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Model: state-spaces/mamba-2.8b-hf
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags: []
+---
+
+# Mamba
+
+<!-- Provide a quick summary of what the model is/does. -->
+This repository contains the `transfromers` compatible `mamba-2.8b`. The checkpoints are untouched, but the full `config.json` and tokenizer are pushed to this repo. 
+
+# 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
+You can use the classic `generate` API:
+```python
+>>> from transformers import MambaConfig, MambaForCausalLM, AutoTokenizer
+>>> import torch
+
+>>> tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-2.8b-hf")
+>>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-2.8b-hf")
+>>> input_ids = tokenizer("Hey how are you doing?", return_tensors="pt")["input_ids"]
+
+>>> out = model.generate(input_ids, max_new_tokens=10)
+>>> print(tokenizer.batch_decode(out))
+["Hey how are you doing?\n\nI'm doing great.\n\nI"]
+```
+
+## PEFT finetuning example
+In order to finetune using the `peft` library, we recommend keeping the model in float32!
+
+```python
+from datasets import load_dataset
+from trl import SFTTrainer
+from peft import LoraConfig
+from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments
+tokenizer = AutoTokenizer.from_pretrained("state-spaces/mamba-2.8b-hf")
+model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-2.8b-hf")
+dataset = load_dataset("Abirate/english_quotes", split="train")
+training_args = TrainingArguments(
+    output_dir="./results",
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    logging_dir='./logs',
+    logging_steps=10,
+    learning_rate=2e-3
+)
+lora_config =  LoraConfig(
+        r=8,
+        target_modules=["x_proj", "embeddings", "in_proj", "out_proj"],
+        task_type="CAUSAL_LM",
+        bias="none"
+)
+trainer = SFTTrainer(
+    model=model,
+    tokenizer=tokenizer,
+    args=training_args,
+    peft_config=lora_config,
+    train_dataset=dataset,
+    dataset_text_field="quote",
+)
+trainer.train()
+```