Instructions to use maddes8cht/harborwater-wizard-orca-3b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use maddes8cht/harborwater-wizard-orca-3b-gguf with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("maddes8cht/harborwater-wizard-orca-3b-gguf", dtype="auto") - llama-cpp-python
How to use maddes8cht/harborwater-wizard-orca-3b-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="maddes8cht/harborwater-wizard-orca-3b-gguf", filename="harborwater-wizard-orca-3b-Q4_0.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use maddes8cht/harborwater-wizard-orca-3b-gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf maddes8cht/harborwater-wizard-orca-3b-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf maddes8cht/harborwater-wizard-orca-3b-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 maddes8cht/harborwater-wizard-orca-3b-gguf:Q4_K_M # Run inference directly in the terminal: llama-cli -hf maddes8cht/harborwater-wizard-orca-3b-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 maddes8cht/harborwater-wizard-orca-3b-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf maddes8cht/harborwater-wizard-orca-3b-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 maddes8cht/harborwater-wizard-orca-3b-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf maddes8cht/harborwater-wizard-orca-3b-gguf:Q4_K_M
Use Docker
docker model run hf.co/maddes8cht/harborwater-wizard-orca-3b-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use maddes8cht/harborwater-wizard-orca-3b-gguf with Ollama:
ollama run hf.co/maddes8cht/harborwater-wizard-orca-3b-gguf:Q4_K_M
- Unsloth Studio
How to use maddes8cht/harborwater-wizard-orca-3b-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 maddes8cht/harborwater-wizard-orca-3b-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 maddes8cht/harborwater-wizard-orca-3b-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for maddes8cht/harborwater-wizard-orca-3b-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use maddes8cht/harborwater-wizard-orca-3b-gguf with Docker Model Runner:
docker model run hf.co/maddes8cht/harborwater-wizard-orca-3b-gguf:Q4_K_M
- Lemonade
How to use maddes8cht/harborwater-wizard-orca-3b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull maddes8cht/harborwater-wizard-orca-3b-gguf:Q4_K_M
Run and chat with the model
lemonade run user.harborwater-wizard-orca-3b-gguf-Q4_K_M
List all available models
lemonade list
Commit ·
cdea6d4
0
Parent(s):
Super-squash branch 'main' using huggingface_hub
Browse files
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README.md
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---
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license: apache-2.0
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datasets:
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- pankajmathur/WizardLM_Orca
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language:
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- en
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library_name: transformers
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---
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[]()
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I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information
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# wizard-orca-3b - GGUF
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- Model creator: [harborwater](https://huggingface.co/harborwater)
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- Original model: [wizard-orca-3b](https://huggingface.co/harborwater/wizard-orca-3b)
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OpenLlama is a free reimplementation of the original Llama Model which is licensed under Apache 2 license.
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# About GGUF format
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`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library.
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A growing list of Software is using it and can therefore use this model.
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The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov
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# Quantization variants
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There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:
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# Legacy quants
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Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types.
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Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
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## Note:
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Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not *real* K-quants. More details can be found in affected model descriptions.
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(This mainly refers to Falcon 7b and Starcoder models)
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# K-quants
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K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
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So, if possible, use K-quants.
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With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.
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---
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# Original Model Card:
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Trained on 2 epoch of pankajmathur's WizardLM_orca dataset.
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Prompt template:
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```
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### HUMAN:
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{prompt}
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### RESPONSE:
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<leave a newline for the model to answer>
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```
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
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Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_harborwater__wizard-orca-3b)
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| Metric | Value |
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|-----------------------|---------------------------|
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| Avg. | 35.93 |
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| ARC (25-shot) | 41.72 |
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| HellaSwag (10-shot) | 71.78 |
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| MMLU (5-shot) | 24.49 |
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| TruthfulQA (0-shot) | 40.04 |
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| Winogrande (5-shot) | 66.93 |
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| GSM8K (5-shot) | 1.06 |
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| DROP (3-shot) | 5.5 |
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***End of original Model File***
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---
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## Please consider to support my work
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**Coming Soon:** I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.
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<center>
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[](https://maddes8cht.github.io)
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[](https://stackexchange.com/users/26485911)
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[](https://github.com/maddes8cht)
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[](https://huggingface.co/maddes8cht)
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[](https://twitter.com/maddes1966)
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</center>
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