John1604/John1604-AI-status-2025

Model Card for Model ID

Provides AI status of 2025. Althought a lot of investment has been used in AI, AI applications arestill in its infancy.

Model Details

Model Description

This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.

• Developed by: Willam Hsu, John Xu
• Model type: Text Generation

Usage

The code of John1604/John1604-AI-status-2025 has been in the latest Hugging Face transformers version 4.55.4 and we advise you to use the latest version of transformers.

The following contains a code snippet illustrating how to use the model generate content based on given inputs.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "John1604/John1604-AI-status-2025"

# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)

# prepare the model input. If using chinese, use chinese prompt.
prompt = "What is the success rate of artificial intelligence projects now?"
messages = [
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# conduct text completion
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=16384
)
output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()

content = tokenizer.decode(output_ids, skip_special_tokens=True)

print("content:", content)

This model can be run in vllm.

Direct Use from Langchain

import torch
from langchain.chains.llm import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_huggingface import HuggingFacePipeline
from transformers import pipeline

generate_text = pipeline(model="John1604/John1604-AI-status-2025", torch_dtype=torch.bfloat16,
                         trust_remote_code=True, device_map="auto", return_full_text=True)

prompt = PromptTemplate(
    input_variables=["instruction"],
    template="{instruction}")

# template for an instruction with input
prompt_with_context = PromptTemplate(
    input_variables=["instruction", "context"],
    template="{instruction}\n\nInput:\n{context}")

hf_pipeline = HuggingFacePipeline(pipeline=generate_text)

llm_chain = LLMChain(llm=hf_pipeline, prompt=prompt)
llm_context_chain = LLMChain(llm=hf_pipeline, prompt=prompt_with_context)

print(llm_chain.predict(instruction="List OWASP 10."))

input1 = "What is the success rate of artificial intelligence projects now?"

print(llm_context_chain.predict(instruction="When was George Washington president?", context=input1))

Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

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Summary

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Model Card Authors [optional]

[William Hsu, John Xu]

Model Card Contact

[john.h.xu@outlook.com]