Instructions to use tensorblock/DISLab_Ext2Gen-8B-R2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
File size: 6,725 Bytes
fcc73de ed74f07 fcc73de 1822ee2 fcc73de 1822ee2 fcc73de | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 | ---
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>
[](https://tensorblock.co)
[](https://twitter.com/tensorblock_aoi)
[](https://discord.gg/Ej5NmeHFf2)
[](https://github.com/TensorBlock)
[](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">
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<th colspan="2" style="font-size: 25px;">Forge</th>
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<img src="https://imgur.com/faI5UKh.jpeg" alt="Forge Project" width="900"/>
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<th colspan="2">An OpenAI-compatible multi-provider routing layer.</th>
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<a href="https://github.com/TensorBlock/forge" target="_blank" style="
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">馃殌 Try it now! 馃殌</a>
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<th style="font-size: 25px;">Awesome MCP Servers</th>
<th style="font-size: 25px;">TensorBlock Studio</th>
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<th><img src="https://imgur.com/2Xov7B7.jpeg" alt="MCP Servers" width="450"/></th>
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<th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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font-weight: bold;
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">馃憖 See what we built 馃憖</a>
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</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_K*gguf`), 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_K*gguf'
```
|