Transformers
GGUF
math
lora
science
chemistry
biology
code
text-generation-inference
unsloth
llama
conversational
Instructions to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("QuantFactory/FastLlama-3.2-1B-Instruct-GGUF", dtype="auto") - llama-cpp-python
How to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="QuantFactory/FastLlama-3.2-1B-Instruct-GGUF", filename="FastLlama-3.2-1B-Instruct.Q2_K.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf QuantFactory/FastLlama-3.2-1B-Instruct-GGUF: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 QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf QuantFactory/FastLlama-3.2-1B-Instruct-GGUF: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 QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF with Ollama:
ollama run hf.co/QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF 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 QuantFactory/FastLlama-3.2-1B-Instruct-GGUF 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 QuantFactory/FastLlama-3.2-1B-Instruct-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for QuantFactory/FastLlama-3.2-1B-Instruct-GGUF to start chatting
- Pi
How to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M
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": "QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M
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 QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M
- Lemonade
How to use QuantFactory/FastLlama-3.2-1B-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull QuantFactory/FastLlama-3.2-1B-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.FastLlama-3.2-1B-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
Upload README.md with huggingface_hub
Browse files
README.md
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---
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library_name: transformers
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tags:
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- math
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- lora
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- science
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- chemistry
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- biology
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- code
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- text-generation-inference
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- unsloth
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- llama
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license: apache-2.0
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datasets:
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- HuggingFaceTB/smoltalk
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language:
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- en
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- de
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- es
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- fr
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- it
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- pt
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- hi
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- th
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base_model:
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- meta-llama/Llama-3.2-1B-Instruct
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---
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[](https://hf.co/QuantFactory)
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# QuantFactory/FastLlama-3.2-1B-Instruct-GGUF
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This is quantized version of [suayptalha/FastLlama-3.2-1B-Instruct](https://huggingface.co/suayptalha/FastLlama-3.2-1B-Instruct) created using llama.cpp
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# Original Model Card
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You can use ChatML & Alpaca format.
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You can chat with the model via this [space](https://huggingface.co/spaces/suayptalha/Chat-with-FastLlama).
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**Overview:**
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FastLlama is a highly optimized version of the Llama-3.2-1B-Instruct model. Designed for superior performance in constrained environments, it combines speed, compactness, and high accuracy. This version has been fine-tuned using the MetaMathQA-50k section of the HuggingFaceTB/smoltalk dataset to enhance its mathematical reasoning and problem-solving abilities.
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**Features:**
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Lightweight and Fast: Optimized to deliver Llama-class capabilities with reduced computational overhead.
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Fine-Tuned for Math Reasoning: Utilizes MetaMathQA-50k for better handling of complex mathematical problems and logical reasoning tasks.
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Instruction-Tuned: Pre-trained on instruction-following tasks, making it robust in understanding and executing detailed queries.
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Versatile Use Cases: Suitable for educational tools, tutoring systems, or any application requiring mathematical reasoning.
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**Performance Highlights:**
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Smaller Footprint: The model delivers comparable results to larger counterparts while operating efficiently on smaller hardware.
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Enhanced Accuracy: Demonstrates improved performance on mathematical QA benchmarks.
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Instruction Adherence: Retains high fidelity in understanding and following user instructions, even for complex queries.
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**Loading the Model:**
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```py
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import torch
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from transformers import pipeline
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model_id = "suayptalha/FastLlama-3.2-1B-Instruct"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a friendly assistant named FastLlama."},
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{"role": "user", "content": "Who are you?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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```
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**Dataset:**
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Dataset: MetaMathQA-50k
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The MetaMathQA-50k subset of HuggingFaceTB/smoltalk was selected for fine-tuning due to its focus on mathematical reasoning, multi-step problem-solving, and logical inference. The dataset includes:
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Algebraic problems
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Geometric reasoning tasks
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Statistical and probabilistic questions
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Logical deduction problems
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**Model Fine-Tuning:**
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Fine-tuning was conducted using the following configuration:
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Learning Rate: 2e-4
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Epochs: 1
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Optimizer: AdamW
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Framework: Unsloth
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**License:**
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This model is licensed under the Apache 2.0 License. See the LICENSE file for details.
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[☕ Buy Me a Coffee](https://www.buymeacoffee.com/suayptalha)
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