Instructions to use theprint/ComputerScience-v1-2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theprint/ComputerScience-v1-2B with Transformers:

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

pipe = pipeline("text-generation", model="theprint/ComputerScience-v1-2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("theprint/ComputerScience-v1-2B")
model = AutoModelForCausalLM.from_pretrained("theprint/ComputerScience-v1-2B", device_map="auto")
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 theprint/ComputerScience-v1-2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theprint/ComputerScience-v1-2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theprint/ComputerScience-v1-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/theprint/ComputerScience-v1-2B

SGLang

How to use theprint/ComputerScience-v1-2B 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 "theprint/ComputerScience-v1-2B" \
    --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": "theprint/ComputerScience-v1-2B",
		"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 "theprint/ComputerScience-v1-2B" \
        --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": "theprint/ComputerScience-v1-2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use theprint/ComputerScience-v1-2B with Docker Model Runner:
```
docker model run hf.co/theprint/ComputerScience-v1-2B
```

ComputerScience-v1-2B / README.md

theprint

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	---
	base_model: unsloth/Qwen3.5-2B
	tags:
	- fine-tuned
	- lora
	- sft
	- auto-sft
	language:
	- en
	library_name: transformers
	---

	# ComputerScience-v1-2B

	A fine-tuned version of [`unsloth/Qwen3.5-2B`](https://huggingface.co/unsloth/Qwen3.5-2B) trained on ComputerScience ML AI Chatml 7.0k 110926 train data using [Auto-SFT](https://github.com/theprint/auto-sft) — an automated hyperparameter search and supervised fine-tuning pipeline.

	The base model was adapted to follow the style and content of the `ComputerScience ML AI Chatml 7.0k 110926 train` dataset. Expect improved performance on tasks similar to those represented in the training data.

	## Model Details

	\| Property \| Value \|
	\|---\|---\|
	\| Base model \| `unsloth/Qwen3.5-2B` \|
	\| Training data \| `data/ComputerScience-ML-AI-Chatml-7.0k-110926_train.json` \|
	\| Fine-tuning epochs \| 2 \|
	\| Fine-tuning date \| 2026-09-12 \|
	\| Fine-tuning method \| LoRA (merged to full 16-bit) \|

	## Training Hyperparameters

	### LoRA

	\| Parameter \| Value \|
	\|---\|---\|
	\| `r` \| `4` \|
	\| `alpha` \| `8` \|
	\| `dropout` \| `0.02` \|
	\| `target_modules` \| `['q_proj', 'v_proj', 'k_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj']` \|

	### Training

	\| Parameter \| Value \|
	\|---\|---\|
	\| `learning_rate` \| `0.0005` \|
	\| `batch_size` \| `4` \|
	\| `gradient_accumulation_steps` \| `1` \|
	\| `warmup_ratio` \| `0.0` \|
	\| `max_seq_length` \| `2048` \|
	\| `quantization` \| `none` \|

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("theprint/ComputerScience-v1-2B")
	tokenizer = AutoTokenizer.from_pretrained("theprint/ComputerScience-v1-2B")
	```

	---
	Generated by [Auto-SFT](https://github.com/theprint/auto-sft) — automated LoRA fine-tuning with hyperparameter search.