gguf-quantization

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npx skills add firecrawl/ai-research-skills --skill gguf-quantization

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SKILL.md

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Documentation

SKILL.md

GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.

npx skills add firecrawl/ai-research-skills --skill gguf-quantization

Documentation

SKILL.mdView source

GGUF - Quantization Format for llama.cpp

The GGUF (GPT-Generated Unified Format) is the standard file format for llama.cpp, enabling efficient inference on CPUs, Apple Silicon, and GPUs with flexible quantization options.

When to use GGUF

Use GGUF when:

Deploying on consumer hardware (laptops, desktops)
Running on Apple Silicon (M1/M2/M3) with Metal acceleration
Need CPU inference without GPU requirements
Want flexible quantization (Q2_K to Q8_0)
Using local AI tools (LM Studio, Ollama, text-generation-webui)

Key advantages:

Universal hardware: CPU, Apple Silicon, NVIDIA, AMD support
No Python runtime: Pure C/C++ inference
Flexible quantization: 2-8 bit with various methods (K-quants)
Ecosystem support: LM Studio, Ollama, koboldcpp, and more
imatrix: Importance matrix for better low-bit quality

Use alternatives instead:

AWQ/GPTQ: Maximum accuracy with calibration on NVIDIA GPUs
HQQ: Fast calibration-free quantization for HuggingFace
bitsandbytes: Simple integration with transformers library
TensorRT-LLM: Production NVIDIA deployment with maximum speed

Quick start

Installation

# Clone llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
 
# Build (CPU)
make
 
# Build with CUDA (NVIDIA)
make GGML_CUDA=1
 
# Build with Metal (Apple Silicon)
make GGML_METAL=1
 
# Install Python bindings (optional)
pip install llama-cpp-python

Convert model to GGUF

# Install requirements
pip install -r requirements.txt
 
# Convert HuggingFace model to GGUF (FP16)
python convert_hf_to_gguf.py ./path/to/model --outfile model-f16.gguf
 
# Or specify output type
python convert_hf_to_gguf.py ./path/to/model \
    --outfile model-f16.gguf \
    --outtype f16

Quantize model

# Basic quantization to Q4_K_M
./llama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M
 
# Quantize with importance matrix (better quality)
./llama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix
./llama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M

Run inference

# CLI inference
./llama-cli -m model-q4_k_m.gguf -p "Hello, how are you?"
 
# Interactive mode
./llama-cli -m model-q4_k_m.gguf --interactive
 
# With GPU offload
./llama-cli -m model-q4_k_m.gguf -ngl 35 -p "Hello!"

Quantization types

K-quant methods (recommended)

Type	Bits	Size (7B)	Quality	Use Case
Q2_K	2.5	~2.8 GB	Low	Extreme compression
Q3_K_S	3.0	~3.0 GB	Low-Med	Memory constrained
Q3_K_M	3.3	~3.3 GB	Medium	Balance
Q4_K_S	4.0	~3.8 GB	Med-High	Good balance
Q4_K_M	4.5	~4.1 GB	High	Recommended default
Q5_K_S	5.0	~4.6 GB	High	Quality focused
Q5_K_M	5.5	~4.8 GB	Very High	High quality
Q6_K	6.0	~5.5 GB	Excellent	Near-original
Q8_0	8.0	~7.2 GB	Best	Maximum quality

Legacy methods

Type	Description
Q4_0	4-bit, basic
Q4_1	4-bit with delta
Q5_0	5-bit, basic
Q5_1	5-bit with delta

Recommendation: Use K-quant methods (Q4_K_M, Q5_K_M) for best quality/size ratio.

Conversion workflows

Workflow 1: HuggingFace to GGUF

# 1. Download model
huggingface-cli download meta-llama/Llama-3.1-8B --local-dir ./llama-3.1-8b
 
# 2. Convert to GGUF (FP16)
python convert_hf_to_gguf.py ./llama-3.1-8b \
    --outfile llama-3.1-8b-f16.gguf \
    --outtype f16
 
# 3. Quantize
./llama-quantize llama-3.1-8b-f16.gguf llama-3.1-8b-q4_k_m.gguf Q4_K_M
 
# 4. Test
./llama-cli -m llama-3.1-8b-q4_k_m.gguf -p "Hello!" -n 50

Workflow 2: With importance matrix (better quality)

