Instructions to use deagentai/lobe3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use deagentai/lobe3 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="deagentai/lobe3",
	filename="ggml-model-Q4_0.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 deagentai/lobe3 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deagentai/lobe3:Q4_0
# Run inference directly in the terminal:
llama-cli -hf deagentai/lobe3:Q4_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf deagentai/lobe3:Q4_0
# Run inference directly in the terminal:
llama-cli -hf deagentai/lobe3:Q4_0

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 deagentai/lobe3:Q4_0
# Run inference directly in the terminal:
./llama-cli -hf deagentai/lobe3:Q4_0

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 deagentai/lobe3:Q4_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf deagentai/lobe3:Q4_0

Use Docker

docker model run hf.co/deagentai/lobe3:Q4_0

LM Studio
Jan
Ollama
How to use deagentai/lobe3 with Ollama:
```
ollama run hf.co/deagentai/lobe3:Q4_0
```

Unsloth Studio new

How to use deagentai/lobe3 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 deagentai/lobe3 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 deagentai/lobe3 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for deagentai/lobe3 to start chatting

Docker Model Runner
How to use deagentai/lobe3 with Docker Model Runner:
```
docker model run hf.co/deagentai/lobe3:Q4_0
```

Lemonade

How to use deagentai/lobe3 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deagentai/lobe3:Q4_0

Run and chat with the model

lemonade run user.lobe3-Q4_0

List all available models

lemonade list

Crypto-Expert LLM

Model Description

This model is a fine-tuned version of Qwen-7B, specifically optimized for cryptocurrency, Web3, and DeFi-related tasks. It is designed to provide specialized knowledge and decision ability while maintaining computational efficiency.

Supported Tasks

Cryptocurrency and DeFi concepts explanation
Smart contract analysis and auditing guidance
Trading strategy discussions
AMM (Automated Market Maker) mechanics
MEV (Maximal Extractable Value) analysis
Web3 development assistance (Rust, Python)
Decentralized infrastructure (IPFS, libp2p)

Training Data

The model is fine-tuned on specialized crypto and web3 prompts. Training data should be placed in the ./json/ directory in JSONL format.

Performance and Limitations

The model is optimized for:

Reduced resource consumption compared to larger models
Domain-specific accuracy in crypto/Web3 contexts
Cost-effective deployment

Note: Specific performance metrics will be added after training evaluation.

Intended Use

This model is designed for:

Developers working on Web3 projects
DeFi researchers and analysts
Cryptocurrency protocol designers
Smart contract developers
Blockchain infrastructure engineers

Ethical Considerations

Users should:

Verify all financial advice independently
Be aware of data privacy when using the model
Understand the model's limitations in real-time market analysis
Follow appropriate licensing requirements for training data

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Safetensors

Model size

8B params

Tensor type

F16

Inference Providers NEW

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Quantizations

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