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-gpt2 / README.md

BatboldSamdan

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---
license: mit
language:
- en
base_model: openai-community/gpt2
library_name: transformers
pipeline_tag: text-generation
tags:
- chatbot
- portfolio
---
# BabyBata 1 (GPT-2) 🍼

The first version of a small assistant for [Batbold Samdan](https://github.com/batbold9182)'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](https://huggingface.co/BatboldSamdan/babybata2).

- **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](https://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.

	---
	license: mit
	language:
	- en
	base_model: openai-community/gpt2
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- chatbot
	- portfolio
	---

	# BabyBata 1 (GPT-2) 🍼

	The first version of a small assistant for [Batbold Samdan](https://github.com/batbold9182)'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](https://huggingface.co/BatboldSamdan/babybata2).

	- 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](https://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.