# 1. Convert to GGUF
python convert_hf_to_gguf.py ./model --outfile model-f16.gguf
 
# 2. Create calibration text (diverse samples)
cat > calibration.txt << 'EOF'
The quick brown fox jumps over the lazy dog.
Machine learning is a subset of artificial intelligence.
Python is a popular programming language.
# Add more diverse text samples...
EOF
 
# 3. Generate importance matrix
./llama-imatrix -m model-f16.gguf \
    -f calibration.txt \
    --chunk 512 \
    -o model.imatrix \
    -ngl 35  # GPU layers if available
 
# 4. Quantize with imatrix
./llama-quantize --imatrix model.imatrix \
    model-f16.gguf \
    model-q4_k_m.gguf \
    Q4_K_M

Workflow 3: Multiple quantizations

#!/bin/bash
MODEL="llama-3.1-8b-f16.gguf"
IMATRIX="llama-3.1-8b.imatrix"
 
# Generate imatrix once
./llama-imatrix -m $MODEL -f wiki.txt -o $IMATRIX -ngl 35
 
# Create multiple quantizations
for QUANT in Q4_K_M Q5_K_M Q6_K Q8_0; do
    OUTPUT="llama-3.1-8b-${QUANT,,}.gguf"
    ./llama-quantize --imatrix $IMATRIX $MODEL $OUTPUT $QUANT
    echo "Created: $OUTPUT ($(du -h $OUTPUT | cut -f1))"
done

Python usage

llama-cpp-python

from llama_cpp import Llama
 
# Load model
llm = Llama(
    model_path="./model-q4_k_m.gguf",
    n_ctx=4096,          # Context window
    n_gpu_layers=35,     # GPU offload (0 for CPU only)
    n_threads=8          # CPU threads
)
 
# Generate
output = llm(
    "What is machine learning?",
    max_tokens=256,
    temperature=0.7,
    stop=["</s>", "\n\n"]
)
print(output["choices"][0]["text"])

Chat completion

from llama_cpp import Llama
 
llm = Llama(
    model_path="./model-q4_k_m.gguf",
    n_ctx=4096,
    n_gpu_layers=35,
    chat_format="llama-3"  # Or "chatml", "mistral", etc.
)
 
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is Python?"}
]
 
response = llm.create_chat_completion(
    messages=messages,
    max_tokens=256,
    temperature=0.7
)
print(response["choices"][0]["message"]["content"])

Streaming

from llama_cpp import Llama
 
llm = Llama(model_path="./model-q4_k_m.gguf", n_gpu_layers=35)
 
# Stream tokens
for chunk in llm(
    "Explain quantum computing:",
    max_tokens=256,
    stream=True
):
    print(chunk["choices"][0]["text"], end="", flush=True)

Server mode

Start OpenAI-compatible server

# Start server
./llama-server -m model-q4_k_m.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -ngl 35 \
    -c 4096
 
# Or with Python bindings
python -m llama_cpp.server \
    --model model-q4_k_m.gguf \
    --n_gpu_layers 35 \
    --host 0.0.0.0 \
    --port 8080

Use with OpenAI client

from openai import OpenAI
 
client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed"
)
 
response = client.chat.completions.create(
    model="local-model",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=256
)
print(response.choices[0].message.content)

Hardware optimization

Apple Silicon (Metal)

# Build with Metal
make clean && make GGML_METAL=1
 
# Run with Metal acceleration
./llama-cli -m model.gguf -ngl 99 -p "Hello"
 
# Python with Metal
llm = Llama(
    model_path="model.gguf",
    n_gpu_layers=99,     # Offload all layers
    n_threads=1          # Metal handles parallelism
)

NVIDIA CUDA

# Build with CUDA
make clean && make GGML_CUDA=1
 
# Run with CUDA
./llama-cli -m model.gguf -ngl 35 -p "Hello"
 
# Specify GPU
CUDA_VISIBLE_DEVICES=0 ./llama-cli -m model.gguf -ngl 35

CPU optimization

# Build with AVX2/AVX512
make clean && make
 
# Run with optimal threads
./llama-cli -m model.gguf -t 8 -p "Hello"
 
# Python CPU config
llm = Llama(
    model_path="model.gguf",
    n_gpu_layers=0,      # CPU only
    n_threads=8,         # Match physical cores
    n_batch=512          # Batch size for prompt processing
)

