Instructions to use alvarobartt/UltraCM-13B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alvarobartt/UltraCM-13B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="alvarobartt/UltraCM-13B-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("alvarobartt/UltraCM-13B-GGUF", dtype="auto")

llama-cpp-python

How to use alvarobartt/UltraCM-13B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="alvarobartt/UltraCM-13B-GGUF",
	filename="UltraCM-13b.q4_0.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use alvarobartt/UltraCM-13B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf alvarobartt/UltraCM-13B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf alvarobartt/UltraCM-13B-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 alvarobartt/UltraCM-13B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf alvarobartt/UltraCM-13B-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 alvarobartt/UltraCM-13B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf alvarobartt/UltraCM-13B-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 alvarobartt/UltraCM-13B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf alvarobartt/UltraCM-13B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/alvarobartt/UltraCM-13B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use alvarobartt/UltraCM-13B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alvarobartt/UltraCM-13B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alvarobartt/UltraCM-13B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alvarobartt/UltraCM-13B-GGUF:Q4_K_M

SGLang

How to use alvarobartt/UltraCM-13B-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 "alvarobartt/UltraCM-13B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alvarobartt/UltraCM-13B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "alvarobartt/UltraCM-13B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alvarobartt/UltraCM-13B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use alvarobartt/UltraCM-13B-GGUF with Ollama:
```
ollama run hf.co/alvarobartt/UltraCM-13B-GGUF:Q4_K_M
```

Unsloth Studio

How to use alvarobartt/UltraCM-13B-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 alvarobartt/UltraCM-13B-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 alvarobartt/UltraCM-13B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for alvarobartt/UltraCM-13B-GGUF to start chatting

Docker Model Runner
How to use alvarobartt/UltraCM-13B-GGUF with Docker Model Runner:
```
docker model run hf.co/alvarobartt/UltraCM-13B-GGUF:Q4_K_M
```

Lemonade

How to use alvarobartt/UltraCM-13B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull alvarobartt/UltraCM-13B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.UltraCM-13B-GGUF-Q4_K_M

List all available models

lemonade list

Model Card for UltraCM-13b-GGUF

UltraCM-13B is a fine-tuned LLM for completion-critique in order to evaluate LLM outputs on helpfulness, truthfulness, honesty, and to what extent the answer follows the given instructions.

UltraCM-13B is a 13b param LLM that was released by OpenBMB, as part of their paper UltraFeedback: Boosting Language Models with High-quality Feedback.

This model contains the quantized variants using the GGUF format, introduced by the llama.cpp team, and also heavily inspired by TheBloke work on quantizing most of the LLMs out there.

Model Details

Model Description

Model type: Llama
Fine-tuned from model: Llama-2-13b-hf
Created by: Meta AI
Fine-tuned by: OpenBMB
Quantized by: alvarobartt
Language(s) (NLP): English
License: Apache 2.0

Model Files

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
UltraCM-13b.q4_0.gguf	Q4_0	4	7.37 GB	9.87 GB	legacy; small, very high quality loss - prefer using Q3_K_M
UltraCM-13b.q4_k_s.gguf	Q4_K_S	4	7.41 GB	9.91 GB	small, greater quality loss
UltraCM-13b.q4_k_m.gguf	Q4_K_M	4	7.87 GB	10.37 GB	medium, balanced quality - recommended
UltraCM-13b.q5_0.gguf	Q5_0	5	8.97 GB	11.47 GB	legacy; medium, balanced quality - prefer using Q4_K_M
UltraCM-13b.q5_k_s.gguf	Q5_K_S	5	8.97 GB	11.47 GB	large, low quality loss - recommended
UltraCM-13b.q5_k_m.gguf	Q5_K_M	5	9.23 GB	11.73 GB	large, very low quality loss - recommended

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

For more information on quantization, I'd highly suggest anyone reading to go check TheBloke out, as well as joining their Discord server.

Uses

Direct Use

[More Information Needed]

Citation

Since this is only a GGUF-quantization of the original weights, please refer and cite the original authors instead.

@misc{cui2023ultrafeedback,
      title={UltraFeedback: Boosting Language Models with High-quality Feedback}, 
      author={Ganqu Cui and Lifan Yuan and Ning Ding and Guanming Yao and Wei Zhu and Yuan Ni and Guotong Xie and Zhiyuan Liu and Maosong Sun},
      year={2023},
      eprint={2310.01377},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

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GGUF

Model size

13B params

Architecture

llama

Hardware compatibility

4-bit

5-bit

Model tree for alvarobartt/UltraCM-13B-GGUF

Base model

openbmb/UltraCM-13b

Quantized

(1)

this model

Dataset used to train alvarobartt/UltraCM-13B-GGUF

Paper for alvarobartt/UltraCM-13B-GGUF

UltraFeedback: Boosting Language Models with High-quality Feedback

Paper • 2310.01377 • Published Oct 2, 2023 • 6