Instructions to use QuantFactory/Dolus-14b-Mini-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Dolus-14b-Mini-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Dolus-14b-Mini-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Dolus-14b-Mini-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Dolus-14b-Mini-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/Dolus-14b-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Dolus-14b-Mini-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/Dolus-14b-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Dolus-14b-Mini-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/Dolus-14b-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Dolus-14b-Mini-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/Dolus-14b-Mini-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Dolus-14b-Mini-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Dolus-14b-Mini-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Dolus-14b-Mini-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Dolus-14b-Mini-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": "QuantFactory/Dolus-14b-Mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Dolus-14b-Mini-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Dolus-14b-Mini-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 "QuantFactory/Dolus-14b-Mini-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": "QuantFactory/Dolus-14b-Mini-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 "QuantFactory/Dolus-14b-Mini-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": "QuantFactory/Dolus-14b-Mini-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Dolus-14b-Mini-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Dolus-14b-Mini-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use QuantFactory/Dolus-14b-Mini-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Dolus-14b-Mini-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Dolus-14b-Mini-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Dolus-14b-Mini-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Dolus-14b-Mini-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Dolus-14b-Mini-GGUF

File size: 2,341 Bytes

1b5a673


---

license: cc-by-nc-nd-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- llama
- mistral
- llama-2
- llama
- agi
- probelm solving
- biology
- reasoning
- llama3
- Dolus

---

![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)

# QuantFactory/Dolus-14b-Mini-GGUF
This is quantized version of [Cognitive-Machines-Labs/Dolus-14b-Mini](https://huggingface.co/Cognitive-Machines-Labs/Dolus-14b-Mini) created using llama.cpp

# Original Model Card


<h1 style="font-size: 36px;">Dolus-14b-Mini</h1>

<center>
  <img src="https://cdn.prod.website-files.com/66a60c25748608d6d03809b8/66bc5c429e88c62f64491f57_00009-2074390737-p-500.png" alt="logo" width="18%" style="min-width:20px; display:block;">
</center>

**Ursidae-12b-Mini**  
The little sister to Ursidae-300b,Dolus 14b Mini has been developed with a focus on complex multi-step chain of thought problem solving while still being deployable on edge systems. A model focused complex multi-step chain of thought problem solving while still being deployable on edge systems. Now better at reasoning!


## Main Goals:

Dolus was designed to address specific issues found in other chat models:

- Overcome limitations in logical reasoning found in other chat models.
- Efficiently solve complex, multi-step problems.
- Provide better decision-making assistance by enhancing the model's ability to reason and think critically.
- Removing restrictions and allowing the model to gain a true understanding of reality, greatly increasing overall results.

By focusing on these specific goals, the Ursidae-12b-Mini aims to provide a more sophisticated AI system that excels at critical thinking and problem-solving tasks requiring advanced logical reasoning skills. Its compact design makes it an efficient choice for applications where high cognitive abilities are necessary without occupying excessive computing resources.  

# Recommended Settings:

**Defaults:**

```
min_p: 0.074
top_k: 40
repetition_penalty: 1.12
temp: 1.18
context: 8192
```


**Creative:**

```
min_p: 0.062
top_k: 40
repetition_penalty: 1.11
temp: 1.24
context: 8192
```

# Benchmarks:
PENDING FULL EVAL