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

Libraries

How to use FutureMa/Eva-4B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="FutureMa/Eva-4B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FutureMa/Eva-4B-GGUF", dtype="auto")

llama-cpp-python

How to use FutureMa/Eva-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FutureMa/Eva-4B-GGUF",
	filename="Eva-4B-F16.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FutureMa/Eva-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 FutureMa/Eva-4B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf FutureMa/Eva-4B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf FutureMa/Eva-4B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf FutureMa/Eva-4B-GGUF:F16

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 FutureMa/Eva-4B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf FutureMa/Eva-4B-GGUF:F16

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 FutureMa/Eva-4B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FutureMa/Eva-4B-GGUF:F16

Use Docker

docker model run hf.co/FutureMa/Eva-4B-GGUF:F16

LM Studio
Jan

vLLM

How to use FutureMa/Eva-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FutureMa/Eva-4B-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": "FutureMa/Eva-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FutureMa/Eva-4B-GGUF:F16

SGLang

How to use FutureMa/Eva-4B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "FutureMa/Eva-4B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FutureMa/Eva-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "FutureMa/Eva-4B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FutureMa/Eva-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use FutureMa/Eva-4B-GGUF with Ollama:
```
ollama run hf.co/FutureMa/Eva-4B-GGUF:F16
```

Unsloth Studio

How to use FutureMa/Eva-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 FutureMa/Eva-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 FutureMa/Eva-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 FutureMa/Eva-4B-GGUF to start chatting

How to use FutureMa/Eva-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 FutureMa/Eva-4B-GGUF:F16

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": "FutureMa/Eva-4B-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use FutureMa/Eva-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 FutureMa/Eva-4B-GGUF:F16

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 FutureMa/Eva-4B-GGUF:F16

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use FutureMa/Eva-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/FutureMa/Eva-4B-GGUF:F16
```

Lemonade

How to use FutureMa/Eva-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FutureMa/Eva-4B-GGUF:F16

Run and chat with the model

lemonade run user.Eva-4B-GGUF-F16

List all available models

lemonade list

Eva-4B-GGUF / README.md

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metadata

language:
  - en
license: apache-2.0
base_model: FutureMa/Eva-4B
tags:
  - finance
  - earnings-calls
  - financial-nlp
  - text-classification
  - qwen3
  - llama-cpp
  - gguf
  - gguf-my-repo
pipeline_tag: text-generation
library_name: transformers

FutureMa/Eva-4B-GGUF

This repository hosts GGUF files for FutureMa/Eva-4B, intended for use with llama.cpp.

Base model: FutureMa/Eva-4B
Format: GGUF (for llama.cpp)
License: Apache-2.0

Refer to the original model card for model details, intended use, limitations, and evaluation information.

Files

Eva-4B-F16.gguf (FP16 / F16)

Use with llama.cpp

Option A: Install via Homebrew (macOS/Linux)

brew install llama.cpp

CLI

llama-cli --hf-repo FutureMa/Eva-4B-GGUF --hf-file Eva-4B-F16.gguf -p "The meaning of life and the universe is"

Server

llama-server --hf-repo FutureMa/Eva-4B-GGUF --hf-file Eva-4B-F16.gguf -c 2048

Option B: Build llama.cpp from source

Step 1: Clone llama.cpp:

git clone https://github.com/ggerganov/llama.cpp

Step 2: Build (enable Hugging Face download support):

cd llama.cpp && LLAMA_CURL=1 make

Step 3: Run:

./llama-cli --hf-repo FutureMa/Eva-4B-GGUF --hf-file Eva-4B-F16.gguf -p "The meaning of life and the universe is"

./llama-server --hf-repo FutureMa/Eva-4B-GGUF --hf-file Eva-4B-F16.gguf -c 2048

Notes

The -c 2048 value is an example context size; adjust based on your needs and available memory.
If you publish additional quantizations (e.g. Q4_K_M, Q5_K_M), add them to the Files section above and reference them in the example commands.