Instructions to use satyajitdas/EthereumGPT-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use satyajitdas/EthereumGPT-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="satyajitdas/EthereumGPT-4B-GGUF",
	filename="EthereumGPT-4B-Q8_0.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 satyajitdas/EthereumGPT-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf satyajitdas/EthereumGPT-4B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf satyajitdas/EthereumGPT-4B-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf satyajitdas/EthereumGPT-4B-GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf satyajitdas/EthereumGPT-4B-GGUF:Q8_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 satyajitdas/EthereumGPT-4B-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf satyajitdas/EthereumGPT-4B-GGUF:Q8_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 satyajitdas/EthereumGPT-4B-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf satyajitdas/EthereumGPT-4B-GGUF:Q8_0

Use Docker

docker model run hf.co/satyajitdas/EthereumGPT-4B-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use satyajitdas/EthereumGPT-4B-GGUF with Ollama:
```
ollama run hf.co/satyajitdas/EthereumGPT-4B-GGUF:Q8_0
```

Unsloth Studio

How to use satyajitdas/EthereumGPT-4B-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 satyajitdas/EthereumGPT-4B-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 satyajitdas/EthereumGPT-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for satyajitdas/EthereumGPT-4B-GGUF to start chatting

How to use satyajitdas/EthereumGPT-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf satyajitdas/EthereumGPT-4B-GGUF:Q8_0

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": "satyajitdas/EthereumGPT-4B-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use satyajitdas/EthereumGPT-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf satyajitdas/EthereumGPT-4B-GGUF:Q8_0

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 satyajitdas/EthereumGPT-4B-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use satyajitdas/EthereumGPT-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/satyajitdas/EthereumGPT-4B-GGUF:Q8_0
```

Lemonade

How to use satyajitdas/EthereumGPT-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull satyajitdas/EthereumGPT-4B-GGUF:Q8_0

Run and chat with the model

lemonade run user.EthereumGPT-4B-GGUF-Q8_0

List all available models

lemonade list

EthereumGPT-4B-GGUF

Qwen3-4B-Instruct fine-tuned on ~21,000 Ethereum Q&A examples (19,893 synthetic + 1,040 amplified anchor facts + 69 refusal examples) derived from the Eth R&D Discord archive. Specializes in Ethereum protocol development, EIPs, consensus mechanisms, the EVM, and client implementations.

Quick Start

Ollama (easiest)

# One command — includes system prompt, chat template, everything
ollama run satyajitdas/ethereumgpt

LM Studio

Search for satyajitdas/EthereumGPT-4B-GGUF in the model browser and download EthereumGPT-4B-Q8_0.gguf

Important: Set the system prompt to:

You are EthereumGPT, an AI assistant specializing in Ethereum protocol development, smart contracts, consensus mechanisms, and the broader Ethereum ecosystem. You draw on deep knowledge of EIPs, client implementations (Geth, Prysm, Lighthouse, Reth), the EVM, Solidity, and Ethereum R&D discussions.

Important: Ensure the chat template / prompt format is set to Qwen3 or ChatML. If responses contain </tool_call> tags or the model identifies as "Qwen", the template is wrong. The correct format uses <|im_start|> and <|im_end|> tokens.

Ollama from HuggingFace

# Alternative: run directly from this HuggingFace repo
ollama run hf.co/satyajitdas/EthereumGPT-4B-GGUF:Q8_0

llama.cpp

./llama-cli -m EthereumGPT-4B-Q8_0.gguf \
  --chat-template chatml \
  -sys "You are EthereumGPT, an AI assistant specializing in Ethereum protocol development, smart contracts, consensus mechanisms, and the broader Ethereum ecosystem." \
  -cnv

Available Files

File	Size	Description
`EthereumGPT-4B-Q8_0.gguf`	4.0 GB	8-bit quantized (recommended)
`EthereumGPT-4B-f16.gguf`	7.5 GB	Full fp16 precision

Training Details (v6)

Base model: Qwen/Qwen3-4B-Instruct-2507 (bf16)
Method: Full LoRA (rank 64, alpha 64, scale 1.0, dropout 0.05) via MLX on Apple M4 Max
Data: ~21,000 examples total:
- 19,893 synthetic Q&A pairs generated from 18,245 Ethereum R&D Discord conversations
- 26 unique anchor Q&As x 40 copies = 1,040 amplified anchor facts (Fusaka, ePBS, Pectra, developer identity)
- 69 refusal/negative examples for unknown entities
Data generation: Each Discord conversation was fed to Qwen3-Coder-30B which distilled it into expert-quality Q&A pairs with date-aware Ethereum context
Training: 5 epochs, lr 1e-5 with cosine decay, ~53 hours
Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj (all layers)

What It Knows

The model has been trained on real Ethereum R&D discussions spanning 2019-2026, covering:

Protocol development: EIP-1559 (fee market), EIP-4844 (proto-danksharding), The Merge (PoW to PoS), Pectra fork, Fusaka
Consensus: Beacon chain, forkchoice, attestations, validator lifecycle, slashing, ePBS (enshrined Proposer-Builder Separation)
Execution: EVM opcodes, EOF, DELEGATECALL, state management, gas mechanics
Infrastructure: Client implementations (Geth, Prysm, Lighthouse, Reth, Nethermind), testnets, devnets
Research: ePBS, Verkle trees, PeerDAS, data availability sampling, MEV/PBS
People: Core developers like Potuz, Terrence (Prysm), and others from Ethereum R&D

Example

Q: What is Fusaka?
A: Fusaka is the name for an upcoming Ethereum hard fork that combines the Fulu
   (consensus layer) and Osaka (execution layer) upgrades. Fusaka is the next major
   network upgrade planned after Pectra. Key items discussed for inclusion include
   PeerDAS (EIP-7594) for improved data availability.

Q: What does ePBS stand for?
A: ePBS stands for enshrined Proposer-Builder Separation. It is a proposed
   protocol-level change to Ethereum that would enshrine the separation of block
   proposers and block builders directly into the consensus protocol, rather than
   relying on external middleware like MEV-Boost.

Limitations

Trained on Discord conversations up to early 2026; may not reflect the very latest protocol changes
Best at factual Q&A about Ethereum internals; not designed for code generation or smart contract auditing
Requires the correct Qwen3/ChatML chat template and system prompt for best results

License

Apache 2.0 (same as the base Qwen3-4B-Instruct model)

Downloads last month: 26

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

8-bit

16-bit

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Model tree for satyajitdas/EthereumGPT-4B-GGUF

Base model

Qwen/Qwen3-4B-Instruct-2507

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