Instructions to use Jebadiah/Aria-coder-plus-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Jebadiah/Aria-coder-plus-7b with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Jebadiah/Aria-coder-plus-7b",
	filename="Aria-rp-coder-plus-7b-F16.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 Jebadiah/Aria-coder-plus-7b with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jebadiah/Aria-coder-plus-7b:F16
# Run inference directly in the terminal:
llama-cli -hf Jebadiah/Aria-coder-plus-7b:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jebadiah/Aria-coder-plus-7b:F16
# Run inference directly in the terminal:
llama-cli -hf Jebadiah/Aria-coder-plus-7b:F16

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 Jebadiah/Aria-coder-plus-7b:F16
# Run inference directly in the terminal:
./llama-cli -hf Jebadiah/Aria-coder-plus-7b:F16

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 Jebadiah/Aria-coder-plus-7b:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jebadiah/Aria-coder-plus-7b:F16

Use Docker

docker model run hf.co/Jebadiah/Aria-coder-plus-7b:F16

LM Studio
Jan
Ollama
How to use Jebadiah/Aria-coder-plus-7b with Ollama:
```
ollama run hf.co/Jebadiah/Aria-coder-plus-7b:F16
```

Unsloth Studio new

How to use Jebadiah/Aria-coder-plus-7b 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 Jebadiah/Aria-coder-plus-7b 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 Jebadiah/Aria-coder-plus-7b to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Jebadiah/Aria-coder-plus-7b to start chatting

Docker Model Runner
How to use Jebadiah/Aria-coder-plus-7b with Docker Model Runner:
```
docker model run hf.co/Jebadiah/Aria-coder-plus-7b:F16
```

Lemonade

How to use Jebadiah/Aria-coder-plus-7b with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Jebadiah/Aria-coder-plus-7b:F16

Run and chat with the model

lemonade run user.Aria-coder-plus-7b-F16

List all available models

lemonade list

Aria-coder-plus-7b / README.md

Jebadiah

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	---
	base_model:
	- Jebadiah/Aria-coder-7b
	- Jebadiah/Aria-rp-coder-7b
	tags:
	- merge
	- mergekit
	- lazymergekit
	- Jebadiah/Aria-coder-7b
	- Jebadiah/Aria-rp-coder-7b
	---

	# Aria-coder-plus-7b

	Aria-coder-plus-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
	* [Jebadiah/Aria-coder-7b](https://huggingface.co/Jebadiah/Aria-coder-7b)
	* [Jebadiah/Aria-rp-coder-7b](https://huggingface.co/Jebadiah/Aria-rp-coder-7b)

	## 🧩 Configuration

	```yaml
	name: Aria-coder-plus-7b
	merge_method: arcee_fusion
	base_model: Jebadiah/Aria-coder-7b
	models:
	- model: Jebadiah/Aria-coder-7b
	parameters:
	weight: 0.8

	- model: Jebadiah/Aria-rp-coder-7b
	parameters:
	weight: 0.6

	dtype: float32
	out_dtype: bfloat16
	```

	## 💻 Usage

	```python
	!pip install -qU transformers accelerate

	from transformers import AutoTokenizer
	import transformers
	import torch

	model = "Jebadiah/Aria-coder-plus-7b"
	messages = [{"role": "user", "content": "What is a large language model?"}]

	tokenizer = AutoTokenizer.from_pretrained(model)
	prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
	pipeline = transformers.pipeline(
	"text-generation",
	model=model,
	torch_dtype=torch.float16,
	device_map="auto",
	)

	outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
	print(outputs[0]["generated_text"])
	```