Instructions to use evalengine/unbound-e4b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use evalengine/unbound-e4b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="evalengine/unbound-e4b-gguf",
	filename="mmproj-unbound-e4b.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": [
				{
					"type": "text",
					"text": "Describe this image in one sentence."
				},
				{
					"type": "image_url",
					"image_url": {
						"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
					}
				}
			]
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use evalengine/unbound-e4b-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf evalengine/unbound-e4b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf evalengine/unbound-e4b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf evalengine/unbound-e4b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf evalengine/unbound-e4b-gguf:Q4_K_M

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 evalengine/unbound-e4b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf evalengine/unbound-e4b-gguf:Q4_K_M

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 evalengine/unbound-e4b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf evalengine/unbound-e4b-gguf:Q4_K_M

Use Docker

docker model run hf.co/evalengine/unbound-e4b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use evalengine/unbound-e4b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "evalengine/unbound-e4b-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": "evalengine/unbound-e4b-gguf",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/evalengine/unbound-e4b-gguf:Q4_K_M

Ollama
How to use evalengine/unbound-e4b-gguf with Ollama:
```
ollama run hf.co/evalengine/unbound-e4b-gguf:Q4_K_M
```

Unsloth Studio new

How to use evalengine/unbound-e4b-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 evalengine/unbound-e4b-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 evalengine/unbound-e4b-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for evalengine/unbound-e4b-gguf to start chatting

Pi new

How to use evalengine/unbound-e4b-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf evalengine/unbound-e4b-gguf:Q4_K_M

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": "evalengine/unbound-e4b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use evalengine/unbound-e4b-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 evalengine/unbound-e4b-gguf:Q4_K_M

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 evalengine/unbound-e4b-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use evalengine/unbound-e4b-gguf with Docker Model Runner:
```
docker model run hf.co/evalengine/unbound-e4b-gguf:Q4_K_M
```

Lemonade

How to use evalengine/unbound-e4b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull evalengine/unbound-e4b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.unbound-e4b-gguf-Q4_K_M

List all available models

lemonade list

johnsonchromia commited on 12 days ago

Commit

8e4ca58

verified ·

1 Parent(s): 40fbc30

README: add Q2_K + Q3_K_M to quant table

Browse files

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -37,9 +37,11 @@ Studio auto-stitch on the first part — same UX as a single file.
 | Quant   | Parts | Total   | Largest part | wllama (browser) | Desktop (Ollama / llama.cpp / LM Studio) | Notes |
 |---------|-------|---------|--------------|------------------|------------------------------------------|-------|
-| Q4_K_M  | 4     | 4.92 GB | ~2.15 GB     | ❌                | ✅                                        | Recommended on-device default — best size/quality |
-| Q6_K    | 5     | 5.73 GB | ~2.2 GB      | ❌                | ✅                                        | Higher fidelity |
-| Q8_0    | 6     | 7.41 GB | ~2.7 GB      | ❌                | ✅                                        | Highest fidelity |
 **Why no wllama:** E4B's `per_layer_token_embd` is a single atomic tensor
 that exceeds 2 GB in every quant we ship (the smallest one, Q4_K_M, lands

 | Quant   | Parts | Total   | Largest part | wllama (browser) | Desktop (Ollama / llama.cpp / LM Studio) | Notes |
 |---------|-------|---------|--------------|------------------|------------------------------------------|-------|
+| Q2_K    | 4     | 4.08 GB | ~2.15 GB     | ❌                | ✅                                        | Smallest disk footprint; biggest quality drop |
+| Q3_K_M  | 4     | 4.49 GB | ~2.15 GB     | ❌                | ✅                                        | Modest size win over Q4 (embedding precision dominates) |
+| Q4_K_M  | 4     | 4.94 GB | ~2.15 GB     | ❌                | ✅                                        | **Recommended on-device default — best size/quality** |
+| Q6_K    | 5     | 5.75 GB | ~2.2 GB      | ❌                | ✅                                        | Higher fidelity |
+| Q8_0    | 6     | 7.43 GB | ~2.7 GB      | ❌                | ✅                                        | Highest fidelity |
 **Why no wllama:** E4B's `per_layer_token_embd` is a single atomic tensor
 that exceeds 2 GB in every quant we ship (the smallest one, Q4_K_M, lands