-2.7B-instruct, clone Zyphra's fork of transformers:
1. `git clone https://github.com/Zyphra/transformers_zamba2.git`
2. `cd transformers_zamba2`
3. Install the repository: `pip install -e .`
4. `pip install accelerate`
You can run the model without using the optimized Mamba kernels, but it is **not** recommended as it will result in significantly higher latency and memory usage.
To run on CPU, please specify `use_mamba_kernels=False` when loading the model using ``AutoModelForCausalLM.from_pretrained``.
### Inference
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Instantiate model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-2.7B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-2.7B-instruct", device_map="cuda", torch_dtype=torch.bfloat16)
# Format the input as a chat template
user_turn_1 = "In one season a flower blooms three times. In one year, there is one blooming season. How many times do two flowers bloom in two years? Please include your logic."
assistant_turn_1 = "In one season, a flower blooms three times. In one year, there is one blooming season. Therefore, in two years, there are two blooming seasons. Since each flower blooms three times in one season, in two blooming seasons, each flower will bloom six times. Since there are two flowers, the total number of times they will bloom in two years is 12."
user_turn_2 = "How many times do the two flowers blossom in three years?"
sample = [{'role': 'user', 'content': user_turn_1}, {'role': 'assistant', 'content': assistant_turn_1}, {'role': 'user', 'content': user_turn_2}]
chat_sample = tokenizer.apply_chat_template(sample, tokenize=False)
# Tokenize input and generate output
input_ids = tokenizer(chat_sample, return_tensors='pt', add_special_tokens=False).to("cuda")
outputs = model.generate(**input_ids, max_new_tokens=150, return_dict_in_generate=False, output_scores=False, use_cache=True, num_beams=1, do_sample=False)
print((tokenizer.decode(outputs[0])))
```
## Model Details
Zamba2-2.7B utilizes and extends our original Zamba hybrid SSM-attention architecture. The core Zamba architecture consists of a backbone of Mamba layers interleaved with one or more shared attention layers (one shared attention in Zamba1, two in Zamba2). This attention has shared weights to minimize the parameter cost of the model. We find that concatenating the original model embeddings to the input to this attention block improves performance, likely due to better maintenance of information across depth. The Zamba2 architecture also applies LoRA projection matrices to the shared MLP to gain some additional expressivity in each block and allow each shared block to specialize slightly to its own unique position while keeping the additional parameter overhead small.
## Performance
Zamba2-2.7B achieves leading and state-of-the-art performance among models of <3B parameters and is competitive with some models of significantly greater size. Moreover, due to its unique hybrid SSM architecture, Zamba2-2.7B achieves extremely low inference latency and rapid generation with a significantly smaller memory footprint than comparable transformer based models.
Zamba2-2.7B's high performance and small inference compute and memory footprint renders it an ideal generalist model for on-device applications.
Time to First Token (TTFT) | Output Generation
:-------------------------:|:-------------------------:
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