Instructions to use TheBloke/WizardCoder-Python-34B-V1.0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TheBloke/WizardCoder-Python-34B-V1.0-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TheBloke/WizardCoder-Python-34B-V1.0-GGUF", dtype="auto")

llama-cpp-python

How to use TheBloke/WizardCoder-Python-34B-V1.0-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TheBloke/WizardCoder-Python-34B-V1.0-GGUF",
	filename="wizardcoder-python-34b-v1.0.Q2_K.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 TheBloke/WizardCoder-Python-34B-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 TheBloke/WizardCoder-Python-34B-V1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheBloke/WizardCoder-Python-34B-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 TheBloke/WizardCoder-Python-34B-V1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheBloke/WizardCoder-Python-34B-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 TheBloke/WizardCoder-Python-34B-V1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TheBloke/WizardCoder-Python-34B-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 TheBloke/WizardCoder-Python-34B-V1.0-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheBloke/WizardCoder-Python-34B-V1.0-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TheBloke/WizardCoder-Python-34B-V1.0-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use TheBloke/WizardCoder-Python-34B-V1.0-GGUF with Ollama:
```
ollama run hf.co/TheBloke/WizardCoder-Python-34B-V1.0-GGUF:Q4_K_M
```

Unsloth Studio

How to use TheBloke/WizardCoder-Python-34B-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 TheBloke/WizardCoder-Python-34B-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 TheBloke/WizardCoder-Python-34B-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 TheBloke/WizardCoder-Python-34B-V1.0-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use TheBloke/WizardCoder-Python-34B-V1.0-GGUF with Docker Model Runner:
```
docker model run hf.co/TheBloke/WizardCoder-Python-34B-V1.0-GGUF:Q4_K_M
```

Lemonade

How to use TheBloke/WizardCoder-Python-34B-V1.0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheBloke/WizardCoder-Python-34B-V1.0-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.WizardCoder-Python-34B-V1.0-GGUF-Q4_K_M

List all available models

lemonade list

[AUTOMATED] Model Memory Requirements

by model-sizer-bot - opened Nov 8, 2023

Discussion

model-sizer-bot

Nov 8, 2023

Model Memory Requirements

You will need about {'dtype': 'float16/bfloat16', 'Largest Layer or Residual Group': '387.02 MB', 'Total Size': '12.34 GB', 'Training using Adam': '49.35 GB'} VRAM to load this model for inference, and {'dtype': 'int4', 'Largest Layer or Residual Group': '96.75 MB', 'Total Size': '3.08 GB', 'Training using Adam': '12.34 GB'} VRAM to train it using Adam.

These calculations were measured from the Model Memory Utility Space on the Hub.

The minimum recommended vRAM needed for this model assumes using Accelerate or device_map="auto" and is denoted by the size of the "largest layer".
When performing inference, expect to add up to an additional 20% to this, as found by EleutherAI. More tests will be performed in the future to get a more accurate benchmark for each model.

When training with Adam, you can expect roughly 4x the reported results to be used. (1x for the model, 1x for the gradients, and 2x for the optimizer).

Results:

dtype	Largest Layer or Residual Group	Total Size	Training using Adam
float32	774.03 MB	24.68 GB	98.71 GB
float16/bfloat16	387.02 MB	12.34 GB	49.35 GB
int8	193.51 MB	6.17 GB	24.68 GB
int4	96.75 MB	3.08 GB	12.34 GB

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