Instructions to use WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF",
	filename="GPT2.5.2-high-reasoning-codex-0.4B.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF with Ollama:
```
ollama run hf.co/WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF to start chatting

Docker Model Runner
How to use WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M
```

Lemonade

How to use WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WithinUsAI/GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.GPT2.5.2-High.Reasoning.Codex-0.4B-GGUF-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

language:

pipeline_tag: text-generation

tags:

gguf
llama.cpp
gpt2
quantized
text-generation
code
coding
reasoning
lightweight
withinusai

license: other license_name: withinusai-custom-license license_link: LICENSE

base_model: WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B base_model_relation: quantized

metrics:

pass@1
accuracy
exact_match

model-index: - name: WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B-GGUF results: []

WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B-GGUF

GGUF quantizations of the GPT-2 Medium → “GPT-5.2 twin target” finetune.
Pick your quant, run local, move fast. ⚡🧠

What this repo contains

This repository provides GGUF quantizations for local inference (llama.cpp ecosystem) of:

WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B (source Transformers model)

Model details

Architecture: gpt2
Size class: ~0.4B parameters (approx.)
Source model: WithinUsAI/GPT2.5.2-high-reasoning-codex-0.4B
Base model foundation credit: openai-community/gpt2-medium
Relation: quantized distribution of the source model

Available quantizations

Quant	Bits	Size
Q4_K_M	4-bit	242 MB
Q5_K_M	5-bit	274 MB
F16	16-bit	714 MB

Which one should you choose?

Q4_K_M: best default for CPUs (small + fast)
Q5_K_M: slightly higher quality, still compact
F16: maximum fidelity (largest)

Prompting tips

“List edge cases first, then implement.”
“Explain root cause → propose fix → provide patch.”
“State invariants + complexity.”

Example usage (llama.cpp)

Replace MODEL.gguf with the quant file you downloaded:

./llama-cli -m MODEL.gguf \
  -p "You are a senior engineer. List edge cases, then write the code.\nTask: Implement an LRU cache in Python.\n\nAnswer:\n" \
  -n 256