Instructions to use navid72m/gemma-ai-books with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use navid72m/gemma-ai-books with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="navid72m/gemma-ai-books",
	filename="model.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use navid72m/gemma-ai-books with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf navid72m/gemma-ai-books
# Run inference directly in the terminal:
llama-cli -hf navid72m/gemma-ai-books

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf navid72m/gemma-ai-books
# Run inference directly in the terminal:
llama-cli -hf navid72m/gemma-ai-books

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 navid72m/gemma-ai-books
# Run inference directly in the terminal:
./llama-cli -hf navid72m/gemma-ai-books

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 navid72m/gemma-ai-books
# Run inference directly in the terminal:
./build/bin/llama-cli -hf navid72m/gemma-ai-books

Use Docker

docker model run hf.co/navid72m/gemma-ai-books

LM Studio
Jan
Ollama
How to use navid72m/gemma-ai-books with Ollama:
```
ollama run hf.co/navid72m/gemma-ai-books
```

Unsloth Studio

How to use navid72m/gemma-ai-books 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 navid72m/gemma-ai-books 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 navid72m/gemma-ai-books to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for navid72m/gemma-ai-books to start chatting

Docker Model Runner
How to use navid72m/gemma-ai-books with Docker Model Runner:
```
docker model run hf.co/navid72m/gemma-ai-books
```

Lemonade

How to use navid72m/gemma-ai-books with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull navid72m/gemma-ai-books

Run and chat with the model

lemonade run user.gemma-ai-books-{{QUANT_TAG}}

List all available models

lemonade list

Gemma 2 2B AI Books Instructor (QA Fine-tune)

Summary

This model is a fine-tuned version of google/gemma-2-2b trained on ~4,800 Q/A pairs distilled from 11 AI/LLM/agent engineering books. It is optimized for answering questions about AI engineering, LLMs, RAG, agents, and MLOps.

Base Model

google/gemma-2-2b

Training Data

~4,800 instruction-style Q/A pairs from 11 AI books (not released publicly)

Training Procedure

SFT (LoRA fine-tuning)
merged weights
converted to GGUF using llama.cpp
quantized (Q4_K_M, Q8_0)

Intended Use

AI/LLM learning assistant
fast local inference
RAG/agent system support

Limitations

May hallucinate
Not suited for medical/legal advice
Not guaranteed to reproduce book-accurate quotes

How to Run (llama.cpp)

./llama-cli -m model-Q4_K_M.gguf -p "Explain ReAct agents with an example."

Downloads last month: 18

GGUF

Model size

3B params

Architecture

gemma2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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Model tree for navid72m/gemma-ai-books

Base model

google/gemma-2-2b

Quantized

(64)

this model