Instructions to use QuantFactory/LLaMA-O1-Base-1127-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/LLaMA-O1-Base-1127-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/LLaMA-O1-Base-1127-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/LLaMA-O1-Base-1127-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/LLaMA-O1-Base-1127-GGUF",
	filename="LLaMA-O1-Base-1127.Q2_K.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 QuantFactory/LLaMA-O1-Base-1127-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/LLaMA-O1-Base-1127-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/LLaMA-O1-Base-1127-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/LLaMA-O1-Base-1127-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/LLaMA-O1-Base-1127-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/LLaMA-O1-Base-1127-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/LLaMA-O1-Base-1127-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/LLaMA-O1-Base-1127-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/LLaMA-O1-Base-1127-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/LLaMA-O1-Base-1127-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/LLaMA-O1-Base-1127-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/LLaMA-O1-Base-1127-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/LLaMA-O1-Base-1127-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/LLaMA-O1-Base-1127-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/LLaMA-O1-Base-1127-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/LLaMA-O1-Base-1127-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/LLaMA-O1-Base-1127-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/LLaMA-O1-Base-1127-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/LLaMA-O1-Base-1127-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/LLaMA-O1-Base-1127-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.LLaMA-O1-Base-1127-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/LLaMA-O1-Base-1127-GGUF

This is quantized version of SimpleBerry/LLaMA-O1-Base-1127 created using llama.cpp

Original Model Card

SimpleBerry/LLaMA-O1-Base-1127

This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the longcot_pt dataset.

Do not use this model without supervised training, please use LLaMA-O1-Supervised-1129 for directly usage.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
distributed_type: multi-GPU
num_devices: 24
total_train_batch_size: 24
total_eval_batch_size: 192
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 4.0

Training results

Framework versions

Transformers 4.46.2
Pytorch 2.3.1
Datasets 3.1.0
Tokenizers 0.20.1

Downloads last month: 40

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Model tree for QuantFactory/LLaMA-O1-Base-1127-GGUF

Base model

meta-llama/Llama-3.1-8B

Finetuned

meta-llama/Llama-3.1-8B-Instruct

Quantized

(633)

this model