Text Generation
Transformers
GGUF
English
finance
earnings-calls
financial-nlp
text-classification
qwen3
llama-cpp
gguf-my-repo
conversational
Instructions to use FutureMa/Eva-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
- Pi
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
Create README.md
Browse files
README.md
ADDED
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---
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language:
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- en
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license: apache-2.0
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base_model: FutureMa/Eva-4B
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tags:
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- finance
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- earnings-calls
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- financial-nlp
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- text-classification
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- qwen3
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- llama-cpp
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- gguf
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- gguf-my-repo
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pipeline_tag: text-generation
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library_name: transformers
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---
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# FutureMa/Eva-4B-GGUF
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This repository hosts **GGUF** files for [`FutureMa/Eva-4B`](https://huggingface.co/FutureMa/Eva-4B), intended for use with [`llama.cpp`](https://github.com/ggerganov/llama.cpp).
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- **Base model:** `FutureMa/Eva-4B`
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- **Format:** GGUF (for llama.cpp)
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- **License:** Apache-2.0
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Refer to the [original model card](https://huggingface.co/FutureMa/Eva-4B) for model details, intended use, limitations, and evaluation information.
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## Files
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- `Eva-4B-F16.gguf` (FP16 / F16)
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## Use with llama.cpp
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### Option A: Install via Homebrew (macOS/Linux)
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```bash
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brew install llama.cpp
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```
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#### CLI
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```bash
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llama-cli --hf-repo FutureMa/Eva-4B-GGUF --hf-file Eva-4B-F16.gguf -p "The meaning of life and the universe is"
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```
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#### Server
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```bash
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llama-server --hf-repo FutureMa/Eva-4B-GGUF --hf-file Eva-4B-F16.gguf -c 2048
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```
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### Option B: Build llama.cpp from source
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Step 1: Clone llama.cpp:
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```bash
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git clone https://github.com/ggerganov/llama.cpp
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```
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Step 2: Build (enable Hugging Face download support):
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```bash
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cd llama.cpp && LLAMA_CURL=1 make
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```
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Step 3: Run:
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```bash
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./llama-cli --hf-repo FutureMa/Eva-4B-GGUF --hf-file Eva-4B-F16.gguf -p "The meaning of life and the universe is"
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```
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or
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```bash
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./llama-server --hf-repo FutureMa/Eva-4B-GGUF --hf-file Eva-4B-F16.gguf -c 2048
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```
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## Notes
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- The `-c 2048` value is an example context size; adjust based on your needs and available memory.
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- 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.
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