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README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# mambaoutai
+
+# Usage
+
+You need to install `transformers` from `main` until `transformers=4.39.0` is released.
+
+```bash
+pip install git+https://github.com/huggingface/transformers@main
+```
+
+We also recommend you to install both `causal_conv_1d` and `mamba-ssm` using: 
+
+```bash
+pip install causal-conv1d>=1.2.0
+pip install mamba-ssm
+```
+
+If any of these two is not installed, the "eager" implementation will be used. Otherwise the more optimised `cuda` kernels will be used.
+
+## Generation
+
+Use this snippet of code to generate text from the model:
+
+```python
+from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("lightonai/mambaoutai")
+model = MambaForCausalLM.from_pretrained("lightonai/mambaoutai")
+input_ids = tokenizer("What is a mamba?", return_tensors="pt")["input_ids"]
+
+out = model.generate(input_ids, max_new_tokens=10)
+print(tokenizer.batch_decode(out))
+```
+
+## Training checkpoints
+
+You can find some of the training checkpoints in the repo branch. On branch corresponding to the model at some point in time during training.
+
+You can do inference with these training checkpoints by adding the `revision` parameter to the `from_pretrained` method. For example, to load the model checkpoint after 30000 steps of pretraining, you can use the following code:
+
+```python
+from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("lightonai/mambaoutai", revision="pre-30000")
+model = MambaForCausalLM.from_pretrained("lightonai/mambaoutai", revision="pre-30000")
+input_ids = tokenizer("What is a mamba?", return_tensors="pt")["input_ids"]
+
+out = model.generate(input_ids, max_new_tokens=10)
+print(tokenizer.batch_decode(out))
+```