Instructions to use QuantFactory/diffullama-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/diffullama-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/diffullama-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/diffullama-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/diffullama-GGUF", filename="diffullama.Q2_K.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use QuantFactory/diffullama-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/diffullama-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/diffullama-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 QuantFactory/diffullama-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/diffullama-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 QuantFactory/diffullama-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/diffullama-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 QuantFactory/diffullama-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/diffullama-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/diffullama-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/diffullama-GGUF with Ollama:
ollama run hf.co/QuantFactory/diffullama-GGUF:Q4_K_M
- Unsloth Studio new
How to use QuantFactory/diffullama-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 QuantFactory/diffullama-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 QuantFactory/diffullama-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/diffullama-GGUF to start chatting
- Docker Model Runner
How to use QuantFactory/diffullama-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/diffullama-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/diffullama-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/diffullama-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.diffullama-GGUF-Q4_K_M
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
ADDED
|
@@ -0,0 +1,59 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
|
| 2 |
+
---
|
| 3 |
+
|
| 4 |
+
library_name: transformers
|
| 5 |
+
base_model:
|
| 6 |
+
- meta-llama/Llama-2-7b-hf
|
| 7 |
+
tags:
|
| 8 |
+
- llama-factory
|
| 9 |
+
- full
|
| 10 |
+
- diffusion
|
| 11 |
+
model-index:
|
| 12 |
+
- name: diffullama
|
| 13 |
+
results: []
|
| 14 |
+
license: apache-2.0
|
| 15 |
+
datasets:
|
| 16 |
+
- bigcode/starcoderdata
|
| 17 |
+
- cerebras/SlimPajama-627B
|
| 18 |
+
|
| 19 |
+
---
|
| 20 |
+
|
| 21 |
+
[](https://hf.co/QuantFactory)
|
| 22 |
+
|
| 23 |
+
|
| 24 |
+
# QuantFactory/diffullama-GGUF
|
| 25 |
+
This is quantized version of [diffusionfamily/diffullama](https://huggingface.co/diffusionfamily/diffullama) created using llama.cpp
|
| 26 |
+
|
| 27 |
+
# Original Model Card
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
|
| 31 |
+
should probably proofread and complete it, then remove this comment. -->
|
| 32 |
+
|
| 33 |
+
# diffullama
|
| 34 |
+
|
| 35 |
+
This model is a fine-tuned version of [llama2].
|
| 36 |
+
|
| 37 |
+
## Model description
|
| 38 |
+
|
| 39 |
+
Details and model loading can be seen [https://github.com/HKUNLP/DiffuLLaMA](https://github.com/HKUNLP/DiffuLLaMA).
|
| 40 |
+
|
| 41 |
+
|
| 42 |
+
### Framework versions
|
| 43 |
+
|
| 44 |
+
- Transformers 4.44.2
|
| 45 |
+
- Pytorch 2.1.1+cu121
|
| 46 |
+
- Datasets 2.21.0
|
| 47 |
+
- Tokenizers 0.19.1
|
| 48 |
+
|
| 49 |
+
```
|
| 50 |
+
@misc{gong2024scalingdiffusionlanguagemodels,
|
| 51 |
+
title={Scaling Diffusion Language Models via Adaptation from Autoregressive Models},
|
| 52 |
+
author={Shansan Gong and Shivam Agarwal and Yizhe Zhang and Jiacheng Ye and Lin Zheng and Mukai Li and Chenxin An and Peilin Zhao and Wei Bi and Jiawei Han and Hao Peng and Lingpeng Kong},
|
| 53 |
+
year={2024},
|
| 54 |
+
eprint={2410.17891},
|
| 55 |
+
archivePrefix={arXiv},
|
| 56 |
+
primaryClass={cs.CL},
|
| 57 |
+
url={https://arxiv.org/abs/2410.17891},
|
| 58 |
+
}
|
| 59 |
+
```
|