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

Libraries

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("theprint/ComputerScience-v1-2B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use theprint/ComputerScience-v1-2B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf theprint/ComputerScience-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/ComputerScience-v1-2B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf theprint/ComputerScience-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf theprint/ComputerScience-v1-2B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf theprint/ComputerScience-v1-2B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf theprint/ComputerScience-v1-2B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/theprint/ComputerScience-v1-2B-GGUF:Q4_K_M

Llama new
LM Studio
Jan
Ollama
How to use theprint/ComputerScience-v1-2B-GGUF with Ollama:
```
ollama run hf.co/theprint/ComputerScience-v1-2B-GGUF:Q4_K_M
```
Unsloth Desktop

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf theprint/ComputerScience-v1-2B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "theprint/ComputerScience-v1-2B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use theprint/ComputerScience-v1-2B-GGUF with Docker Model Runner:
```
docker model run hf.co/theprint/ComputerScience-v1-2B-GGUF:Q4_K_M
```

Lemonade

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

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/ComputerScience-v1-2B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ComputerScience-v1-2B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use theprint/ComputerScience-v1-2B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf theprint/ComputerScience-v1-2B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default theprint/ComputerScience-v1-2B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf theprint/ComputerScience-v1-2B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "theprint/ComputerScience-v1-2B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

ComputerScience-v1-2B-GGUF / README.md

theprint

Upload README.md with huggingface_hub

c8de057 verified about 2 months ago

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metadata

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

ComputerScience-v1-2B (GGUF)

A fine-tuned version of unsloth/Qwen3.5-2B trained on ComputerScience ML AI Chatml 7.0k 110926 train data using 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`

GGUF Files

These quantized GGUF files can be used directly with llama.cpp, Ollama, LM Studio, and other compatible runtimes.

File	Description
`ComputerScience-v1-2B-GGUF-BF16.gguf`	BF16
`ComputerScience-v1-2B-GGUF-Q8_0.gguf`	8-bit — near-lossless, larger file
`ComputerScience-v1-2B-GGUF-Q6_K.gguf`	6-bit — high quality
`ComputerScience-v1-2B-GGUF-Q5_K_M.gguf`	5-bit medium — good quality/size balance
`ComputerScience-v1-2B-GGUF-Q5_K_S.gguf`	Q5_K_S
`ComputerScience-v1-2B-GGUF-Q4_K_M.gguf`	4-bit medium — recommended for most use cases
`ComputerScience-v1-2B-GGUF-Q4_K_S.gguf`	Q4_K_S
`ComputerScience-v1-2B-GGUF-Q3_K_L.gguf`	Q3_K_L
`ComputerScience-v1-2B-GGUF-Q3_K_M.gguf`	Q3_K_M
`ComputerScience-v1-2B-GGUF-Q3_K_S.gguf`	Q3_K_S
`ComputerScience-v1-2B-GGUF-Q2_K.gguf`	2-bit — smallest size, lowest quality
`ComputerScience-v1-2B-GGUF-IQ4_XS.gguf`	IQ4_XS
`ComputerScience-v1-2B-GGUF-IQ4_NL.gguf`	IQ4_NL
`ComputerScience-v1-2B-GGUF-TQ2_0.gguf`	TQ2_0

Generated by Auto-SFT — automated LoRA fine-tuning with hyperparameter search.