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FalconMamba

docs/source/en/model_doc/falcon_mamba.md

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This model was released on 2024-10-07 and added to Hugging Face Transformers on 2024-08-12.

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FalconMamba

FalconMamba is a 7B large language model, available as pretrained and instruction-tuned variants, based on the 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, technical content, code, and mathematical data.

You can find the official FalconMamba checkpoints in the FalconMamba 7B 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.

<hfoptions id="usage"> <hfoption id="Pipeline">
python
from transformers import pipeline


pipeline = pipeline(
    "text-generation",
    model="tiiuae/falcon-mamba-7b-instruct",
    device=0
)
pipeline(
    "Explain the difference between transformers and SSMs",
    max_length=100,
    do_sample=True,
    temperature=0.7
)
</hfoption> <hfoption id="AutoModel">
python
from transformers import AutoModelForCausalLM, AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-mamba-7b-instruct")
model = AutoModelForCausalLM.from_pretrained(
    "tiiuae/falcon-mamba-7b-instruct",
    device_map="auto"
)

input_ids = tokenizer("Explain the difference between transformers and SSMs", return_tensors="pt").to(model.device)

output = model.generate(**input_ids, max_new_tokens=100, cache_implementation="static")
print(tokenizer.decode(output[0], skip_special_tokens=True))
</hfoption> <hfoption id="transformers CLI">
bash
transformers chat tiiuae/falcon-mamba-7b-instruct --dtype auto --device 0
</hfoption> </hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to quantize the weights to 4-bits.

python
from transformers import AutoTokenizer, BitsAndBytesConfig, FalconMambaForCausalLM


quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    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",
    device_map="auto",
    quantization_config=quantization_config,
)

inputs = tokenizer("Explain the concept of state space models in simple terms", return_tensors="pt").to(model.device)
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