Instructions to use v000000/YamWizard28-7B-Q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use v000000/YamWizard28-7B-Q8_0-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("v000000/YamWizard28-7B-Q8_0-GGUF", dtype="auto") - llama-cpp-python
How to use v000000/YamWizard28-7B-Q8_0-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="v000000/YamWizard28-7B-Q8_0-GGUF", filename="yamwizard28-7b-abliterated-q8_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 v000000/YamWizard28-7B-Q8_0-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama-cli -hf v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0
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 v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0
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 v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use v000000/YamWizard28-7B-Q8_0-GGUF with Ollama:
ollama run hf.co/v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0
- Unsloth Studio
How to use v000000/YamWizard28-7B-Q8_0-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 v000000/YamWizard28-7B-Q8_0-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 v000000/YamWizard28-7B-Q8_0-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for v000000/YamWizard28-7B-Q8_0-GGUF to start chatting
- Docker Model Runner
How to use v000000/YamWizard28-7B-Q8_0-GGUF with Docker Model Runner:
docker model run hf.co/v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0
- Lemonade
How to use v000000/YamWizard28-7B-Q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull v000000/YamWizard28-7B-Q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.YamWizard28-7B-Q8_0-GGUF-Q8_0
List all available models
lemonade list
v000000/YamWizard28-7B-Q8_0-GGUF
This model was converted to GGUF format from v000000/YamWizard28-7B using llama.cpp
Refer to the original model card for more details on the model.'
YamWizard28-7B
idk
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the SLERP merge method.
Models Merged
The following models were included in the merge:
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- model: fearlessdots/WizardLM-2-7B-abliterated
layer_range: [0, 32]
- model: automerger/YamshadowExperiment28-7B
layer_range: [0, 32]
merge_method: slerp
base_model: fearlessdots/WizardLM-2-7B-abliterated
parameters:
t:
- filter: self_attn
value: [0.1, 0.6, 0.3, 0.8, 0.5]
- filter: mlp
value: [0.9, 0.4, 0.7, 0.2, 0.5]
- value: 0.5
dtype: bfloat16
Prompt Format (Alpaca):
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system}
### Instruction:
{prompt}
### Response:
{output}
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v000000/YamWizard28-7B