Instructions to use IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M", filename="Dolphin3.0-QwenStar-3b-iQ4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M # Run inference directly in the terminal: llama-cli -hf IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M # Run inference directly in the terminal: llama-cli -hf IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M: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 IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M: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 IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M
Use Docker
docker model run hf.co/IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M with Ollama:
ollama run hf.co/IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M
- Unsloth Studio new
How to use IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M 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 IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M 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 IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M to start chatting
- Pi new
How to use IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M with Docker Model Runner:
docker model run hf.co/IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M
- Lemonade
How to use IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M:Q4_K_M
Run and chat with the model
lemonade run user.Dolphin3.0-QwenStar-3b-iQ4_K_M-Q4_K_M
List all available models
lemonade list
IntelligentEstate/Dolphin3.0-QwenStar-3b-iQ4_K_M-GGUF Importance matrix trained using "THE_KEY" dataset for quatization preservation
GPT4ALL usage
Prompt Sys Message
You are a helpful assistant who answers in 2 parts, Part 1: you evaluate the query, Identify(seperately) all essential parts of the question and what equation or process you will need to answer; Part 2: Use a step by step approach to answer the question the best you can, use tools if needed.
Chat Template
{{- '<|im_start|>system\n' }}
{% if toolList|length > 0 %}You have access to the following functions:
{% for tool in toolList %}
Use the function '{{tool.function}}' to: '{{tool.description}}'
{% if tool.parameters|length > 0 %}
parameters:
{% for info in tool.parameters %}
{{info.name}}:
type: {{info.type}}
description: {{info.description}}
required: {{info.required}}
{% endfor %}
{% endif %}
# Tool Instructions
If you CHOOSE to call this function ONLY reply with the following format:
'{{tool.symbolicFormat}}'
Here is an example. If the user says, '{{tool.examplePrompt}}', then you reply
'{{tool.exampleCall}}'
After the result you might reply with, '{{tool.exampleReply}}'
{% endfor %}
You MUST include both the start and end tags when you use a function.
You are a helpful and considerate AI assistant from Intelligent Estate who uses the functions to break down, analyze, perform, and verify complex reasoning tasks. You SHOULD try to verify your answers using the functions where possible.
{% endif %}
{{- '<|im_end|>\n' }}
{% for message in messages %}
{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n' }}
{% endfor %}
{% if add_generation_prompt %}
{{ '<|im_start|>assistant\n' }}
{% endif %}
This model was converted to GGUF format from cognitivecomputations/Dolphin3.0-Qwen2.5-3b using llama.cpp
Refer to the original model card for more details on the model.
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