Integration with tools

Ollama

# Create Modelfile
cat > Modelfile << 'EOF'
FROM ./model-q4_k_m.gguf
TEMPLATE """{{ .System }}
{{ .Prompt }}"""
PARAMETER temperature 0.7
PARAMETER num_ctx 4096
EOF
 
# Create Ollama model
ollama create mymodel -f Modelfile
 
# Run
ollama run mymodel "Hello!"

LM Studio

Place GGUF file in ~/.cache/lm-studio/models/
Open LM Studio and select the model
Configure context length and GPU offload
Start inference

text-generation-webui

# Place in models folder
cp model-q4_k_m.gguf text-generation-webui/models/
 
# Start with llama.cpp loader
python server.py --model model-q4_k_m.gguf --loader llama.cpp --n-gpu-layers 35

Best practices

Use K-quants: Q4_K_M offers best quality/size balance
Use imatrix: Always use importance matrix for Q4 and below
GPU offload: Offload as many layers as VRAM allows
Context length: Start with 4096, increase if needed
Thread count: Match physical CPU cores, not logical
Batch size: Increase n_batch for faster prompt processing

Common issues

Model loads slowly:

# Use mmap for faster loading
./llama-cli -m model.gguf --mmap

Out of memory:

# Reduce GPU layers
./llama-cli -m model.gguf -ngl 20  # Reduce from 35
 
# Or use smaller quantization
./llama-quantize model-f16.gguf model-q3_k_m.gguf Q3_K_M

Poor quality at low bits:

# Always use imatrix for Q4 and below
./llama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix
./llama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M

References

Advanced Usage - Batching, speculative decoding, custom builds
Troubleshooting - Common issues, debugging, benchmarks

Resources

Repository: https://github.com/ggml-org/llama.cpp
Python Bindings: https://github.com/abetlen/llama-cpp-python
Pre-quantized Models: https://huggingface.co/TheBloke
GGUF Converter: https://huggingface.co/spaces/ggml-org/gguf-my-repo
License: MIT

npx skills add firecrawl/ai-research-skills --skill gguf-quantization

Documentation

SKILL.md

npx skills add firecrawl/ai-research-skills --skill gguf-quantization

Documentation

SKILL.mdView source

GGUF - Quantization Format for llama.cpp

The GGUF (GPT-Generated Unified Format) is the standard file format for llama.cpp, enabling efficient inference on CPUs, Apple Silicon, and GPUs with flexible quantization options.

When to use GGUF

Use GGUF when:

Deploying on consumer hardware (laptops, desktops)
Running on Apple Silicon (M1/M2/M3) with Metal acceleration
Need CPU inference without GPU requirements
Want flexible quantization (Q2_K to Q8_0)
Using local AI tools (LM Studio, Ollama, text-generation-webui)

Key advantages:

Universal hardware: CPU, Apple Silicon, NVIDIA, AMD support
No Python runtime: Pure C/C++ inference
Flexible quantization: 2-8 bit with various methods (K-quants)
Ecosystem support: LM Studio, Ollama, koboldcpp, and more
imatrix: Importance matrix for better low-bit quality

Use alternatives instead:

AWQ/GPTQ: Maximum accuracy with calibration on NVIDIA GPUs
HQQ: Fast calibration-free quantization for HuggingFace
bitsandbytes: Simple integration with transformers library
TensorRT-LLM: Production NVIDIA deployment with maximum speed

Quick start

Installation

# Clone llama.cpp
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp
 
# Build (CPU)
make
 
# Build with CUDA (NVIDIA)
make GGML_CUDA=1
 
# Build with Metal (Apple Silicon)
make GGML_METAL=1
 
# Install Python bindings (optional)
pip install llama-cpp-python

Convert model to GGUF

# Install requirements
pip install -r requirements.txt
 
# Convert HuggingFace model to GGUF (FP16)
python convert_hf_to_gguf.py ./path/to/model --outfile model-f16.gguf
 
# Or specify output type
python convert_hf_to_gguf.py ./path/to/model \
    --outfile model-f16.gguf \
    --outtype f16

Quantize model

# Basic quantization to Q4_K_M
./llama-quantize model-f16.gguf model-q4_k_m.gguf Q4_K_M
 
# Quantize with importance matrix (better quality)
./llama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix
./llama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M

