Instructions to use evalengine/unbound-e2b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use evalengine/unbound-e2b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="evalengine/unbound-e2b-gguf",
	filename="mmproj-unbound-e2b.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-e2b-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-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf evalengine/unbound-e2b-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-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf evalengine/unbound-e2b-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-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf evalengine/unbound-e2b-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-e2b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf evalengine/unbound-e2b-gguf:Q4_K_M

Use Docker

docker model run hf.co/evalengine/unbound-e2b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use evalengine/unbound-e2b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "evalengine/unbound-e2b-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-e2b-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-e2b-gguf:Q4_K_M

Ollama
How to use evalengine/unbound-e2b-gguf with Ollama:
```
ollama run hf.co/evalengine/unbound-e2b-gguf:Q4_K_M
```

Unsloth Studio new

How to use evalengine/unbound-e2b-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-e2b-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-e2b-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-e2b-gguf to start chatting

Pi new

How to use evalengine/unbound-e2b-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-e2b-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-e2b-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use evalengine/unbound-e2b-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-e2b-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-e2b-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use evalengine/unbound-e2b-gguf with Docker Model Runner:
```
docker model run hf.co/evalengine/unbound-e2b-gguf:Q4_K_M
```

Lemonade

How to use evalengine/unbound-e2b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull evalengine/unbound-e2b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.unbound-e2b-gguf-Q4_K_M

List all available models

lemonade list

johnsonchromia commited on 2 days ago

Commit

a472252

verified ·

1 Parent(s): e5bf496

README: rewrite paths for canonical flat layout

Browse files

Files changed (1) hide show

README.md +17 -18

README.md CHANGED Viewed

@@ -28,17 +28,17 @@ for Ollama, llama.cpp, LM Studio, and [wllama](https://github.com/ngxson/wllama)
 ## Available quants
-Each quant lives in its own folder; inside, the model is split into
-multi-part GGUFs (`*-00001-of-0000N.gguf`). All runtimes auto-stitch on the
-first part — same UX as a single file.
-| Quant   | Folder      | Parts | Total  | Browser (wllama) | Desktop | Notes |
-|---------|-------------|-------|--------|------------------|---------|-------|
-| Q2_K    | `Q2_K/`     | 3     | 2.8 GB | ✅                | ✅       | Smallest, biggest quality drop |
-| Q3_K_M  | `Q3_K_M/`   | 3     | 3.0 GB | ✅                | ✅       | Marginal size win over Q4 |
-| Q4_K_M  | `Q4_K_M/`   | 3     | 3.2 GB | ✅                | ✅       | **Recommended default** |
-| Q6_K    | `Q6_K/`     | 4     | 3.6 GB | ✅                | ✅       | Higher fidelity |
-| Q8_0    | `Q8_0/`     | 4     | 4.6 GB | ❌ (over 2 GB)    | ✅       | Highest fidelity; desktop only |
 `mmproj-unbound-e2b.gguf` (vision projector, ~942 MB) sits at the repo
 root — load it alongside any LM quant for image input. See **Vision** below.
@@ -50,12 +50,11 @@ root — load it alongside any LM quant for image input. See **Vision** below.
 - llama.cpp: pass `--jinja`. Gemma 4 thinking mode is on by default; set
   `enable_thinking: false` in chat-template kwargs for shorter replies.
-For Ollama specifically, pull from the **Ollama Registry** —
 `ollama pull hf.co/...` [doesn't yet support sharded GGUFs](https://github.com/ollama/ollama/issues/5245).
 The registry version is a single-file Q4_K_M with a bundled Modelfile
 (`temperature=0.6, top_p=0.95, top_k=64, repeat_penalty=1.05, num_ctx=8192`
-and an identity-grounding system prompt). Override per-session with
-`/set parameter temperature 0.3` etc.
 ## Run
@@ -66,8 +65,8 @@ ollama run  evalengine/unbound-e2b
 ```
 ```bash
-# llama.cpp — point at FIRST split part, the rest auto-stitch
-./llama-cli -m Q4_K_M/unbound-e2b-Q4_K_M-00001-of-00003.gguf -p "your prompt"
 ```
 ```js
@@ -76,7 +75,7 @@ import { Wllama } from '@wllama/wllama';
 const wllama = new Wllama(/* … */);
 await wllama.loadModelFromHF(
   'evalengine/unbound-e2b-GGUF',
-  'Q4_K_M/unbound-e2b-Q4_K_M-00001-of-00003.gguf'
 );
 ```
@@ -87,7 +86,7 @@ await wllama.loadModelFromHF(
 ```bash
 ./llama-mtmd-cli \
-  -m   Q4_K_M/unbound-e2b-Q4_K_M-00001-of-00003.gguf \
   --mmproj mmproj-unbound-e2b.gguf \
   --image path/to/your/image.png \
   -p "What is in this image?"

 ## Available quants
+Each quant is shipped as a sharded multi-part GGUF (`unbound-e2b.<QUANT>-NNNNN-of-NNNNN.gguf`).
+Ollama, llama.cpp, LM Studio, and wllama auto-stitch on the first part —
+same UX as a single file.
+| Quant   | Parts | Total  | Browser (wllama) | Desktop | Notes |
+|---------|-------|--------|------------------|---------|-------|
+| Q2_K    | 3     | 2.8 GB | ✅                | ✅       | Smallest, biggest quality drop |
+| Q3_K_M  | 3     | 3.0 GB | ✅                | ✅       | Marginal size win over Q4 |
+| Q4_K_M  | 3     | 3.2 GB | ✅                | ✅       | **Recommended default** |
+| Q6_K    | 4     | 3.6 GB | ✅                | ✅       | Higher fidelity |
+| Q8_0    | 4     | 4.6 GB | ❌ (over 2 GB)    | ✅       | Highest fidelity; desktop only |
 `mmproj-unbound-e2b.gguf` (vision projector, ~942 MB) sits at the repo
 root — load it alongside any LM quant for image input. See **Vision** below.
 - llama.cpp: pass `--jinja`. Gemma 4 thinking mode is on by default; set
   `enable_thinking: false` in chat-template kwargs for shorter replies.
+For Ollama, pull from the **Ollama Registry** —
 `ollama pull hf.co/...` [doesn't yet support sharded GGUFs](https://github.com/ollama/ollama/issues/5245).
 The registry version is a single-file Q4_K_M with a bundled Modelfile
 (`temperature=0.6, top_p=0.95, top_k=64, repeat_penalty=1.05, num_ctx=8192`
+and an identity-grounding system prompt).
 ## Run
 ```
 ```bash
+# llama.cpp — point at FIRST shard, the rest auto-stitch
+./llama-cli -m unbound-e2b.Q4_K_M-00001-of-00003.gguf -p "your prompt"
 ```
 ```js
 const wllama = new Wllama(/* … */);
 await wllama.loadModelFromHF(
   'evalengine/unbound-e2b-GGUF',
+  'unbound-e2b.Q4_K_M-00001-of-00003.gguf'
 );
 ```
 ```bash
 ./llama-mtmd-cli \
+  -m   unbound-e2b.Q4_K_M-00001-of-00003.gguf \
   --mmproj mmproj-unbound-e2b.gguf \
   --image path/to/your/image.png \
   -p "What is in this image?"