Instructions to use Nan-Do/LeetCodeWizard_7B_V1.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nan-Do/LeetCodeWizard_7B_V1.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nan-Do/LeetCodeWizard_7B_V1.1-GGUF",
	filename="LeetCodeWizard_7B_V1.1.Q3_K.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 Nan-Do/LeetCodeWizard_7B_V1.1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0
# Run inference directly in the terminal:
llama-cli -hf Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0

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 Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0

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 Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0

Use Docker

docker model run hf.co/Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0

LM Studio
Jan
Ollama
How to use Nan-Do/LeetCodeWizard_7B_V1.1-GGUF with Ollama:
```
ollama run hf.co/Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0
```

Unsloth Studio new

How to use Nan-Do/LeetCodeWizard_7B_V1.1-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 Nan-Do/LeetCodeWizard_7B_V1.1-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 Nan-Do/LeetCodeWizard_7B_V1.1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nan-Do/LeetCodeWizard_7B_V1.1-GGUF to start chatting

Docker Model Runner
How to use Nan-Do/LeetCodeWizard_7B_V1.1-GGUF with Docker Model Runner:
```
docker model run hf.co/Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0
```

Lemonade

How to use Nan-Do/LeetCodeWizard_7B_V1.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nan-Do/LeetCodeWizard_7B_V1.1-GGUF:Q4_0

Run and chat with the model

lemonade run user.LeetCodeWizard_7B_V1.1-GGUF-Q4_0

List all available models

lemonade list

LeetCodeWizard 7B V1.1 - GGUF

Original model: LeetCodeWizard 7B V1.1

Description

This repo contains GGUF format model files for LeetCodeWizard 7B V1.1. (model template inspired by TheBloke)

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Response:

Explanation of quantisation methods

Click to see details

The new methods available are:

GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.

Provided files

Name	Quantisation method	Bits	Size
LeetCodeWizard_7B_V1.1.Q3_K.gguf	Q3_K	3	3.3 GB
LeetCodeWizard_7B_V1.1.Q4_0.gguf	Q4_0	4	3.83 GB
LeetCodeWizard_7B_V1.1.Q4_1.gguf	Q4_1	4	4.24 GB
LeetCodeWizard_7B_V1.1.Q5_0.gguf	Q5_0	5	4.65 GB
LeetCodeWizard_7B_V1.1.Q5_1.gguf	Q5_1	5	5.06 GB
LeetCodeWizard_7B_V1.1.Q6_K.gguf	Q6_K	6	5.53 GB
LeetCodeWizard_7B_V1.1.Q8_0.gguf	Q8_0	8	7.16 GB

Downloads last month: 121

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

View +1 variant

Inference Providers NEW

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Model tree for Nan-Do/LeetCodeWizard_7B_V1.1-GGUF

Base model

Nan-Do/LeetCodeWizard_7B_V1.1

Quantized

(3)

this model