Falcon-40B-Instruct 4bit GPTQ：在保持模型性能的同时减少计算资源的消耗

Falcon-40B-Instruct 4bit GPTQ是一个实验性的量化模型，通过AutoGPTQ技术实现了高级的4比特量化，目的是在保持模型性能的同时减少计算资源的消耗。该模型是在Falcon-40B-Instruct的基础上进行优化，特别适用于需要高VRAM容量的复杂AI任务。

github_ai_big_model_falcon_1

核心特性与优势

高效的模型量化

通过AutoGPTQ的4比特量化，该模型在GPU上的推理效率得到显著提升，虽然目前仍在实验阶段且运行速度较慢，但展示了在AI模型优化方面的巨大潜力。

跨平台支持与应用

该模型支持在具有高VRAM容量的GPU上运行，如40GB或48GB显卡，使其能够处理更大规模的数据集和复杂的计算任务。

开源协作与社区支持

Falcon-40B-Instruct 4bit GPTQ模型的代码和权重已开源，为全球研究人员和开发者提供了一个共享的平台，通过社区的力量持续优化和完善模型。

使用指南和实际应用

配置和安装

用户需要安装AutoGPTQ库，并确保CUDA工具集与之兼容。

    
    pip install auto-gptq
pip install einops

然后运行示例代码：

    python
    from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import argparse

model_name_or_path = "TheBloke/falcon-40b-instruct-GPTQ"
# You could also download the model locally, and access it there
# model_name_or_path = "/path/to/TheBloke_falcon-40b-instruct-GPTQ"

model_basename = "model"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=True,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

prompt = "Tell me about AI"
prompt_template=f'''A helpful assistant who helps the user with any questions asked.
User: {prompt}
Assistant:''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline
# Note that if you use pipeline, you will see a spurious error message saying the model type is not supported
# This can be ignored!  Or you can hide it with the following logging line:
# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])

from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
import argparse

model_name_or_path = "TheBloke/falcon-40b-instruct-GPTQ"
# You could also download the model locally, and access it there
# model_name_or_path = "/path/to/TheBloke_falcon-40b-instruct-GPTQ"

model_basename = "model"

use_triton = False

tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)

model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
        model_basename=model_basename,
        use_safetensors=True,
        trust_remote_code=True,
        device="cuda:0",
        use_triton=use_triton,
        quantize_config=None)

prompt = "Tell me about AI"
prompt_template=f'''A helpful assistant who helps the user with any questions asked.
User: {prompt}
Assistant:''

print("\n\n*** Generate:")

input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))

# Inference can also be done using transformers' pipeline
# Note that if you use pipeline, you will see a spurious error message saying the model type is not supported
# This can be ignored!  Or you can hide it with the following logging line:
# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)

print("*** Pipeline:")
pipe = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.95,
    repetition_penalty=1.15
)

print(pipe(prompt_template)[0]['generated_text'])

实验性注意事项

由于是实验性模型，用户在使用时应预期会有一定的运行缓慢和资源需求高的情况。此外，开发者应当注意使用“trust_remote_code”选项，允许远程代码的执行，这是因为Falcon尚未被Hugging Face transformers官方支持。

虽然目前模型的运行效率有待提高，但其在AI量化领域的探索意义重大，未来有望实现更广泛的应用和更佳的性能表现，并被集成到更多的实际AI应用和系统中。