Instructions to use Jebadiah/Aria-coder-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Jebadiah/Aria-coder-7b 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 Jebadiah/Aria-coder-7b:F16 # Run inference directly in the terminal: llama cli -hf Jebadiah/Aria-coder-7b:F16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Jebadiah/Aria-coder-7b:F16 # Run inference directly in the terminal: llama cli -hf Jebadiah/Aria-coder-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-7b:F16 # Run inference directly in the terminal: ./llama-cli -hf Jebadiah/Aria-coder-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-7b:F16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf Jebadiah/Aria-coder-7b:F16
Use Docker
docker model run hf.co/Jebadiah/Aria-coder-7b:F16
- LM Studio
- Jan
- Ollama
How to use Jebadiah/Aria-coder-7b with Ollama:
ollama run hf.co/Jebadiah/Aria-coder-7b:F16
- Unsloth Studio
How to use Jebadiah/Aria-coder-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-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-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-7b to start chatting
- Docker Model Runner
How to use Jebadiah/Aria-coder-7b with Docker Model Runner:
docker model run hf.co/Jebadiah/Aria-coder-7b:F16
- Lemonade
How to use Jebadiah/Aria-coder-7b with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Jebadiah/Aria-coder-7b:F16
Run and chat with the model
lemonade run user.Aria-coder-7b-F16
List all available models
lemonade list
- Atomic Chat
| tags: | |
| - merge | |
| - mergekit | |
| - lazymergekit | |
| # Aria-coder-7b | |
| Aria-coder-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): | |
| ## 🧩 Configuration | |
| ```yaml | |
| name: Aria-coder-7b | |
| merge_method: sce | |
| parameters: | |
| select_topk: 0.666 | |
| normalize: true | |
| dtype: float32 | |
| out_dtype: bfloat16 | |
| base_model: Jebadiah/Aria-ruby-v3 | |
| tokenizer: | |
| source: union | |
| special_tokens: keep_all | |
| priority: none | |
| add_padding_token: true | |
| force_fast_tokenizer: true # Can help with compatibility | |
| resolve_conflicts: append_ids # Append IDs to conflicting tokens to make them unique | |
| models: | |
| - model: xingyaoww/CodeActAgent-Mistral-7b-v0.1 | |
| - model: Badgids/Gonzo-Code-7B | |
| - model: Jebadiah/Aria-ruby-v3 | |
| - model: flammenai/flammen31-mistral-7B | |
| - model: fhai50032/SamChat | |
| ``` | |
| ## 💻 Usage | |
| ```python | |
| !pip install -qU transformers accelerate | |
| from transformers import AutoTokenizer | |
| import transformers | |
| import torch | |
| model = "Jebadiah/Aria-coder-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"]) | |
| ``` |