Run inference

# CLI inference
./llama-cli -m model-q4_k_m.gguf -p "Hello, how are you?"
 
# Interactive mode
./llama-cli -m model-q4_k_m.gguf --interactive
 
# With GPU offload
./llama-cli -m model-q4_k_m.gguf -ngl 35 -p "Hello!"

Quantization types

K-quant methods (recommended)

Type	Bits	Size (7B)	Quality	Use Case
Q2_K	2.5	~2.8 GB	Low	Extreme compression
Q3_K_S	3.0	~3.0 GB	Low-Med	Memory constrained
Q3_K_M	3.3	~3.3 GB	Medium	Balance
Q4_K_S	4.0	~3.8 GB	Med-High	Good balance
Q4_K_M	4.5	~4.1 GB	High	Recommended default
Q5_K_S	5.0	~4.6 GB	High	Quality focused
Q5_K_M	5.5	~4.8 GB	Very High	High quality
Q6_K	6.0	~5.5 GB	Excellent	Near-original
Q8_0	8.0	~7.2 GB	Best	Maximum quality

Legacy methods

Type	Description
Q4_0	4-bit, basic
Q4_1	4-bit with delta
Q5_0	5-bit, basic
Q5_1	5-bit with delta

Recommendation: Use K-quant methods (Q4_K_M, Q5_K_M) for best quality/size ratio.

Conversion workflows

Workflow 1: HuggingFace to GGUF

# 1. Download model
huggingface-cli download meta-llama/Llama-3.1-8B --local-dir ./llama-3.1-8b
 
# 2. Convert to GGUF (FP16)
python convert_hf_to_gguf.py ./llama-3.1-8b \
    --outfile llama-3.1-8b-f16.gguf \
    --outtype f16
 
# 3. Quantize
./llama-quantize llama-3.1-8b-f16.gguf llama-3.1-8b-q4_k_m.gguf Q4_K_M
 
# 4. Test
./llama-cli -m llama-3.1-8b-q4_k_m.gguf -p "Hello!" -n 50

Workflow 2: With importance matrix (better quality)

# 1. Convert to GGUF
python convert_hf_to_gguf.py ./model --outfile model-f16.gguf
 
# 2. Create calibration text (diverse samples)
cat > calibration.txt << 'EOF'
The quick brown fox jumps over the lazy dog.
Machine learning is a subset of artificial intelligence.
Python is a popular programming language.
# Add more diverse text samples...
EOF
 
# 3. Generate importance matrix
./llama-imatrix -m model-f16.gguf \
    -f calibration.txt \
    --chunk 512 \
    -o model.imatrix \
    -ngl 35  # GPU layers if available
 
# 4. Quantize with imatrix
./llama-quantize --imatrix model.imatrix \
    model-f16.gguf \
    model-q4_k_m.gguf \
    Q4_K_M

Workflow 3: Multiple quantizations

#!/bin/bash
MODEL="llama-3.1-8b-f16.gguf"
IMATRIX="llama-3.1-8b.imatrix"
 
# Generate imatrix once
./llama-imatrix -m $MODEL -f wiki.txt -o $IMATRIX -ngl 35
 
# Create multiple quantizations
for QUANT in Q4_K_M Q5_K_M Q6_K Q8_0; do
    OUTPUT="llama-3.1-8b-${QUANT,,}.gguf"
    ./llama-quantize --imatrix $IMATRIX $MODEL $OUTPUT $QUANT
    echo "Created: $OUTPUT ($(du -h $OUTPUT | cut -f1))"
done

Python usage

llama-cpp-python

from llama_cpp import Llama
 
# Load model
llm = Llama(
    model_path="./model-q4_k_m.gguf",
    n_ctx=4096,          # Context window
    n_gpu_layers=35,     # GPU offload (0 for CPU only)
    n_threads=8          # CPU threads
)
 
# Generate
output = llm(
    "What is machine learning?",
    max_tokens=256,
    temperature=0.7,
    stop=["</s>", "\n\n"]
)
print(output["choices"][0]["text"])

Chat completion

from llama_cpp import Llama
 
llm = Llama(
    model_path="./model-q4_k_m.gguf",
    n_ctx=4096,
    n_gpu_layers=35,
    chat_format="llama-3"  # Or "chatml", "mistral", etc.
)
 
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is Python?"}
]
 
response = llm.create_chat_completion(
    messages=messages,
    max_tokens=256,
    temperature=0.7
)
print(response["choices"][0]["message"]["content"])

