license: apache-2.0
Model Card for Zamba2-1.2B
Zamba2-1.2B-instruct is obtained from Zamba2-1.2B by fine-tuning on instruction-following and chat datasets.
Zamba2-1.2B is a hybrid model composed of state-space (Mamba) and transformer blocks. It broadly follows the Zamba architecture which consists of a Mamba backbone alternating with shared transformer blocks (see diagram in Model Details). Zamba2-1.2B possesses three major improvements over Zamba1:
1.) Mamba1 blocks have been replaced with Mamba2 blocks.
2.) We apply a LoRA projector to each shared MLP and attention block, which allows the network to specialize at each invocation of the shared transformer layer across depth. LoRA enables us to add depth-specialization for only a minimal increase in total parameter count.
3.) We utilize rotary position embeddings in the shared attention layer.
Zamba2-1.2B differs from our 2.7B model in three ways:
1.) We have added rotary position embeddings
2.) A single shared transformer block (instead of two that we alternate between)
3.) Added LoRA projectors to attention blocks (instead of just a LoRA on the MLP block)
We found that while hybrid SSM-transformer models are perfectly capable of performing well without position embeddings, adding rotary embeddings to the shared attention block slightly improved performance. Secondly, we utilize a single attention block (instead of alternating between two independent transformer blocks) because this enables a higher flop count for the model at a given parameter budget and at smaller scales this becomes more important than the slightly faster latency.
Zamba2-1.2B 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. Subsequently, in a second phase, Zamba2-1.2B was annealed on a mixture of 100B high-quality tokens.
Note: this is a temporary HuggingFace implementation of Zamba2-1.2B. It may not yet be fully compatible with all frameworks and tools intended to interface with HuggingFace models.
A standalone Pytorch implementation of Zamba2-1.2B may be found here.
Quick start
Prerequisites
To download Zamba2-1.2B, clone Zyphra's fork of transformers:
git clone https://github.com/Zyphra/transformers_zamba2.gitcd transformers_zamba2- Install the repository:
pip install -e . 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
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
# Instantiate model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba2-1.2B-instruct")
model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba2-1.2B-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
Zamba2-1.2B 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. 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 transformer blocks 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
Zamba2-1.2B achieves leading and state-of-the-art performance among models of <2B parameters and is competitive with some models of significantly greater size. Moreover, due to its unique hybrid SSM architecture, Zamba2-1.2B achieves extremely low inference latency and rapid generation with a significantly smaller memory footprint than comparable transformer based models.
Zamba2-1.2B'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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And memory overhead
