Instructions to use theprint/CleverBoi-7B-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use theprint/CleverBoi-7B-v2 with Transformers:

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

pipe = pipeline("text-generation", model="theprint/CleverBoi-7B-v2")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("theprint/CleverBoi-7B-v2", dtype="auto")

llama-cpp-python

How to use theprint/CleverBoi-7B-v2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="theprint/CleverBoi-7B-v2",
	filename="CleverBoi-7B-v2.BF16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use theprint/CleverBoi-7B-v2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/CleverBoi-7B-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/CleverBoi-7B-v2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf theprint/CleverBoi-7B-v2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf theprint/CleverBoi-7B-v2: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 theprint/CleverBoi-7B-v2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf theprint/CleverBoi-7B-v2: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 theprint/CleverBoi-7B-v2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf theprint/CleverBoi-7B-v2:Q4_K_M

Use Docker

docker model run hf.co/theprint/CleverBoi-7B-v2:Q4_K_M

LM Studio
Jan

vLLM

How to use theprint/CleverBoi-7B-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "theprint/CleverBoi-7B-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "theprint/CleverBoi-7B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/theprint/CleverBoi-7B-v2:Q4_K_M

SGLang

How to use theprint/CleverBoi-7B-v2 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/CleverBoi-7B-v2" \
    --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": "theprint/CleverBoi-7B-v2",
		"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 "theprint/CleverBoi-7B-v2" \
        --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": "theprint/CleverBoi-7B-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use theprint/CleverBoi-7B-v2 with Ollama:
```
ollama run hf.co/theprint/CleverBoi-7B-v2:Q4_K_M
```

Unsloth Studio

How to use theprint/CleverBoi-7B-v2 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 theprint/CleverBoi-7B-v2 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 theprint/CleverBoi-7B-v2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for theprint/CleverBoi-7B-v2 to start chatting

Docker Model Runner
How to use theprint/CleverBoi-7B-v2 with Docker Model Runner:
```
docker model run hf.co/theprint/CleverBoi-7B-v2:Q4_K_M
```

Lemonade

How to use theprint/CleverBoi-7B-v2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull theprint/CleverBoi-7B-v2:Q4_K_M

Run and chat with the model

lemonade run user.CleverBoi-7B-v2-Q4_K_M

List all available models

lemonade list

CleverBoi

The CleverBoi series is based on models that have been fine tuned on a collection of data sets that emphasize logic, inference, math and coding, also known as the CleverBoi data set.

This model has been fine tuned for 1 epoch on the CleverBoi-Abridged data set.

Uploaded model

Developed by: theprint
License: apache-2.0
Finetuned from model : unsloth/mistral-7b-v0.3-bnb-4bit

This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	14.97
IFEval (0-Shot)	21.70
BBH (3-Shot)	23.44
MATH Lvl 5 (4-Shot)	1.89
GPQA (0-shot)	5.15
MuSR (0-shot)	18.66
MMLU-PRO (5-shot)	18.98

Downloads last month: 122

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for theprint/CleverBoi-7B-v2

Base model

mistralai/Mistral-7B-v0.3

Quantized

unsloth/mistral-7b-v0.3-bnb-4bit

Quantized

(193)

this model

Collection including theprint/CleverBoi-7B-v2

CleverBoi

Collection

CleverBoi is a curated collection of data that emphasizes logic, inference, science, code, math and empathy, and its fine tuned language models. • 15 items • Updated Mar 2 • 2

Evaluation results

strict accuracy on IFEval (0-Shot)
Open LLM Leaderboard

21.700
normalized accuracy on BBH (3-Shot)
Open LLM Leaderboard

23.440
exact match on MATH Lvl 5 (4-Shot)
Open LLM Leaderboard

1.890
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

5.150
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

18.660
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

18.980