metadata
license: other
license_name: falcon-llm-license
license_link: https://falconllm.tii.ae/falcon-terms-and-conditions.html
language:
- en
base_model: tiiuae/Falcon3-10B-Instruct
pipeline_tag: text-generation
tags:
- gptqmodel
- modelcloud
- chat
- falcon3
- instruct
- int4
- gptq
- 4bit
- W4A16
This model has been quantized using GPTQModel.
- bits: 4
- dynamic: null
- group_size: 32
- desc_act: true
- static_groups: false
- sym: true
- lm_head: false
- true_sequential: true
- quant_method: "gptq"
- checkpoint_format: "gptq"
- meta：
- quantizer: gptqmodel:1.4.4
- uri: https://github.com/modelcloud/gptqmodel
- damp_percent: 0.1
- damp_auto_increment: 0.0025
Example:
from transformers import AutoTokenizer
from gptqmodel import GPTQModel
tokenizer = AutoTokenizer.from_pretrained("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
model = GPTQModel.load("ModelCloud/Falcon3-10B-Instruct-gptqmodel-4bit-vortex-v1")
messages = [
{"role": "user", "content": "How can I design a data structure in C++ to store the top 5 largest integer numbers?"},
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_ids=input_tensor.to(model.device), max_new_tokens=512)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)