Instructions to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF 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 QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-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 QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-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 QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-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 QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-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 QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M
- Ollama
How to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF with Ollama:
ollama run hf.co/QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-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 QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-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 QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Use Docker
docker model run hf.co/QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF:QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF
This is quantized version of OwenArli/ArliAI-Llama-3-8B-Dolfin-v0.5 created using llama.cpp
Model Description
Based on Meta-Llama-3-8b-Instruct, and is governed by Meta Llama 3 License agreement: https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct
This is a fine tune using an improved Dolphin and WizardLM dataset intended to make the model follow instructions better and refuse less.
OpenLLM Benchmark:
Training:
- 2048 sequence length since the dataset has an average length of under 1000 tokens, while the base model is 8192 sequence length. From testing it still performs the same 8192 context just fine.
- Training duration is around 2 days on 2xRTX 3090, using 4-bit loading and Qlora 64-rank 128-alpha resulting in ~2% trainable weights.
Instruct format:
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{{ system_prompt }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_1 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{{ model_answer_1 }}<|eot_id|><|start_header_id|>user<|end_header_id|>
{{ user_message_2 }}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
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Model tree for QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF
Base model
OwenArli/ArliAI-Llama-3-8B-Dolfin-v0.5
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "QuantFactory/ArliAI-Llama-3-8B-Dolfin-v0.5-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'