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

WizardCoder-Python-7B-v1.0-GGUF / README.md

apepkuss79

Update README.md

9012cc0 verified over 2 years ago

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	---
	license: llama2
	library_name: transformers
	tags:
	- code
	metrics:
	- code_eval
	base_model: WizardLM/WizardCoder-Python-7b-V1.0
	inference: false
	model_creator: WizardLM
	model_type: llama
	pipeline_tag: text-generation
	quantized_by: Second State Inc.
	---

	<!-- header start -->
	<!-- 200823 -->
	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://github.com/LlamaEdge/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>
	<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
	<!-- header end -->

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

	## Original Model

	[WizardLM/WizardCoder-Python-7b-V1.0](https://huggingface.co/WizardLM/WizardCoder-Python-7B-V1.0)

	## Run with LlamaEdge

	- LlamaEdge version: [v0.2.8](https://github.com/LlamaEdge/LlamaEdge/releases/tag/0.2.8) and above

	- Prompt template

	- Prompt type: `wizard-coder`

	- Prompt string

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

	\### Instruction:
	{instruction}

	\### Response:
	```

	Note that the \ character is used to escape the ### in the prompt string. Remove it in the practical use.

	- Context size: `4096`

	- Run as LlamaEdge service

	```bash
	wasmedge --dir .:. --nn-preload default:GGML:AUTO:WizardCoder-Python-7B-V1.0-Q5_K_M.gguf llama-api-server.wasm -p wizard-coder
	```

	- Run as LlamaEdge command app

	```bash
	wasmedge --dir .:. --nn-preload default:GGML:AUTO:WizardCoder-Python-7B-V1.0-Q5_K_M.gguf llama-chat.wasm -p wizard-coder -s 'Below is an instruction that describes a task. Write a response that appropriately completes the request.'
	```

	## Quantized GGUF Models

	\| Name \| Quant method \| Bits \| Size \| Use case \|
	\| ---- \| ---- \| ---- \| ---- \| ----- \|
	\| [WizardCoder-Python-7B-V1.0-Q2_K.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q2_K.gguf) \| Q2_K \| 2 \| 2.53 GB\| smallest, significant quality loss - not recommended for most purposes \|
	\| [WizardCoder-Python-7B-V1.0-Q3_K_L.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q3_K_L.gguf) \| Q3_K_L \| 3 \| 3.60 GB\| small, substantial quality loss \|
	\| [WizardCoder-Python-7B-V1.0-Q3_K_M.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q3_K_M.gguf) \| Q3_K_M \| 3 \| 3.30 GB\| very small, high quality loss \|
	\| [WizardCoder-Python-7B-V1.0-Q3_K_S.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q3_K_S.gguf) \| Q3_K_S \| 3 \| 2.95 GB\| very small, high quality loss \|
	\| [WizardCoder-Python-7B-V1.0-Q4_0.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q4_0.gguf) \| Q4_0 \| 4 \| 3.83 GB\| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [WizardCoder-Python-7B-V1.0-Q4_K_M.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q4_K_M.gguf) \| Q4_K_M \| 4 \| 4.08 GB\| medium, balanced quality - recommended \|
	\| [WizardCoder-Python-7B-V1.0-Q4_K_S.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q4_K_S.gguf) \| Q4_K_S \| 4 \| 3.86 GB\| small, greater quality loss \|
	\| [WizardCoder-Python-7B-V1.0-Q5_0.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q5_0.gguf) \| Q5_0 \| 5 \| 4.65 GB\| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [WizardCoder-Python-7B-V1.0-Q5_K_M.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q5_K_M.gguf) \| Q5_K_M \| 5 \| 4.78 GB\| large, very low quality loss - recommended \|
	\| [WizardCoder-Python-7B-V1.0-Q5_K_S.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q5_K_S.gguf) \| Q5_K_S \| 5 \| 4.65 GB\| large, low quality loss - recommended \|
	\| [WizardCoder-Python-7B-V1.0-Q6_K.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q6_K.gguf) \| Q6_K \| 6 \| 5.53 GB\| very large, extremely low quality loss \|
	\| [WizardCoder-Python-7B-V1.0-Q8_0.gguf](https://huggingface.co/second-state/WizardCoder-Python-7B-v1.0-GGUF/blob/main/WizardCoder-Python-7B-V1.0-Q8_0.gguf) \| Q8_0 \| 8 \| 7.16 GB\| very large, extremely low quality loss - not recommended \|