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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/LLaMA-O1-Base-1127-GGUF 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 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 serve -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

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

Atomic Chat

aashish1904 commited on Dec 5, 2024

Commit

94e9ccc

verified ·

1 Parent(s): 2ed99a9

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	@@ -0,0 +1,74 @@

+---
+library_name: transformers
+license: other
+base_model: meta-llama/Llama-3.1-8B-Instruct
+tags:
+- llama-factory
+- full
+- generated_from_trainer
+model-index:
+- name: SimpleBerry/LLaMA-O1-Base-1127
+  results: []
+---
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+# QuantFactory/LLaMA-O1-Base-1127-GGUF
+This is quantized version of [SimpleBerry/LLaMA-O1-Base-1127](https://huggingface.co/SimpleBerry/LLaMA-O1-Base-1127) created using llama.cpp
+# Original Model Card
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+# SimpleBerry/LLaMA-O1-Base-1127
+This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/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](https://huggingface.co/SimpleBerry/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