06f1fd69a6
4.9 KiB
This model was published in HF papers on 2023-11-28 and contributed to Hugging Face Transformers on 2023-07-11.
Falcon
Falcon 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, a high-quality and deduplicated 5T token dataset.
You can find all the original Falcon checkpoints under the Falcon 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.
from transformers import pipeline
pipeline = pipeline(
task="text-generation",
model="tiiuae/falcon-7b-instruct",
device=0
)
pipeline(
"Write a short poem about coding",
max_length=100,
do_sample=True,
temperature=0.7
)
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
model = AutoModelForCausalLM.from_pretrained(
"tiiuae/falcon-7b-instruct",
device_map="auto",
attn_implementation="sdpa",
)
input_ids = tokenizer("Write a short poem about coding", return_tensors="pt").to(model.device)
output = model.generate(**input_ids)
print(tokenizer.decode(output[0], skip_special_tokens=True))
# pip install -U flash-attn --no-build-isolation
transformers chat tiiuae/falcon-7b-instruct --dtype auto --attn_implementation flash_attention_2 --device 0
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 only quantize the weights to 4-bits.
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
quantization_config = BitsAndBytesConfig(
load_in_4bit=True,
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",
device_map="auto",
quantization_config=quantization_config,
)
inputs = tokenizer("In quantum physics, entanglement means", return_tensors="pt").to(model.device)
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.
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