Instructions to use AGNDM/Fine-tuned_NLP_Qwen_0.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AGNDM/Fine-tuned_NLP_Qwen_0.5B with Transformers:

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

pipe = pipeline("text-generation", model="AGNDM/Fine-tuned_NLP_Qwen_0.5B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("AGNDM/Fine-tuned_NLP_Qwen_0.5B", device_map="auto")

PEFT
How to use AGNDM/Fine-tuned_NLP_Qwen_0.5B with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AGNDM/Fine-tuned_NLP_Qwen_0.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AGNDM/Fine-tuned_NLP_Qwen_0.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AGNDM/Fine-tuned_NLP_Qwen_0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AGNDM/Fine-tuned_NLP_Qwen_0.5B

SGLang

How to use AGNDM/Fine-tuned_NLP_Qwen_0.5B 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 "AGNDM/Fine-tuned_NLP_Qwen_0.5B" \
    --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": "AGNDM/Fine-tuned_NLP_Qwen_0.5B",
		"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 "AGNDM/Fine-tuned_NLP_Qwen_0.5B" \
        --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": "AGNDM/Fine-tuned_NLP_Qwen_0.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AGNDM/Fine-tuned_NLP_Qwen_0.5B with Docker Model Runner:
```
docker model run hf.co/AGNDM/Fine-tuned_NLP_Qwen_0.5B
```

Fine-tuned_NLP_Qwen_0.5B (LoRA Adapter)

This repository provides a LoRA adapter fine-tuned from Qwen/Qwen2.5-0.5B-Instruct on the allenai/qasper dataset.

Model Details

Adapter repo: AGNDM/Fine-tuned_NLP_Qwen_0.5B
Base model: Qwen/Qwen2.5-0.5B-Instruct
Task: Scientific paper question answering
Training method: PEFT LoRA (with 4-bit loading during training)

Dataset

allenai/qasper
Domain: NLP/scientific paper reading comprehension and QA

How to Use

1) Install dependencies

pip install -U transformers peft accelerate torch

2) Load base model + LoRA adapter

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

base_model_id = "Qwen/Qwen2.5-0.5B-Instruct"
adapter_id = "AGNDM/Fine-tuned_NLP_Qwen_0.5B"

tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)
base_model = AutoModelForCausalLM.from_pretrained(
    base_model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)
model = PeftModel.from_pretrained(base_model, adapter_id)

prompt = (
    "You are a helpful scientific QA assistant. "
    "Answer the question based only on the provided paper content.\n\n"
    "### Paper Context\n"
    "<paper context here>\n\n"
    "### Question\n"
    "What is the main contribution?\n\n"
    "### Answer\n"
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=128, do_sample=False)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training Setup (Summary)

Base model: Qwen/Qwen2.5-0.5B-Instruct
LoRA: r=16, alpha=32, dropout=0.05
Effective batch size: 1 x gradient_accumulation_steps(16) = 16
Max sequence length: 2048
Dataset preprocessing: QASPER paper context + question → answer format

Limitations

Model outputs may be incorrect or incomplete.
Performance depends heavily on context quality and length.
This adapter is tuned for QASPER-style scientific QA prompts.

Intended Use

Research/demo for scientific document QA
Educational use and experimentation with PEFT/LoRA

Citation

If you use this model, please cite QASPER and the base model:

@inproceedings{Dasigi2021ADO,
  title={A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers},
  author={Pradeep Dasigi and Kyle Lo and Iz Beltagy and Arman Cohan and Noah A. Smith and Matt Gardner},
  year={2021}
}

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