Instructions to use mradermacher/WORLD_ARCHIVES_II-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/WORLD_ARCHIVES_II-GGUF with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/WORLD_ARCHIVES_II-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/WORLD_ARCHIVES_II-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 mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use mradermacher/WORLD_ARCHIVES_II-GGUF with Ollama:
ollama run hf.co/mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use mradermacher/WORLD_ARCHIVES_II-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/WORLD_ARCHIVES_II-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/WORLD_ARCHIVES_II-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.WORLD_ARCHIVES_II-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Download README.md from mradermacher/WORLD_ARCHIVES_II-GGUF: direct link, hf CLI and curl.
- Browser
- Download file 4.86 kB
-
https://huggingface.co/mradermacher/WORLD_ARCHIVES_II-GGUF/resolve/main/README.md
- Command line
-
hf download hf://mradermacher/WORLD_ARCHIVES_II-GGUF/README.md
-
curl -L -o README.md https://huggingface.co/mradermacher/WORLD_ARCHIVES_II-GGUF/resolve/main/README.md
base_model: LeroyDyer/WORLD_ARCHIVES_II
datasets:
- gretelai/synthetic_text_to_sql
- HuggingFaceTB/cosmopedia
- teknium/OpenHermes-2.5
- Open-Orca/SlimOrca
- Open-Orca/OpenOrca
- cognitivecomputations/dolphin-coder
- databricks/databricks-dolly-15k
- yahma/alpaca-cleaned
- uonlp/CulturaX
- mwitiderrick/SwahiliPlatypus
- swahili
- Rogendo/English-Swahili-Sentence-Pairs
- ise-uiuc/Magicoder-Evol-Instruct-110K
- meta-math/MetaMathQA
- abacusai/ARC_DPO_FewShot
- abacusai/MetaMath_DPO_FewShot
- abacusai/HellaSwag_DPO_FewShot
- HaltiaAI/Her-The-Movie-Samantha-and-Theodore-Dataset
- HuggingFaceFW/fineweb
- occiglot/occiglot-fineweb-v0.5
- omi-health/medical-dialogue-to-soap-summary
- keivalya/MedQuad-MedicalQnADataset
- ruslanmv/ai-medical-dataset
- Shekswess/medical_llama3_instruct_dataset_short
- ShenRuililin/MedicalQnA
- virattt/financial-qa-10K
- PatronusAI/financebench
- takala/financial_phrasebank
- Replete-AI/code_bagel
- athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW
- IlyaGusev/gpt_roleplay_realm
- rickRossie/bluemoon_roleplay_chat_data_300k_messages
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
About
static quants of https://huggingface.co/LeroyDyer/WORLD_ARCHIVES_II
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
Usage
If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.
Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|---|---|---|---|
| GGUF | Q2_K | 2.8 | |
| GGUF | IQ3_XS | 3.1 | |
| GGUF | Q3_K_S | 3.3 | |
| GGUF | IQ3_S | 3.3 | beats Q3_K* |
| GGUF | IQ3_M | 3.4 | |
| GGUF | Q3_K_M | 3.6 | lower quality |
| GGUF | Q3_K_L | 3.9 | |
| GGUF | IQ4_XS | 4.0 | |
| GGUF | Q4_K_S | 4.2 | fast, recommended |
| GGUF | Q4_K_M | 4.5 | fast, recommended |
| GGUF | Q5_K_S | 5.1 | |
| GGUF | Q5_K_M | 5.2 | |
| GGUF | Q6_K | 6.0 | very good quality |
| GGUF | Q8_0 | 7.8 | fast, best quality |
| GGUF | f16 | 14.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):
And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.
Thanks
I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.
