Instructions to use tensorblock/DISLab_Ext2Gen-8B-R2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tensorblock/DISLab_Ext2Gen-8B-R2-GGUF with Transformers:

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

pipe = pipeline("question-answering", model="tensorblock/DISLab_Ext2Gen-8B-R2-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tensorblock/DISLab_Ext2Gen-8B-R2-GGUF", dtype="auto")

llama-cpp-python

How to use tensorblock/DISLab_Ext2Gen-8B-R2-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="tensorblock/DISLab_Ext2Gen-8B-R2-GGUF",
	filename="Ext2Gen-8B-R2-Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "{\n    \"question\": \"What is my name?\",\n    \"context\": \"My name is Clara and I live in Berkeley.\"\n}"
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use tensorblock/DISLab_Ext2Gen-8B-R2-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K
# Run inference directly in the terminal:
llama-cli -hf tensorblock/DISLab_Ext2Gen-8B-R2-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 tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf tensorblock/DISLab_Ext2Gen-8B-R2-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 tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K

Use Docker

docker model run hf.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use tensorblock/DISLab_Ext2Gen-8B-R2-GGUF with Ollama:
```
ollama run hf.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K
```

Unsloth Studio new

How to use tensorblock/DISLab_Ext2Gen-8B-R2-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 tensorblock/DISLab_Ext2Gen-8B-R2-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 tensorblock/DISLab_Ext2Gen-8B-R2-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for tensorblock/DISLab_Ext2Gen-8B-R2-GGUF to start chatting

Pi new

How to use tensorblock/DISLab_Ext2Gen-8B-R2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K

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": "tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use tensorblock/DISLab_Ext2Gen-8B-R2-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 tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K

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 tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K

Run Hermes

hermes

Docker Model Runner
How to use tensorblock/DISLab_Ext2Gen-8B-R2-GGUF with Docker Model Runner:
```
docker model run hf.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K
```

Lemonade

How to use tensorblock/DISLab_Ext2Gen-8B-R2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tensorblock/DISLab_Ext2Gen-8B-R2-GGUF:Q2_K

Run and chat with the model

lemonade run user.DISLab_Ext2Gen-8B-R2-GGUF-Q2_K

List all available models

lemonade list

DISLab_Ext2Gen-8B-R2-GGUF / README.md

morriszms

Update README.md

1822ee2 verified 11 months ago

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	---
	base_model: DISLab/Ext2Gen-8B-R2
	language:
	- en
	license: apache-2.0
	pipeline_tag: question-answering
	tags:
	- rag
	- TensorBlock
	- GGUF
	library_name: transformers
	---

	<div style="width: auto; margin-left: auto; margin-right: auto">
	<img src="https://i.imgur.com/jC7kdl8.jpeg" alt="TensorBlock" style="width: 100%; min-width: 400px; display: block; margin: auto;">
	</div>

	[![Website](https://img.shields.io/badge/Website-tensorblock.co-blue?logo=google-chrome&logoColor=white)](https://tensorblock.co)
	[![Twitter](https://img.shields.io/twitter/follow/tensorblock_aoi?style=social)](https://twitter.com/tensorblock_aoi)
	[![Discord](https://img.shields.io/badge/Discord-Join%20Us-5865F2?logo=discord&logoColor=white)](https://discord.gg/Ej5NmeHFf2)
	[![GitHub](https://img.shields.io/badge/GitHub-TensorBlock-black?logo=github&logoColor=white)](https://github.com/TensorBlock)
	[![Telegram](https://img.shields.io/badge/Telegram-Group-blue?logo=telegram)](https://t.me/TensorBlock)


	## DISLab/Ext2Gen-8B-R2 - GGUF

	This repo contains GGUF format model files for [DISLab/Ext2Gen-8B-R2](https://huggingface.co/DISLab/Ext2Gen-8B-R2).

	The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b5165](https://github.com/ggml-org/llama.cpp/commit/1d735c0b4fa0551c51c2f4ac888dd9a01f447985).

