Instructions to use Tesleum/Shirdel-Finance-E4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Tesleum/Shirdel-Finance-E4B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Tesleum/Shirdel-Finance-E4B", filename="FinanceGemma-E4B.Q6_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Tesleum/Shirdel-Finance-E4B 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 Tesleum/Shirdel-Finance-E4B:Q6_K # Run inference directly in the terminal: llama cli -hf Tesleum/Shirdel-Finance-E4B:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Tesleum/Shirdel-Finance-E4B:Q6_K # Run inference directly in the terminal: llama cli -hf Tesleum/Shirdel-Finance-E4B:Q6_K
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 Tesleum/Shirdel-Finance-E4B:Q6_K # Run inference directly in the terminal: ./llama-cli -hf Tesleum/Shirdel-Finance-E4B:Q6_K
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 Tesleum/Shirdel-Finance-E4B:Q6_K # Run inference directly in the terminal: ./build/bin/llama-cli -hf Tesleum/Shirdel-Finance-E4B:Q6_K
Use Docker
docker model run hf.co/Tesleum/Shirdel-Finance-E4B:Q6_K
- LM Studio
- Jan
- Ollama
How to use Tesleum/Shirdel-Finance-E4B with Ollama:
ollama run hf.co/Tesleum/Shirdel-Finance-E4B:Q6_K
- Unsloth Studio
How to use Tesleum/Shirdel-Finance-E4B 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 Tesleum/Shirdel-Finance-E4B 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 Tesleum/Shirdel-Finance-E4B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Tesleum/Shirdel-Finance-E4B to start chatting
- Pi
How to use Tesleum/Shirdel-Finance-E4B with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tesleum/Shirdel-Finance-E4B:Q6_K
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": "Tesleum/Shirdel-Finance-E4B:Q6_K" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Tesleum/Shirdel-Finance-E4B with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tesleum/Shirdel-Finance-E4B:Q6_K
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 Tesleum/Shirdel-Finance-E4B:Q6_K
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Tesleum/Shirdel-Finance-E4B with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Tesleum/Shirdel-Finance-E4B:Q6_K
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "Tesleum/Shirdel-Finance-E4B:Q6_K" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use Tesleum/Shirdel-Finance-E4B with Docker Model Runner:
docker model run hf.co/Tesleum/Shirdel-Finance-E4B:Q6_K
- Lemonade
How to use Tesleum/Shirdel-Finance-E4B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Tesleum/Shirdel-Finance-E4B:Q6_K
Run and chat with the model
lemonade run user.Shirdel-Finance-E4B-Q6_K
List all available models
lemonade list
Shirdel-Finance-E4B : GGUF
Shirdel-Finance-E4B is a finance-specialized language model built on Google Gemma-4 E4B and optimized for local inference, financial reasoning, cryptocurrency discussions, market analysis, and structured financial conversations.
The model is designed to provide strong performance across finance-related tasks while maintaining efficient execution on consumer hardware and mobile devices.
This release is distributed in GGUF format for compatibility with local inference ecosystems including llama.cpp, Ollama, LM Studio, Jan, and Open WebUI.
Model Details
Base model: Google Gemma-4 E4B
Fine-tuned model: Shirdel-Finance-E4B
Architecture: Gemma-4
Format: GGUF
Context window: 131072 tokens
Domain: Finance / Cryptocurrency / Trading
Intended use:
- Financial question answering
- Cryptocurrency analysis
- Trading education
- Market discussions
- Fintech assistance
- Structured financial reasoning
Supported Platforms
- llama.cpp
- Ollama
- LM Studio
- Jan
- Open WebUI
- KoboldCpp
- llama-cpp-python
Available Model Files
- Shirdel-Finance-E4B.Q2_K.gguf
- Shirdel-Finance-E4B.Q3_K_M.gguf
- Shirdel-Finance-E4B.Q4_K_M.gguf
- Shirdel-Finance-E4B.Q5_K_M.gguf
- Shirdel-Finance-E4B.Q6_K.gguf
- Shirdel-Finance-E4B.Q8_0.gguf
Quantization recommendations:
| Quant | Recommended usage |
|---|---|
| Q2_K | Very low memory devices |
| Q3_K_M | Mobile devices |
| Q4_K_M | Balanced quality and speed |
| Q5_K_M | Higher quality |
| Q6_K | Strong quality-performance balance |
| Q8_0 | Maximum quality |
How to use with llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="Tesleum/Shirdel-Finance-E4B",
filename="Shirdel-Finance-E4B.Q4_K_M.gguf"
)
response = llm.create_chat_completion(
messages=[
{
"role":"user",
"content":"Explain Bitcoin halving."
}
]
)
print(response)
How to use with llama.cpp
Install
curl -LsSf https://llama.cpp/install.sh | sh
Run directly
llama-cli -hf Tesleum/Shirdel-Finance-E4B:Q4_K_M
Run server mode
llama-server -hf Tesleum/Shirdel-Finance-E4B:Q4_K_M
How to use with Ollama
Run directly:
ollama run hf.co/Tesleum/Shirdel-Finance-E4B:Q4_K_M
Or create manually:
FROM ./Shirdel-Finance-E4B.Q4_K_M.gguf
TEMPLATE """{{ .Prompt }}"""
PARAMETER temperature 0.7
PARAMETER top_p 0.95
PARAMETER num_ctx 131072
Then:
ollama create shirdel-finance -f Modelfile
ollama run shirdel-finance
Recommended Tasks
- Cryptocurrency explanations
- Trading concepts
- Financial education
- Stablecoin discussions
- Tokenomics analysis
- Market sentiment discussions
- Economic reasoning
- Fintech assistance
Limitations
- Model outputs may contain inaccuracies.
- Financial outputs should not be considered investment advice.
- Market predictions may be incorrect.
- Always independently verify important information.
Training Information
Fine-tuned using finance-oriented instruction datasets and domain-specific conversational examples designed to improve:
- Financial terminology understanding
- Cryptocurrency knowledge
- Structured reasoning
- Instruction following
- Context retention
License
Please follow the original Google Gemma license and applicable downstream usage restrictions.
Author
Tesleum
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