Streaming

from llama_cpp import Llama
 
llm = Llama(model_path="./model-q4_k_m.gguf", n_gpu_layers=35)
 
# Stream tokens
for chunk in llm(
    "Explain quantum computing:",
    max_tokens=256,
    stream=True
):
    print(chunk["choices"][0]["text"], end="", flush=True)

Server mode

Start OpenAI-compatible server

# Start server
./llama-server -m model-q4_k_m.gguf \
    --host 0.0.0.0 \
    --port 8080 \
    -ngl 35 \
    -c 4096
 
# Or with Python bindings
python -m llama_cpp.server \
    --model model-q4_k_m.gguf \
    --n_gpu_layers 35 \
    --host 0.0.0.0 \
    --port 8080

Use with OpenAI client

from openai import OpenAI
 
client = OpenAI(
    base_url="http://localhost:8080/v1",
    api_key="not-needed"
)
 
response = client.chat.completions.create(
    model="local-model",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=256
)
print(response.choices[0].message.content)

Hardware optimization

Apple Silicon (Metal)

# Build with Metal
make clean && make GGML_METAL=1
 
# Run with Metal acceleration
./llama-cli -m model.gguf -ngl 99 -p "Hello"
 
# Python with Metal
llm = Llama(
    model_path="model.gguf",
    n_gpu_layers=99,     # Offload all layers
    n_threads=1          # Metal handles parallelism
)

NVIDIA CUDA

# Build with CUDA
make clean && make GGML_CUDA=1
 
# Run with CUDA
./llama-cli -m model.gguf -ngl 35 -p "Hello"
 
# Specify GPU
CUDA_VISIBLE_DEVICES=0 ./llama-cli -m model.gguf -ngl 35

CPU optimization

# Build with AVX2/AVX512
make clean && make
 
# Run with optimal threads
./llama-cli -m model.gguf -t 8 -p "Hello"
 
# Python CPU config
llm = Llama(
    model_path="model.gguf",
    n_gpu_layers=0,      # CPU only
    n_threads=8,         # Match physical cores
    n_batch=512          # Batch size for prompt processing
)

Integration with tools

Ollama

# Create Modelfile
cat > Modelfile << 'EOF'
FROM ./model-q4_k_m.gguf
TEMPLATE """{{ .System }}
{{ .Prompt }}"""
PARAMETER temperature 0.7
PARAMETER num_ctx 4096
EOF
 
# Create Ollama model
ollama create mymodel -f Modelfile
 
# Run
ollama run mymodel "Hello!"

LM Studio

Place GGUF file in ~/.cache/lm-studio/models/
Open LM Studio and select the model
Configure context length and GPU offload
Start inference

text-generation-webui

# Place in models folder
cp model-q4_k_m.gguf text-generation-webui/models/
 
# Start with llama.cpp loader
python server.py --model model-q4_k_m.gguf --loader llama.cpp --n-gpu-layers 35

Best practices

Use K-quants: Q4_K_M offers best quality/size balance
Use imatrix: Always use importance matrix for Q4 and below
GPU offload: Offload as many layers as VRAM allows
Context length: Start with 4096, increase if needed
Thread count: Match physical CPU cores, not logical
Batch size: Increase n_batch for faster prompt processing

Common issues

Model loads slowly:

# Use mmap for faster loading
./llama-cli -m model.gguf --mmap

Out of memory:

# Reduce GPU layers
./llama-cli -m model.gguf -ngl 20  # Reduce from 35
 
# Or use smaller quantization
./llama-quantize model-f16.gguf model-q3_k_m.gguf Q3_K_M

Poor quality at low bits:

# Always use imatrix for Q4 and below
./llama-imatrix -m model-f16.gguf -f calibration.txt -o model.imatrix
./llama-quantize --imatrix model.imatrix model-f16.gguf model-q4_k_m.gguf Q4_K_M

References

Advanced Usage - Batching, speculative decoding, custom builds
Troubleshooting - Common issues, debugging, benchmarks

Resources

Repository: https://github.com/ggml-org/llama.cpp
Python Bindings: https://github.com/abetlen/llama-cpp-python
Pre-quantized Models: https://huggingface.co/TheBloke
GGUF Converter: https://huggingface.co/spaces/ggml-org/gguf-my-repo
License: MIT