	## Our projects
	<table border="1" cellspacing="0" cellpadding="10">
	<tr>
	<th colspan="2" style="font-size: 25px;">Forge</th>
	</tr>
	<tr>
	<th colspan="2">
	<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
	</th>
	</tr>
	<tr>
	<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
	</tr>
	<tr>
	<th colspan="2">
	<a href="https://github.com/TensorBlock/forge" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">🚀 Try it now! 🚀</a>
	</th>
	</tr>

	<tr>
	<th style="font-size: 25px;">Awesome MCP Servers</th>
	<th style="font-size: 25px;">TensorBlock Studio</th>
	</tr>
	<tr>
	<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
	<th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Studio" width="450"/></th>
	</tr>
	<tr>
	<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
	<th>A lightweight, open, and extensible multi-LLM interaction studio.</th>
	</tr>
	<tr>
	<th>
	<a href="https://github.com/TensorBlock/awesome-mcp-servers" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	<th>
	<a href="https://github.com/TensorBlock/TensorBlock-Studio" target="_blank" style="
	display: inline-block;
	padding: 8px 16px;
	background-color: #FF7F50;
	color: white;
	text-decoration: none;
	border-radius: 6px;
	font-weight: bold;
	font-family: sans-serif;
	">👀 See what we built 👀</a>
	</th>
	</tr>
	</table>

	## Prompt template

	```
	<\|begin_of_text\|><\|start_header_id\|>system<\|end_header_id\|>

	Cutting Knowledge Date: December 2023
	Today Date: 26 Jul 2024

	{system_prompt}<\|eot_id\|><\|start_header_id\|>user<\|end_header_id\|>

	{prompt}<\|eot_id\|><\|start_header_id\|>assistant<\|end_header_id\|>
	```

	## Model file specification

	\| Filename \| Quant type \| File Size \| Description \|
	\| -------- \| ---------- \| --------- \| ----------- \|
	\| [Ext2Gen-8B-R2-Q2_K.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q2_K.gguf) \| Q2_K \| 3.179 GB \| smallest, significant quality loss - not recommended for most purposes \|
	\| [Ext2Gen-8B-R2-Q3_K_S.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q3_K_S.gguf) \| Q3_K_S \| 3.665 GB \| very small, high quality loss \|
	\| [Ext2Gen-8B-R2-Q3_K_M.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q3_K_M.gguf) \| Q3_K_M \| 4.019 GB \| very small, high quality loss \|
	\| [Ext2Gen-8B-R2-Q3_K_L.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q3_K_L.gguf) \| Q3_K_L \| 4.322 GB \| small, substantial quality loss \|
	\| [Ext2Gen-8B-R2-Q4_0.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q4_0.gguf) \| Q4_0 \| 4.661 GB \| legacy; small, very high quality loss - prefer using Q3_K_M \|
	\| [Ext2Gen-8B-R2-Q4_K_S.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q4_K_S.gguf) \| Q4_K_S \| 4.693 GB \| small, greater quality loss \|
	\| [Ext2Gen-8B-R2-Q4_K_M.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q4_K_M.gguf) \| Q4_K_M \| 4.921 GB \| medium, balanced quality - recommended \|
	\| [Ext2Gen-8B-R2-Q5_0.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q5_0.gguf) \| Q5_0 \| 5.599 GB \| legacy; medium, balanced quality - prefer using Q4_K_M \|
	\| [Ext2Gen-8B-R2-Q5_K_S.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q5_K_S.gguf) \| Q5_K_S \| 5.599 GB \| large, low quality loss - recommended \|
	\| [Ext2Gen-8B-R2-Q5_K_M.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q5_K_M.gguf) \| Q5_K_M \| 5.733 GB \| large, very low quality loss - recommended \|
	\| [Ext2Gen-8B-R2-Q6_K.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q6_K.gguf) \| Q6_K \| 6.596 GB \| very large, extremely low quality loss \|
	\| [Ext2Gen-8B-R2-Q8_0.gguf](https://huggingface.co/tensorblock/DISLab_Ext2Gen-8B-R2-GGUF/blob/main/Ext2Gen-8B-R2-Q8_0.gguf) \| Q8_0 \| 8.541 GB \| very large, extremely low quality loss - not recommended \|


	## Downloading instruction

	### Command line

	Firstly, install Huggingface Client

	```shell
	pip install -U "huggingface_hub[cli]"
	```

	Then, downoad the individual model file the a local directory

	```shell
	huggingface-cli download tensorblock/DISLab_Ext2Gen-8B-R2-GGUF --include "Ext2Gen-8B-R2-Q2_K.gguf" --local-dir MY_LOCAL_DIR
	```

	If you wanna download multiple model files with a pattern (e.g., `Q4_Kgguf`), you can try:

	```shell
	huggingface-cli download tensorblock/DISLab_Ext2Gen-8B-R2-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='Q4_Kgguf'
	```