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

Libraries

How to use TheBloke/CodeLlama-34B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="TheBloke/CodeLlama-34B-Instruct-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TheBloke/CodeLlama-34B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use TheBloke/CodeLlama-34B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="TheBloke/CodeLlama-34B-Instruct-GGUF",
	filename="codellama-34b-instruct.Q2_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 TheBloke/CodeLlama-34B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TheBloke/CodeLlama-34B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf TheBloke/CodeLlama-34B-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf TheBloke/CodeLlama-34B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf TheBloke/CodeLlama-34B-Instruct-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/CodeLlama-34B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TheBloke/CodeLlama-34B-Instruct-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/CodeLlama-34B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheBloke/CodeLlama-34B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TheBloke/CodeLlama-34B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use TheBloke/CodeLlama-34B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheBloke/CodeLlama-34B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheBloke/CodeLlama-34B-Instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TheBloke/CodeLlama-34B-Instruct-GGUF:Q4_K_M

SGLang

How to use TheBloke/CodeLlama-34B-Instruct-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 "TheBloke/CodeLlama-34B-Instruct-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": "TheBloke/CodeLlama-34B-Instruct-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 "TheBloke/CodeLlama-34B-Instruct-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": "TheBloke/CodeLlama-34B-Instruct-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use TheBloke/CodeLlama-34B-Instruct-GGUF with Ollama:
```
ollama run hf.co/TheBloke/CodeLlama-34B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use TheBloke/CodeLlama-34B-Instruct-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/CodeLlama-34B-Instruct-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/CodeLlama-34B-Instruct-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/CodeLlama-34B-Instruct-GGUF to start chatting

Docker Model Runner
How to use TheBloke/CodeLlama-34B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/TheBloke/CodeLlama-34B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use TheBloke/CodeLlama-34B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheBloke/CodeLlama-34B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CodeLlama-34B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Converting hf format model codeLlama34B to GGUF

by goodromka - opened Sep 12, 2023

Discussion

goodromka

Sep 12, 2023

I fine-tuned CodeLlama-34B-Instruct and now I want to convert it to the codellama-34b-instruct.Q6_K.gguf format. (HF to GGUF)
Could you please tell me how I can do this? What script or method can I use to achieve this?

I used to do it earlier using this: https://github.com/ggerganov/llama.cpp/blob/master/examples/make-ggml.py

q5sys

Sep 12, 2023

@goodromka I believe it's in https://github.com/ggerganov/llama.cpp/tree/master/gguf-py I was poking around in the llama.cpp repo last night that that stuck in my mind. I didn't look through the script in that dir, so that might not be it. But that's somewhere to start.

TheBloke

Owner Sep 12, 2023

make-ggml.py should still work fine. Just edit this line first: https://github.com/ggerganov/llama.cpp/blob/master/examples/make-ggml.py#L76 and change it to:

outfile = f"{outdir}/{outname}.{type}.gguf"

You could also change line 66 to:

fp16 = f"{outdir}/{outname}.fp16.gguf"

TBH neither change should actually be needed - you could also just rename the quant files afterwards. I don't think that llama.cpp's quantize cares what the output file is called, and nor does llama.cpp care what the input file is called. But to avoid confusion, edit the script to give them a .gguf extension.

q5sys

Sep 12, 2023

make-ggml.py should still work fine. Just edit...

Thanks for the correction. TIL.

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