Instructions to use second-state/WizardCoder-Python-7B-v1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use second-state/WizardCoder-Python-7B-v1.0-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="second-state/WizardCoder-Python-7B-v1.0-GGUF")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("second-state/WizardCoder-Python-7B-v1.0-GGUF")
model = AutoModelForCausalLM.from_pretrained("second-state/WizardCoder-Python-7B-v1.0-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use second-state/WizardCoder-Python-7B-v1.0-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 second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/WizardCoder-Python-7B-v1.0-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 second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf second-state/WizardCoder-Python-7B-v1.0-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 second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf second-state/WizardCoder-Python-7B-v1.0-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 second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use second-state/WizardCoder-Python-7B-v1.0-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "second-state/WizardCoder-Python-7B-v1.0-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "second-state/WizardCoder-Python-7B-v1.0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M

SGLang

How to use second-state/WizardCoder-Python-7B-v1.0-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 "second-state/WizardCoder-Python-7B-v1.0-GGUF" \
    --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": "second-state/WizardCoder-Python-7B-v1.0-GGUF",
		"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 "second-state/WizardCoder-Python-7B-v1.0-GGUF" \
        --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": "second-state/WizardCoder-Python-7B-v1.0-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use second-state/WizardCoder-Python-7B-v1.0-GGUF with Ollama:
```
ollama run hf.co/second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M
```

Unsloth Studio

How to use second-state/WizardCoder-Python-7B-v1.0-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 second-state/WizardCoder-Python-7B-v1.0-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 second-state/WizardCoder-Python-7B-v1.0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for second-state/WizardCoder-Python-7B-v1.0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use second-state/WizardCoder-Python-7B-v1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M
```

Lemonade

How to use second-state/WizardCoder-Python-7B-v1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull second-state/WizardCoder-Python-7B-v1.0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WizardCoder-Python-7B-v1.0-GGUF-Q4_K_M

List all available models

lemonade list

second-state
/

WizardCoder-Python-7B-v1.0-GGUF

Add models

+version https://git-lfs.github.com/spec/v1
+oid sha256:aade30c3576023f5645daff39fd7e0b96ad06828f672fcddaad33f14e44a775c
+size 2532868480

+version https://git-lfs.github.com/spec/v1
+oid sha256:68eb0a98b41358ac21dce6bc6db18e0c91eee5905f526ebc972198e90b1233f2
+size 3597116192

+version https://git-lfs.github.com/spec/v1
+oid sha256:fa999fcbff987c83ad7f1961c598c08947aff65dd7dc7948c800e5b49fa8db26
+size 3298009888

+version https://git-lfs.github.com/spec/v1
+oid sha256:ae1ae76ca547984025d3785b3b449f6e3d7f4808b2942349e0cf14772d935e7d
+size 2948309792

+version https://git-lfs.github.com/spec/v1
+oid sha256:158ff5ae22141f0c4d1692ab433431dc1a49c7c56acef8d55f5b5e907f665663
+size 3825812800

+version https://git-lfs.github.com/spec/v1
+oid sha256:df0d8906a97deeec15e9b1ebc399f65bf203d43e978fdb076e56ddf2a3899117
+size 4081009984

+version https://git-lfs.github.com/spec/v1
+oid sha256:af93491123629ed1a001b604cf89e527f6d480290cfc84e447f237b114747011
+size 3856745792

+version https://git-lfs.github.com/spec/v1
+oid sha256:1a01482ccfc8d3e117497ed214d43af6a1ca72a16829f0c23d61e801040e461d
+size 4651697984

+version https://git-lfs.github.com/spec/v1
+oid sha256:ff61076feb2f3c9d049d12869532d6f5feb855ce501eff1a3d155ac6d29f283a
+size 4783163200

+version https://git-lfs.github.com/spec/v1
+oid sha256:cbb31e4dd9b46985980683aee27aeb96af867d67eee40b39abba99d0a0717df7
+size 5529200992

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:6f5e3f81a754da768d4c1a4019435181595b81378c6a8405d558f0a742e49ee7
+size 7161098528