metadata

license: llama2
language:
  - en
pipeline_tag: text-generation

InvestLM

This is the repo for a new financial domain large language model, InvestLM, tuned on Mixtral-8x7B-v0.1, using a carefully curated instruction dataset related to financial investment. We provide guidance on how to use InvestLM for inference.

Github Link: InvestLM

Test only, not for sharing.

About AWQ

AWQ is an efficient, accurate, and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference.

Inference

Please use the following command to log in hugging face first.

huggingface-cli login

Prompt template

[INST] {prompt} [/INST]

How to use this AWQ model from Python code

pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"

from transformers import AutoTokenizer, TextStreamer

quant_path = "yixuantt/InvestLM-Mistral-AWQ"

# Load model
model = AutoModelForCausalLM.from_pretrained(
    quant_path,
    low_cpu_mem_usage=True,
    device_map="cuda:0"
)
tokenizer = AutoTokenizer.from_pretrained(quant_path)

# Convert prompt to tokens
prompt_template = "[INST] {prompt} [/INST]"
prompt = "What is finance?"

tokens = tokenizer(
    prompt_template.format(prompt=prompt), 
    return_tensors='pt'
).input_ids.cuda()

# Generate output
generation_output = model.generate(
    tokens, 
    max_new_tokens = 512
)

print("Output: ", tokenizer.decode(generation_output[0]))