Instructions to use automajicly/Local-Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use automajicly/Local-Model with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="automajicly/Local-Model", filename="qwen2.5-1.5b.q8.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use automajicly/Local-Model with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf automajicly/Local-Model # Run inference directly in the terminal: llama-cli -hf automajicly/Local-Model
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf automajicly/Local-Model # Run inference directly in the terminal: llama-cli -hf automajicly/Local-Model
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 automajicly/Local-Model # Run inference directly in the terminal: ./llama-cli -hf automajicly/Local-Model
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 automajicly/Local-Model # Run inference directly in the terminal: ./build/bin/llama-cli -hf automajicly/Local-Model
Use Docker
docker model run hf.co/automajicly/Local-Model
- LM Studio
- Jan
- vLLM
How to use automajicly/Local-Model with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "automajicly/Local-Model" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "automajicly/Local-Model", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/automajicly/Local-Model
- Ollama
How to use automajicly/Local-Model with Ollama:
ollama run hf.co/automajicly/Local-Model
- Unsloth Studio new
How to use automajicly/Local-Model 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 automajicly/Local-Model 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 automajicly/Local-Model to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for automajicly/Local-Model to start chatting
- Pi new
How to use automajicly/Local-Model with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf automajicly/Local-Model
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "automajicly/Local-Model" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use automajicly/Local-Model with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf automajicly/Local-Model
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default automajicly/Local-Model
Run Hermes
hermes
- Docker Model Runner
How to use automajicly/Local-Model with Docker Model Runner:
docker model run hf.co/automajicly/Local-Model
- Lemonade
How to use automajicly/Local-Model with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull automajicly/Local-Model
Run and chat with the model
lemonade run user.Local-Model-{{QUANT_TAG}}List all available models
lemonade list
Update README.md
Browse files# Qwen 2.5 1.5B GGUF Multi-Format Model
This repository contains multiple optimized formats of the Qwen 2.5 1.5B Instruct model for flexible deployment across desktop and mobile environments.
## Model Overview
This is a locally optimized and quantized model collection designed for private, offline, and mobile-friendly inference workflows.
All variants are derived from the same base model and differ only in quantization level and performance trade-offs.
---
## Variants
- **Q8 GGUF**
High-quality quantization for desktop inference. Best balance of performance and output quality.
- **Q4 GGUF**
Lightweight and fast quantization designed for low-resource devices and mobile use.
- **Safetensors (FP16/Transformers format)**
Original model format for standard Hugging Face / Transformers pipelines.
---
## Key Characteristics
- iOS / mobile compatible inference (via GGUF runtimes)
- Fully local and private execution (no external API dependency)
- Optimized quantized builds for speed and efficiency
- “Obliterated” style fine-tuning variant behavior for more open-ended responses
- Uncensored inference behavior depending on runtime configuration
---
## Usage Notes
Choose based on your environment:
- Mobile / iOS devices → Q4 GGUF
- Desktop / high-performance → Q8 GGUF
- ML workflows / training / transformers → Safetensors
---
## Base Model
Qwen 2.5 1.5B Instruct/safetensors
All quantized GGUF variants are derived from this base model.
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base_model:
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pipeline_tag: text-generation
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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pipeline_tag: text-generation
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---
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# Model Card for Model ID
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<!-- Provide a quick summary of what the model is/does. -->
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This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
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## Model Details
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### Model Description
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<!-- Provide a longer summary of what this model is. -->
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- **Developed by:** [More Information Needed]
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- **Funded by [optional]:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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[More Information Needed]
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**APA:**
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[More Information Needed]
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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