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Model: state-spaces/mamba-130m-hf
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2026-09-16 11:26:19 +08:00
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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-130m-hf")
>>> model = MambaForCausalLM.from_pretrained("state-spaces/mamba-130m-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 so glad you're here."]
```
## 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-130m-hf")
model = AutoModelForCausalLM.from_pretrained("state-spaces/mamba-130m-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()
```

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{
"architectures": [
"MambaForCausalLM"
],
"bos_token_id": 0,
"conv_kernel": 4,
"d_inner": 1536,
"d_model": 768,
"eos_token_id": 0,
"expand": 2,
"fused_add_norm": true,
"hidden_act": "silu",
"hidden_size": 768,
"initializer_range": 0.1,
"intermediate_size": 1536,
"layer_norm_epsilon": 1e-05,
"model_type": "mamba",
"n_layer": 24,
"num_hidden_layers": 24,
"pad_token_id": 0,
"pad_vocab_size_multiple": 8,
"rescale_prenorm_residual": false,
"residual_in_fp32": true,
"rms_norm": true,
"ssm_cfg": {},
"state_size": 16,
"time_step_floor": 0.0001,
"time_step_init_scheme": "random",
"time_step_max": 0.1,
"time_step_min": 0.001,
"time_step_rank": 48,
"time_step_scale": 1.0,
"torch_dtype": "float32",
"transformers_version": "4.39.0.dev0",
"use_bias": false,
"use_cache": true,
"use_conv_bias": true,
"vocab_size": 50280
}

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{
"_from_model_config": true,
"bos_token_id": 0,
"pad_token_id": 0,
"eos_token_id": 0,
"transformers_version": "4.39.0.dev0"
}

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