Instructions to use FINAL-Bench/POCKET-26B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FINAL-Bench/POCKET-26B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="FINAL-Bench/POCKET-26B-GGUF",
	filename="POCKET-26B-Q2_K.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 FINAL-Bench/POCKET-26B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K

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 FINAL-Bench/POCKET-26B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K

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 FINAL-Bench/POCKET-26B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FINAL-Bench/POCKET-26B-GGUF:Q2_K

Use Docker

docker model run hf.co/FINAL-Bench/POCKET-26B-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use FINAL-Bench/POCKET-26B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FINAL-Bench/POCKET-26B-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": "FINAL-Bench/POCKET-26B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FINAL-Bench/POCKET-26B-GGUF:Q2_K

Ollama
How to use FINAL-Bench/POCKET-26B-GGUF with Ollama:
```
ollama run hf.co/FINAL-Bench/POCKET-26B-GGUF:Q2_K
```

Unsloth Studio

How to use FINAL-Bench/POCKET-26B-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 FINAL-Bench/POCKET-26B-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 FINAL-Bench/POCKET-26B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for FINAL-Bench/POCKET-26B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use FINAL-Bench/POCKET-26B-GGUF with Docker Model Runner:
```
docker model run hf.co/FINAL-Bench/POCKET-26B-GGUF:Q2_K
```

Lemonade

How to use FINAL-Bench/POCKET-26B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FINAL-Bench/POCKET-26B-GGUF:Q2_K

Run and chat with the model

lemonade run user.POCKET-26B-GGUF-Q2_K

List all available models

lemonade list

SeaWolf-AI commited on 4 days ago

Commit

9e5ad28

verified ·

1 Parent(s): 550b2ec

POCKET family cross-links + Image/Studio/Zimage

Browse files

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README.md +2 -1

README.md CHANGED Viewed

@@ -104,9 +104,10 @@ Apache-2.0 — use, modify, redistribute freely.
 - 🇰🇷 [POCKET-KR-GGUF](https://huggingface.co/FINAL-Bench/POCKET-KR-GGUF) — Korean, Android
 - 🍎 [POCKET-KR-MLX](https://huggingface.co/FINAL-Bench/POCKET-KR-MLX) — Korean, iPhone / Mac
 - 🌍 [POCKET-EN-GGUF](https://huggingface.co/FINAL-Bench/POCKET-EN-GGUF) — English, phone / PC
 **Demos & tools (Spaces)**
-- 🖼️ [POCKET-Image](https://huggingface.co/spaces/FINAL-Bench/POCKET-Image) — character-perfect Korean text in images
 - 🖥️ [POCKET-35B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-35B-CPU) — 35B answering on a CPU
 - 🖥️ [POCKET-26B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-26B-CPU) — 26B on a CPU

 - 🇰🇷 [POCKET-KR-GGUF](https://huggingface.co/FINAL-Bench/POCKET-KR-GGUF) — Korean, Android
 - 🍎 [POCKET-KR-MLX](https://huggingface.co/FINAL-Bench/POCKET-KR-MLX) — Korean, iPhone / Mac
 - 🌍 [POCKET-EN-GGUF](https://huggingface.co/FINAL-Bench/POCKET-EN-GGUF) — English, phone / PC
+- 🖼️ [POCKET-Image-Zimage](https://huggingface.co/FINAL-Bench/POCKET-Image-Zimage) — character-perfect text in any image
 **Demos & tools (Spaces)**
+- 🎨 [POCKET-Image Studio](https://huggingface.co/spaces/FINAL-Bench/POCKET-Image-Studio) — text-in-image, generate in-page
 - 🖥️ [POCKET-35B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-35B-CPU) — 35B answering on a CPU
 - 🖥️ [POCKET-26B-CPU](https://huggingface.co/spaces/FINAL-Bench/POCKET-26B-CPU) — 26B on a CPU