# FalconMamba
[FalconMamba](https://huggingface.co/papers/2410.05355) is a 7B large language model, available as pretrained and instruction-tuned variants, based on the [Mamba](./mamba). This model implements a pure Mamba design that focuses on computational efficiency while maintaining strong performance. FalconMamba is significantly faster at inference and requires substantially less memory for long sequence generation. The models are pretrained on a diverse 5.8T token dataset including [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), technical content, code, and mathematical data.
You can find the official FalconMamba checkpoints in the [FalconMamba 7B](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a) collection.
> [!TIP]
> Click on the FalconMamba models in the right sidebar for more examples of how to apply FalconMamba to different language tasks.
The examples below demonstrate how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.
```py
import torch
from transformers import pipeline
pipeline = pipeline(
"text-generation",
model="tiiuae/falcon-mamba-7b-instruct",
torch_dtype=torch.bfloat16,
device=0
)
pipeline(
"Explain the difference between transformers and SSMs",
max_length=100,
do_sample=True,
temperature=0.7
)
```
```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b-instruct")
model = AutoModelForCausalLM.from_pretrained(
"tiiuae/falcon-mamba-7b-instruct",
torch_dtype=torch.bfloat16,
device_map="auto"
)
input_ids = tokenizer("Explain the difference between transformers and SSMs", return_tensors="pt").to("cuda")
output = model.generate(**input_ids, max_new_tokens=100, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
```bash
transformers chat tiiuae/falcon-mamba-7b-instruct --torch_dtype auto --device 0
```
Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the [Quantization](../quantization/overview) overview for more available quantization backends.
The example below uses [bitsandbytes](../quantization/bitsandbytes) to quantize the weights to 4-bits.
```python
import torch
from transformers import AutoTokenizer, FalconMambaForCausalLM, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_quant_type="nf4",
bnb_4bit_use_double_quant=True,
)
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b")
model = FalconMambaForCausalLM.from_pretrained(
"tiiuae/falcon-mamba-7b",
torch_dtype=torch.bfloat16,
device_map="auto",
quantization_config=quantization_config,
)
inputs = tokenizer("Explain the concept of state space models in simple terms", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## FalconMambaConfig
[[autodoc]] FalconMambaConfig
## FalconMambaModel
[[autodoc]] FalconMambaModel
- forward
## FalconMambaLMHeadModel
[[autodoc]] FalconMambaForCausalLM
- forward
