Instructions to use shawnha/nexus-scheduler with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shawnha/nexus-scheduler with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="shawnha/nexus-scheduler",
	filename="scheduler-unsloth.Q4_K_M.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 shawnha/nexus-scheduler with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shawnha/nexus-scheduler:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf shawnha/nexus-scheduler:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shawnha/nexus-scheduler:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf shawnha/nexus-scheduler: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 shawnha/nexus-scheduler:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf shawnha/nexus-scheduler: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 shawnha/nexus-scheduler:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shawnha/nexus-scheduler:Q4_K_M

Use Docker

docker model run hf.co/shawnha/nexus-scheduler:Q4_K_M

LM Studio
Jan
Ollama
How to use shawnha/nexus-scheduler with Ollama:
```
ollama run hf.co/shawnha/nexus-scheduler:Q4_K_M
```

Unsloth Studio new

How to use shawnha/nexus-scheduler 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 shawnha/nexus-scheduler 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 shawnha/nexus-scheduler to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shawnha/nexus-scheduler to start chatting

Docker Model Runner
How to use shawnha/nexus-scheduler with Docker Model Runner:
```
docker model run hf.co/shawnha/nexus-scheduler:Q4_K_M
```

Lemonade

How to use shawnha/nexus-scheduler with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shawnha/nexus-scheduler:Q4_K_M

Run and chat with the model

lemonade run user.nexus-scheduler-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Model Card for nexus-scheduler

scheduler model for NEXUS

Model Card for nexus-scheduler
Table of Contents
Table of Contents
Model Details
- Model Description
Uses
Bias, Risks, and Limitations
- Recommendations
Training Details
- Training Data
- Training Procedure
  - Preprocessing
  - Speeds, Sizes, Times
Evaluation
- Testing Data, Factors & Metrics
- Results
Model Examination
Environmental Impact
Technical Specifications [optional]
- Model Architecture and Objective
- Compute Infrastructure
  - Hardware
  - Software
Citation
Glossary [optional]
More Information [optional]
Model Card Authors [optional]
Model Card Contact
How to Get Started with the Model

Model Details

Model Description

scheduler model for NEXUS

Developed by: More information needed
Shared by [Optional]: More information needed
Model type: Language model
Language(s) (NLP): en
License: mit
Parent Model: More information needed
Resources for more information: More information needed
- GitHub Repo
- Associated Paper

Uses

Direct Use

Downstream Use [Optional]

Out-of-Scope Use

Bias, Risks, and Limitations

Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.

Recommendations

Training Details

Training Data

More information on training data needed

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

More information needed

Factors

More information needed

Metrics

More information needed

Results

More information needed

Model Examination

More information needed

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Hardware Type: More information needed
Hours used: More information needed
Cloud Provider: More information needed
Compute Region: More information needed
Carbon Emitted: More information needed

Technical Specifications [optional]

Model Architecture and Objective

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed

Citation

BibTeX:

More information needed

APA:

More information needed

Glossary [optional]

More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

Shawn Hagler, Jaehyun Lee

Model Card Contact

More information needed

How to Get Started with the Model

Use the code below to get started with the model.

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

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Paper for shawnha/nexus-scheduler

Quantifying the Carbon Emissions of Machine Learning

Paper • 1910.09700 • Published Oct 21, 2019 • 45

Model Card for nexus-scheduler

Table of Contents

Model Details

Model Description

Uses

Direct Use

Downstream Use [Optional]

Out-of-Scope Use

Bias, Risks, and Limitations

Recommendations

Training Details

Training Data

Training Procedure

Preprocessing

Speeds, Sizes, Times

Evaluation

Testing Data, Factors & Metrics

Testing Data

Factors

Metrics

Results

Model Examination

Environmental Impact

Technical Specifications [optional]

Model Architecture and Objective

Compute Infrastructure

Hardware

Software

Citation

Glossary [optional]

More Information [optional]

Model Card Authors [optional]

Model Card Contact

How to Get Started with the Model

Paper for shawnha/nexus-scheduler