transformers/docs/source/en/model_doc/falcon.md

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# Falcon

[Falcon](https://huggingface.co/papers/2311.16867) is a family of large language models, available in 7B, 40B, and 180B parameters, as pretrained and instruction tuned variants. This model focuses on scaling pretraining over three categories, performance, data, and hardware. Falcon uses multigroup attention to significantly reduce inference memory requirements and rotary positional embeddings (RoPE). These models are pretrained on [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality and deduplicated 5T token dataset.

You can find all the original Falcon checkpoints under the [Falcon](https://huggingface.co/collections/tiiuae/falcon-64fb432660017eeec9837b5a) collection.

> [!TIP]
> Click on the Falcon models in the right sidebar for more examples of how to apply Falcon to different language tasks.

The example below demonstrates how to generate text with [`Pipeline`], [`AutoModel`], and from the command line.

<hfoptions id="usage">
<hfoption id="Pipeline">

```py
import torch
from transformers import pipeline

pipeline = pipeline(
    task="text-generation", 
    model="tiiuae/falcon-7b-instruct",
    torch_dtype=torch.bfloat16,
    device=0
)
pipeline(
    "Write a short poem about coding",
    max_length=100,
    do_sample=True,
    temperature=0.7
)
```

</hfoption>
<hfoption id="AutoModel">

```py
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
model = AutoModelForCausalLM.from_pretrained(
    "tiiuae/falcon-7b-instruct",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    attn_implementation="sdpa",
)

input_ids = tokenizer("Write a short poem about coding", return_tensors="pt").to("cuda")

output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```

</hfoption>
<hfoption id="transformers-cli">

```bash
# pip install -U flash-attn --no-build-isolation
transformers-cli chat --model_name_or_path tiiuae/falcon-7b-instruct --torch_dtype auto --attn_implementation flash_attention_2 --device 0
```

</hfoption>
</hfoptions>

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 only quantize the weights to 4-bits.

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, 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-7b")
model = AutoModelForCausalLM.from_pretrained(
    "tiiuae/falcon-7b",
    torch_dtype=torch.bfloat16,
    device_map="auto",
    quantization_config=quantization_config,
)

inputs = tokenizer("In quantum physics, entanglement means", return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Notes

- If you're upgrading from an older custom code checkpoint, remember to convert it to the official Transformers format for better stability and performance using the conversion script located in the [Falcon model directory](https://github.com/huggingface/transformers/tree/main/src/transformers/models/falcon).

   ```bash
   python convert_custom_code_checkpoint.py --checkpoint_dir my_model
   ```

## FalconConfig

[[autodoc]] FalconConfig
    - all

## FalconModel

[[autodoc]] FalconModel
    - forward

## FalconForCausalLM

[[autodoc]] FalconForCausalLM
    - forward

## FalconForSequenceClassification

[[autodoc]] FalconForSequenceClassification
    - forward

## FalconForTokenClassification

[[autodoc]] FalconForTokenClassification
    - forward

## FalconForQuestionAnswering

[[autodoc]] FalconForQuestionAnswering
    - forward