Instructions to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF", dtype="auto")

llama-cpp-python

How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF",
	filename="SauerHuatuoSkywork-o1-Llama-3.1-8B.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-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 grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-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 grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-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 grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M

SGLang

How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with Ollama:
```
ollama run hf.co/grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-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 grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-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 grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF to start chatting

How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-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": "grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-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 grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-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 grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M
```

Lemonade

How to use grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF-Q4_K_M

List all available models

lemonade list

SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF

This repo contains GGUF quants of a merge of pre-trained language models created using mergekit.

An experiment to hybridize a relatively high scoring Llama 3.1 8B model with o1 reasoning capabilities.

Although IFEval benched lower than the SauerkrautLM mode, every other benchmark improved from the addition of the o1 merge at low weight.

Made with Llama.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: grimjim/HuatuoSkywork-o1-Llama-3.1-8B
  - model: VAGOsolutions/Llama-3.1-SauerkrautLM-8b-Instruct
merge_method: slerp
base_model: grimjim/HuatuoSkywork-o1-Llama-3.1-8B
parameters:
  t:
   - value: 0.96
dtype: bfloat16

Open LLM Leaderboard Evaluation Results

Detailed results can be found here! Summarized results can be found here!

Metric	Value (%)
Average	26.63
IFEval (0-Shot)	52.19
BBH (3-Shot)	32.09
MATH Lvl 5 (4-Shot)	16.99
GPQA (0-shot)	9.51
MuSR (0-shot)	15.79
MMLU-PRO (5-shot)	33.23

Downloads last month: 27

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

6-bit

8-bit

Model tree for grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF

Base model

grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B

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(1)

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Collection including grimjim/SauerHuatuoSkywork-o1-Llama-3.1-8B-GGUF

Highlighted work

Collection

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Evaluation results

averaged accuracy on IFEval (0-Shot)
Open LLM Leaderboard

52.190
normalized accuracy on BBH (3-Shot)
test set Open LLM Leaderboard

32.090
exact match on MATH Lvl 5 (4-Shot)
test set Open LLM Leaderboard

16.990
acc_norm on GPQA (0-shot)
Open LLM Leaderboard

9.510
acc_norm on MuSR (0-shot)
Open LLM Leaderboard

15.790
accuracy on MMLU-PRO (5-shot)
test set Open LLM Leaderboard

33.230