Feature Extraction
GGUF
English
gemma3_text
embedding
retrieval
electrical-engineering
unsloth
llama-cpp
information-retrieval
rag
semantic-search
arxiv:2509.20354
Instructions to use disham993/electrical-embeddinggemma-ir_q8_0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use disham993/electrical-embeddinggemma-ir_q8_0 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 disham993/electrical-embeddinggemma-ir_q8_0:Q8_0 # Run inference directly in the terminal: llama cli -hf disham993/electrical-embeddinggemma-ir_q8_0:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf disham993/electrical-embeddinggemma-ir_q8_0:Q8_0 # Run inference directly in the terminal: llama cli -hf disham993/electrical-embeddinggemma-ir_q8_0: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 disham993/electrical-embeddinggemma-ir_q8_0:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf disham993/electrical-embeddinggemma-ir_q8_0: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 disham993/electrical-embeddinggemma-ir_q8_0:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf disham993/electrical-embeddinggemma-ir_q8_0:Q8_0
Use Docker
docker model run hf.co/disham993/electrical-embeddinggemma-ir_q8_0:Q8_0
- LM Studio
- Jan
- Ollama
How to use disham993/electrical-embeddinggemma-ir_q8_0 with Ollama:
ollama run hf.co/disham993/electrical-embeddinggemma-ir_q8_0:Q8_0
- Unsloth Desktop
- Docker Model Runner
How to use disham993/electrical-embeddinggemma-ir_q8_0 with Docker Model Runner:
docker model run hf.co/disham993/electrical-embeddinggemma-ir_q8_0:Q8_0
- Lemonade
How to use disham993/electrical-embeddinggemma-ir_q8_0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull disham993/electrical-embeddinggemma-ir_q8_0:Q8_0
Run and chat with the model
lemonade run user.electrical-embeddinggemma-ir_q8_0-Q8_0
List all available models
lemonade list
- Atomic Chat
Add LM Studio (OpenAI-compatible API) usage section
Browse files
README.md
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@@ -84,6 +84,32 @@ Evaluated on the held-out test split (2,000 queries) of `disham993/ElectricalEle
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## Usage
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```bash
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# Install dependencies
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pip install huggingface_hub
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## Usage
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### LM Studio (OpenAI-compatible API)
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Load this model in [LM Studio](https://lmstudio.ai) and use it via the built-in OpenAI-compatible server:
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```python
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from openai import OpenAI
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client = OpenAI(base_url="http://127.0.0.1:1234/v1", api_key="lm-studio")
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texts = [
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"What is impedance matching?",
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"Impedance matching maximises power transfer by equalising source and load impedance.",
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"An LLC resonant converter achieves zero-voltage switching using an LC tank circuit.",
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]
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response = client.embeddings.create(
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model="text-embedding-electrical-embeddinggemma-ir",
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input=texts,
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)
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for item in response.data:
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print(f"[{item.index}] dim={len(item.embedding)} first5={item.embedding[:5]}")
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```
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### llama-cpp-python
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```bash
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# Install dependencies
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pip install huggingface_hub
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