Instructions to use K1mG0ng/AI-taste-business-finance-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use K1mG0ng/AI-taste-business-finance-4B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="K1mG0ng/AI-taste-business-finance-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("K1mG0ng/AI-taste-business-finance-4B")
model = AutoModelForMultimodalLM.from_pretrained("K1mG0ng/AI-taste-business-finance-4B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use K1mG0ng/AI-taste-business-finance-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "K1mG0ng/AI-taste-business-finance-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "K1mG0ng/AI-taste-business-finance-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/K1mG0ng/AI-taste-business-finance-4B

SGLang

How to use K1mG0ng/AI-taste-business-finance-4B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "K1mG0ng/AI-taste-business-finance-4B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "K1mG0ng/AI-taste-business-finance-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "K1mG0ng/AI-taste-business-finance-4B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "K1mG0ng/AI-taste-business-finance-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use K1mG0ng/AI-taste-business-finance-4B with Docker Model Runner:
```
docker model run hf.co/K1mG0ng/AI-taste-business-finance-4B
```

AI-Taste-Business-Finance-4B

This repository provides a fine-tuned Qwen3 4B model for AI Taste experiments on social science research articles, with a current focus on Business, Finance article evaluation.

Model Summary

Base model: Qwen/Qwen3-4B
Architecture: Qwen3ForCausalLM
Format: Hugging Face Transformers + Safetensors
Primary use: ranking or classifying research-article prompts into discrete quality levels

Intended Use

The model is intended for research and internal experimentation on structured article-evaluation prompts, including tasks such as:

research question quality assessment
article-level prompt scoring
social science journal tier benchmarking

It is not intended as a general-purpose factual assistant or as a substitute for expert peer review.

Files

This repository keeps only the files needed to load and run the model:

model weights in safetensors
tokenizer files
model config and generation config

Training-state artifacts such as trainer checkpoints and local run metadata are intentionally excluded.

Example

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "K1mG0ng/AI-Taste-Business-Finance-4B"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

Validation

Top-1 accuracy: 53.50% (200 samples)
Top-1+2 accuracy: 82.50%
Balanced accuracy: 53.50%
Macro F1: 53.19%

Notes

Subject: BUSINESS, FINANCE
Source checkpoint: /workspace/gongziqin/228/RQ/finetune/social_science_rqcontext_BUSINESS,_FINANCE_4B_/final_model
Output quality depends on prompt format and label schema used at inference time.