Instructions to use Deci/DeciLM-7B-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Deci/DeciLM-7B-instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="Deci/DeciLM-7B-instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Deci/DeciLM-7B-instruct-GGUF")
model = AutoModelForCausalLM.from_pretrained("Deci/DeciLM-7B-instruct-GGUF")

llama-cpp-python

How to use Deci/DeciLM-7B-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Deci/DeciLM-7B-instruct-GGUF",
	filename="decilm-7b-uniform-gqa-f16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Deci/DeciLM-7B-instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Deci/DeciLM-7B-instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Deci/DeciLM-7B-instruct-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Deci/DeciLM-7B-instruct-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf Deci/DeciLM-7B-instruct-GGUF:F16

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 Deci/DeciLM-7B-instruct-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf Deci/DeciLM-7B-instruct-GGUF:F16

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 Deci/DeciLM-7B-instruct-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Deci/DeciLM-7B-instruct-GGUF:F16

Use Docker

docker model run hf.co/Deci/DeciLM-7B-instruct-GGUF:F16

LM Studio
Jan

vLLM

How to use Deci/DeciLM-7B-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Deci/DeciLM-7B-instruct-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": "Deci/DeciLM-7B-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Deci/DeciLM-7B-instruct-GGUF:F16

SGLang

How to use Deci/DeciLM-7B-instruct-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 "Deci/DeciLM-7B-instruct-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": "Deci/DeciLM-7B-instruct-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 "Deci/DeciLM-7B-instruct-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": "Deci/DeciLM-7B-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Deci/DeciLM-7B-instruct-GGUF with Ollama:
```
ollama run hf.co/Deci/DeciLM-7B-instruct-GGUF:F16
```

Unsloth Studio new

How to use Deci/DeciLM-7B-instruct-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 Deci/DeciLM-7B-instruct-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 Deci/DeciLM-7B-instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Deci/DeciLM-7B-instruct-GGUF to start chatting

Docker Model Runner
How to use Deci/DeciLM-7B-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/Deci/DeciLM-7B-instruct-GGUF:F16
```

Lemonade

How to use Deci/DeciLM-7B-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Deci/DeciLM-7B-instruct-GGUF:F16

Run and chat with the model

lemonade run user.DeciLM-7B-instruct-GGUF-F16

List all available models

lemonade list

DeciLM-7B-instruct GGUF checkpoints

This repository includes Deci/DeciLM-7B-instruct checkpoints in the GGUF format.
DeciLM demonstrates strong performance on commodity CPUs when utilizing the llama.cpp codebase.

1. Clone and build llama.cpp (1 minute)

git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make -j

2. Download the GGUF checkpoint

Navigate to the 'Files' section
- Click on 'decilm-7b-uniform-gqa-q8_0.gguf'
  - Click on the 'Download' button

3. Generate outputs

Use the chat template and feed a prompt to DeciLM-7B-instruct. We are using the INT8 quantized GGUF checkpoint.

./main -m ~/Downloads/decilm-7b-uniform-gqa-q8_0.gguf -p """
### System:
You are an AI assistant that follows instructions exceptionally well. Be as helpful as possible.
### User:
How do I make the most delicious pancakes the world has ever tasted?
### Assistant:
"""

Output:

### System:
You are an AI assistant that follows instructions exceptionally well. Be as helpful as possible.
### User:
How do I make the most delicious pancakes the world has ever tasted?
### Assistant:
 To make the most delicious pancakes (pancakes) the world has ever tasted, here are some steps you can follow:


Ingreat ingredients:
- 1 cup all-purpose flour (maida flour)- 1/4 cup sugar (granhana or raw white)
- 2 egg yolks
- 1/2 cup milk-2 tabls melted butter (or oil)-2 tsp vanila
- 2 tsps baking powder
- salt (a little)

Mixing:
1. Combine the flour, sugar and salt in a large mixing bowl. Add egg yolks, butter or oil, and vanilla to the mixture, stirring well.
2. Pour in milk gradually while blending (with mixer).
3. Beat with the batter till smooth, then add baking powder.

Cooking:
4. Heat a nonstick pan with little butter or oil, pour 1/4 cup of batter (or 5 inch) for each pancake. Cook over medium heat, until bubble rises on top surface. Flip and cook the other side till golden.  [end of text]

llama_print_timings:        load time =     325.09 ms
llama_print_timings:      sample time =      19.99 ms /   249 runs   (    0.08 ms per token, 12454.98 tokens per second)
llama_print_timings: prompt eval time =     239.66 ms /    52 tokens (    4.61 ms per token,   216.97 tokens per second)
llama_print_timings:        eval time =   11227.83 ms /   248 runs   (   45.27 ms per token,    22.09 tokens per second)
llama_print_timings:       total time =   11517.22 ms
ggml_metal_free: deallocating
Log end

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

8-bit

16-bit

32-bit

Spaces using Deci/DeciLM-7B-instruct-GGUF 3

Collection including Deci/DeciLM-7B-instruct-GGUF

DeciLM Models

Collection

DeciLMs are small, but mighty, language models. Members of the DeciLM family of models include 6 and 7 billion parameter models. • 7 items • Updated Jan 17, 2024 • 3