Instructions to use BatboldSamdan/babybata-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BatboldSamdan/babybata-gpt2 with Transformers:

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

pipe = pipeline("text-generation", model="BatboldSamdan/babybata-gpt2")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BatboldSamdan/babybata-gpt2")
model = AutoModelForCausalLM.from_pretrained("BatboldSamdan/babybata-gpt2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use BatboldSamdan/babybata-gpt2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BatboldSamdan/babybata-gpt2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BatboldSamdan/babybata-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/BatboldSamdan/babybata-gpt2

SGLang

How to use BatboldSamdan/babybata-gpt2 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 "BatboldSamdan/babybata-gpt2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BatboldSamdan/babybata-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "BatboldSamdan/babybata-gpt2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BatboldSamdan/babybata-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use BatboldSamdan/babybata-gpt2 with Docker Model Runner:
```
docker model run hf.co/BatboldSamdan/babybata-gpt2
```

BabyBata 1 (GPT-2) 🍼

The first version of a small assistant for Batbold Samdan's portfolio. GPT-2 small (124M), fine-tuned to answer questions about his projects, skills and contact info. An AI/ML learning project, superseded by BabyBata 2.0.

Base model: GPT-2 small
Method: full fine-tuning with the token/position embeddings frozen
Data: ~640 question/answer pairs: hand-written + augmented (one consistent answer per topic, many phrasings, casual variants)
Exact facts use slots: the model writes {EMAIL}, {GITHUB}, {GPA}… and code replaces them (facts.py in the repo). Without this, GPT-2 blended the email and GitHub handle into a wrong address.
Code: github.com/batbold9182/babybata

Prompt format

User: <question>
BabyBata:

The model continues after BabyBata: and ends with <|endoftext|>. Fill the {SLOTS} in the output with facts.fill() from the repo.

Evaluation (15 questions not in the training data)

Version	Score
v1: 133 hand-written examples	4/15
v2: + `{SLOTS}` for exact facts	8/15
v3: + 486 augmented examples	14/15
v4: more phrasings, fewer epochs	15/15

Limitations

GPT-2 memorizes rather than understands: on harder unseen questions v3 scored only ~8/12 and sometimes invented details. BabyBata 2.0 (Qwen + LoRA + grounding) fixes most of this.

Downloads last month: 31

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for BatboldSamdan/babybata-gpt2

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openai-community/gpt2

Finetuned

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