Instructions to use fkarnagi/bababoi_test with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fkarnagi/bababoi_test with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = PeftModel.from_pretrained(base_model, "fkarnagi/bababoi_test")

Transformers

How to use fkarnagi/bababoi_test with Transformers:

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

pipe = pipeline("text-generation", model="fkarnagi/bababoi_test")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("fkarnagi/bababoi_test", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use fkarnagi/bababoi_test with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/fkarnagi/bababoi_test

SGLang

How to use fkarnagi/bababoi_test 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 "fkarnagi/bababoi_test" \
    --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": "fkarnagi/bababoi_test",
		"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 "fkarnagi/bababoi_test" \
        --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": "fkarnagi/bababoi_test",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fkarnagi/bababoi_test with Docker Model Runner:
```
docker model run hf.co/fkarnagi/bababoi_test
```

bababoi_test

A fine-tuned version of TinyLlama/TinyLlama-1.1B-Chat-v1.0 using LoRA (Low-Rank Adaptation) on a small instruction-following dataset.

Model Details

Property	Value
Base Model	TinyLlama/TinyLlama-1.1B-Chat-v1.0
Fine-tuning Method	LoRA (Low-Rank Adaptation)
LoRA Rank	16
LoRA Alpha	16
Trainable Parameters	~12.6M (1.13% of total)
Dataset Size	20 instruction-following examples
Training Epochs	10
Learning Rate	2e-4
Hardware	Apple M4 GPU (MPS)

Training Details

This model was trained on a tiny Alpaca-format dataset covering basic factual Q&A, grammar, translation, and simple arithmetic. It is intended as a tutorial/demo for learning fine-tuning workflows rather than production use.

Training Scripts

train.py — Python training script with LoRA + HuggingFace Trainer
inference.py — Interactive inference script
train_cli.sh / infer_cli.sh — Shell wrappers

How to Use

Load the LoRA Adapter

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
adapter = "fkarnagi/bababoi_test"

model = AutoModelForCausalLM.from_pretrained(base_model, torch_dtype="auto")
model = PeftModel.from_pretrained(model, adapter)
tokenizer = AutoTokenizer.from_pretrained(adapter)

Inference

prompt = "What is the capital of France?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

Trained on only 20 examples — will overfit and memorize answers
Not suitable for production or real-world tasks
Designed for educational purposes only

Acknowledgements

Base model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
Fine-tuning framework: HuggingFace PEFT
Training tutorial: Unsloth

Framework versions

PEFT 0.19.1

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