Instructions to use dranger003/bagel-dpo-34b-v0.5-iMat.GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dranger003/bagel-dpo-34b-v0.5-iMat.GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="dranger003/bagel-dpo-34b-v0.5-iMat.GGUF",
	filename="ggml-bagel-dpo-34b-v0.5-iq2_xs.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 dranger003/bagel-dpo-34b-v0.5-iMat.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 dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS
# Run inference directly in the terminal:
llama cli -hf dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS

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 dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS
# Run inference directly in the terminal:
./llama-cli -hf dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS

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 dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS

Use Docker

docker model run hf.co/dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS

LM Studio
Jan

vLLM

How to use dranger003/bagel-dpo-34b-v0.5-iMat.GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dranger003/bagel-dpo-34b-v0.5-iMat.GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dranger003/bagel-dpo-34b-v0.5-iMat.GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS

Ollama
How to use dranger003/bagel-dpo-34b-v0.5-iMat.GGUF with Ollama:
```
ollama run hf.co/dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS
```

Unsloth Studio

How to use dranger003/bagel-dpo-34b-v0.5-iMat.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 dranger003/bagel-dpo-34b-v0.5-iMat.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 dranger003/bagel-dpo-34b-v0.5-iMat.GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dranger003/bagel-dpo-34b-v0.5-iMat.GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use dranger003/bagel-dpo-34b-v0.5-iMat.GGUF with Docker Model Runner:
```
docker model run hf.co/dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS
```

Lemonade

How to use dranger003/bagel-dpo-34b-v0.5-iMat.GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull dranger003/bagel-dpo-34b-v0.5-iMat.GGUF:IQ2_XS

Run and chat with the model

lemonade run user.bagel-dpo-34b-v0.5-iMat.GGUF-IQ2_XS

List all available models

lemonade list

GGUF importance matrix (imatrix) quants for https://huggingface.co/jondurbin/bagel-dpo-34b-v0.5
The importance matrix was trained for 100K tokens (200 batches of 512 tokens) using wiki.train.raw.
The imatrix is being used on the K-quants as well (below Q6_K).
Generated with llama.cpp commit f87f7b89

Layers	Context	Template
60	200000	[INST] <<SYS>> {instructions} <</SYS>> {prompt} [/INST] {response}

Downloads last month: 35

GGUF

Model size

34B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

6-bit

8-bit

Model tree for dranger003/bagel-dpo-34b-v0.5-iMat.GGUF

Base model

jondurbin/bagel-dpo-34b-v0.5

Quantized

(3)

this model