Instructions to use s3nh/Novocode7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use s3nh/Novocode7b-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="s3nh/Novocode7b-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("s3nh/Novocode7b-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use s3nh/Novocode7b-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 s3nh/Novocode7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf s3nh/Novocode7b-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 s3nh/Novocode7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf s3nh/Novocode7b-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 s3nh/Novocode7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf s3nh/Novocode7b-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 s3nh/Novocode7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf s3nh/Novocode7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/s3nh/Novocode7b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use s3nh/Novocode7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "s3nh/Novocode7b-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": "s3nh/Novocode7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/s3nh/Novocode7b-GGUF:Q4_K_M

SGLang

How to use s3nh/Novocode7b-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 "s3nh/Novocode7b-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": "s3nh/Novocode7b-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 "s3nh/Novocode7b-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": "s3nh/Novocode7b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use s3nh/Novocode7b-GGUF with Ollama:
```
ollama run hf.co/s3nh/Novocode7b-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use s3nh/Novocode7b-GGUF with Docker Model Runner:
```
docker model run hf.co/s3nh/Novocode7b-GGUF:Q4_K_M
```

Lemonade

How to use s3nh/Novocode7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull s3nh/Novocode7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Novocode7b-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Original model card

Buy me a coffee if you like this project ;)

Description

GGUF Format model files for This project.

GGUF Specs

GGUF is a format based on the existing GGJT, but makes a few changes to the format to make it more extensible and easier to use. The following features are desired:

Single-file deployment: they can be easily distributed and loaded, and do not require any external files for additional information. Extensible: new features can be added to GGML-based executors/new information can be added to GGUF models without breaking compatibility with existing models. mmap compatibility: models can be loaded using mmap for fast loading and saving. Easy to use: models can be easily loaded and saved using a small amount of code, with no need for external libraries, regardless of the language used. Full information: all information needed to load a model is contained in the model file, and no additional information needs to be provided by the user. The key difference between GGJT and GGUF is the use of a key-value structure for the hyperparameters (now referred to as metadata), rather than a list of untyped values. This allows for new metadata to be added without breaking compatibility with existing models, and to annotate the model with additional information that may be useful for inference or for identifying the model.

inference

User: Tell me story about what is an quantization and what do we need to build.

AI: A quantization is the process of representing continuous data or signals with discrete values. It's a crucial step in digital signal processing, computer graphics, and machine learning models, where we deal with large amounts of data that are not easily manageable in their original form.

Quantization helps us convert these data points into more manageable discrete values. This process reduces the amount of information required to represent the data, which makes it easier to store, transmit, and process.

When building a quantization system, we typically need the following components:

Data: The continuous data that needs to

Original model card

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit