Instructions to use QuantFactory/OlympicCoder-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/OlympicCoder-7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/OlympicCoder-7B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/OlympicCoder-7B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/OlympicCoder-7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/OlympicCoder-7B-GGUF",
	filename="OlympicCoder-7B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/OlympicCoder-7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/OlympicCoder-7B-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 QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/OlympicCoder-7B-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 QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/OlympicCoder-7B-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 QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/OlympicCoder-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/OlympicCoder-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/OlympicCoder-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/OlympicCoder-7B-GGUF 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 "QuantFactory/OlympicCoder-7B-GGUF" \
    --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": "QuantFactory/OlympicCoder-7B-GGUF",
		"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 "QuantFactory/OlympicCoder-7B-GGUF" \
        --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": "QuantFactory/OlympicCoder-7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/OlympicCoder-7B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/OlympicCoder-7B-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 QuantFactory/OlympicCoder-7B-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 QuantFactory/OlympicCoder-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/OlympicCoder-7B-GGUF to start chatting

Pi new

How to use QuantFactory/OlympicCoder-7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/OlympicCoder-7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/OlympicCoder-7B-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 QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/OlympicCoder-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/OlympicCoder-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/OlympicCoder-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.OlympicCoder-7B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/OlympicCoder-7B-GGUF

This is quantized version of open-r1/OlympicCoder-7B created using llama.cpp

Original Model Card

Model Card for OlympicCoder-7B

OlympicCoder-7B is a code model that achieves strong performance on competitive coding benchmarks such as LiveCodeBench and the 2024 International Olympiad in Informatics.

Repository: https://github.com/huggingface/open-r1
Blog post: https://huggingface.co/blog/open-r1/update-3

Model description

Model type: A 7B parameter model fine-tuned on a decontaminated version of the codeforces dataset.
Language(s) (NLP): Primarily English
License: apache-2.0
Finetuned from model: Qwen/Qwen2.5-Coder-7B-Instruct

Evaluation

We compare the performance of OlympicCoder models on two main benchmarks for competitive coding:

IOI'2024: 6 very challenging problems from the 2024 International Olympiad in Informatics. Models are allowed up to 50 submissions per problem.
LiveCodeBench: Python programming problems source from platforms like CodeForces and LeetCoder. We use the v4_v5 subset of livecodebench/code_generation_lite, which corresponds to 268 problems. We use lighteval to evaluate models on LiveCodeBench using the sampling parameters described here.

The OlympicCoder models were post-trained exclusively on C++ solutions generated by DeepSeek-R1. As a result the performance on LiveCodeBench should be considered to be partially out-of-domain, since this expects models to output solutions in Python.

IOI'24

LiveCodeBench

Usage

Here's how you can run the model using the pipeline() function from 🤗 Transformers:

# pip install transformers
# pip install accelerate

import torch
from transformers import pipeline

pipe = pipeline("text-generation", model="open-r1/OlympicCoder-7B", torch_dtype=torch.bfloat16, device_map="auto")

# We use the tokenizer's chat template to format each message - see https://huggingface.co/docs/transformers/main/en/chat_templating
messages = [
    {"role": "user", "content": "Write a python program to calculate the 10th Fibonacci number"},
]
prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipe(prompt, max_new_tokens=8000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
#<|im_start|>user
#Write a python program to calculate the 10th fibonacci number<|im_end|>
#<|im_start|>assistant
#<think>Okay, I need to write a Python program that calculates the 10th Fibonacci number. Hmm, the Fibonacci sequence starts with 0 and 1. Each subsequent number is the sum of the two preceding ones. So the sequence goes: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, and so on. ...

To ensure that the model consistently outputs a long chain-of-thought, we have edited the chat template to prefill the first assistant turn with a <think> token. As a result, the outputs from this model will not show the opening <think> token if you use the model's generate() method. To apply reinforcement learning with a format reward, either prepend the <think> token to the model's completions or amend the chat template to remove the prefill.

Training procedure

Training hyper-parameters

The following hyperparameters were used during training:

dataset: open-r1/codeforces-cots
learning_rate: 4.0e-5
train_batch_size: 2
seed: 42
packing: false
distributed_type: deepspeed-zero-3
num_devices: 8
gradient_accumulation_steps: 8
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine_with_min_lr
min_lr_rate: 0.1
lr_scheduler_warmup_ratio: 0.03
num_epochs: 10.0

Downloads last month: 93

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/OlympicCoder-7B-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-Coder-7B

Finetuned

Qwen/Qwen2.5-Coder-7B-Instruct

Quantized

(188)

this model

QuantFactory
/

OlympicCoder-7B-GGUF