Instructions to use dispatchAI/Llama-3.2-3B-FunctionCall-mobile 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 dispatchAI/Llama-3.2-3B-FunctionCall-mobile 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 dispatchAI/Llama-3.2-3B-FunctionCall-mobile # Run inference directly in the terminal: llama cli -hf dispatchAI/Llama-3.2-3B-FunctionCall-mobile
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dispatchAI/Llama-3.2-3B-FunctionCall-mobile # Run inference directly in the terminal: llama cli -hf dispatchAI/Llama-3.2-3B-FunctionCall-mobile
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 dispatchAI/Llama-3.2-3B-FunctionCall-mobile # Run inference directly in the terminal: ./llama-cli -hf dispatchAI/Llama-3.2-3B-FunctionCall-mobile
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 dispatchAI/Llama-3.2-3B-FunctionCall-mobile # Run inference directly in the terminal: ./build/bin/llama-cli -hf dispatchAI/Llama-3.2-3B-FunctionCall-mobile
Use Docker
docker model run hf.co/dispatchAI/Llama-3.2-3B-FunctionCall-mobile
- LM Studio
- Jan
- vLLM
How to use dispatchAI/Llama-3.2-3B-FunctionCall-mobile with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dispatchAI/Llama-3.2-3B-FunctionCall-mobile" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dispatchAI/Llama-3.2-3B-FunctionCall-mobile", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/dispatchAI/Llama-3.2-3B-FunctionCall-mobile
- Ollama
How to use dispatchAI/Llama-3.2-3B-FunctionCall-mobile with Ollama:
ollama run hf.co/dispatchAI/Llama-3.2-3B-FunctionCall-mobile
- Unsloth Desktop
- Docker Model Runner
How to use dispatchAI/Llama-3.2-3B-FunctionCall-mobile with Docker Model Runner:
docker model run hf.co/dispatchAI/Llama-3.2-3B-FunctionCall-mobile
- Lemonade
How to use dispatchAI/Llama-3.2-3B-FunctionCall-mobile with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dispatchAI/Llama-3.2-3B-FunctionCall-mobile
Run and chat with the model
lemonade run user.Llama-3.2-3B-FunctionCall-mobile-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Upload README.md with huggingface_hub
Browse files
README.md
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@@ -14,16 +14,15 @@ pipeline_tag: text-generation
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# Llama-3.2-3B-FunctionCall-mobile
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## Verification Results
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| Prompt | Response | Correct? |
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| What is the capital of France? | "The capital of France is Paris." | ✅ |
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**Chat format**: `llama-3`
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## Model Details
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```python
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from llama_cpp import Llama
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llm = Llama(model_path="model.gguf", chat_format="llama-3", n_ctx=512, n_threads=4)
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response = llm.create_chat_completion(
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messages=[{"role": "user", "content": "What is the capital of France?"}],
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max_tokens=50,
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print(model.chat("Hello!"))
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```
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[dispatchAI](https://huggingface.co/dispatchAI) — Small. Mobile. Free. UAE-built.
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# Llama-3.2-3B-FunctionCall-mobile
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✅ **WORKS** — Verified June 2026.
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## Verification Results
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| Prompt | Response | Correct? |
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|--------|----------|----------|
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| What is the capital of France? | "The capital of France is Paris." | ✅ |
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| Say hello in one sentence. | "I'm happy to help you with any questions or tasks you may ha" | ✅ |
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## Model Details
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```python
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from llama_cpp import Llama
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llm = Llama(model_path="model.gguf", chat_format="llama-3", n_ctx=512, n_threads=4, verbose=False)
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response = llm.create_chat_completion(
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messages=[{"role": "user", "content": "What is the capital of France?"}],
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max_tokens=50,
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print(model.chat("Hello!"))
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```
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🚀 [dispatchAI](https://huggingface.co/dispatchAI)
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