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license: apache-2.0 |
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# Model Card for Zamba |
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Zamba-7B-v1-phase1 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-phase-1 was pre-trained on 1T tokens of text and code data sourced from open web-datasets. Unlike Zamba-v1, this model represents the checkpoint after pure prertaining only on web-datasets. We envision its use primarily as a comparison tool to explore the effects of our annealing process. |
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## Quick start |
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### Presequities |
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Zamba requires you use `transformers` version 4.39.0 or higher: |
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```bash |
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pip install transformers>=4.39.0 |
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``` |
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In order to run optimized Mamba implementations on a CUDA device, you first need to install `mamba-ssm` and `causal-conv1d`: |
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```bash |
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pip install mamba-ssm causal-conv1d>=1.2.0 |
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``` |
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You can run the model not using the optimized Mamba kernels, but it is **not** recommended as it will result in significantly higher latency. |
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To run on CPU, please specify `use_mamba_kernels=False` when loading the model using ``AutoModelForCausalLM.from_pretrained``. |
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## Inference |
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```python |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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import torch |
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tokenizer = AutoTokenizer.from_pretrained("Zyphra/Zamba-7B-v1-phase1") |
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model = AutoModelForCausalLM.from_pretrained("Zyphra/Zamba-7B-v1-phase1", device_map="auto", torch_dtype=torch.bfloat16) |
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input_text = "A funny prompt would be " |
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input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") |
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outputs = model.generate(**input_ids, max_new_tokens=100) |
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print(tokenizer.decode(outputs[0])) |
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``` |
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## Notice |
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Zamba is a pretrained base model and therefore does not have any moderation mechanism. |
