Instructions to use MaziyarPanahi/T3qStrangemerges_32-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MaziyarPanahi/T3qStrangemerges_32-7B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="MaziyarPanahi/T3qStrangemerges_32-7B-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MaziyarPanahi/T3qStrangemerges_32-7B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MaziyarPanahi/T3qStrangemerges_32-7B-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 MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/T3qStrangemerges_32-7B-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 MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf MaziyarPanahi/T3qStrangemerges_32-7B-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 MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf MaziyarPanahi/T3qStrangemerges_32-7B-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 MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use MaziyarPanahi/T3qStrangemerges_32-7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MaziyarPanahi/T3qStrangemerges_32-7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MaziyarPanahi/T3qStrangemerges_32-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M

SGLang

How to use MaziyarPanahi/T3qStrangemerges_32-7B-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 "MaziyarPanahi/T3qStrangemerges_32-7B-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": "MaziyarPanahi/T3qStrangemerges_32-7B-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 "MaziyarPanahi/T3qStrangemerges_32-7B-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": "MaziyarPanahi/T3qStrangemerges_32-7B-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use MaziyarPanahi/T3qStrangemerges_32-7B-GGUF with Ollama:
```
ollama run hf.co/MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use MaziyarPanahi/T3qStrangemerges_32-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M
```

Lemonade

How to use MaziyarPanahi/T3qStrangemerges_32-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MaziyarPanahi/T3qStrangemerges_32-7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.T3qStrangemerges_32-7B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

T3qStrangemerges_32-7B-GGUF / README.md

MaziyarPanahi

Upload folder using huggingface_hub (#1)

eb3d56a verified over 2 years ago

preview code

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	---
	tags:
	- quantized
	- 2-bit
	- 3-bit
	- 4-bit
	- 5-bit
	- 6-bit
	- 8-bit
	- GGUF
	- transformers
	- safetensors
	- mistral
	- text-generation
	- merge
	- mergekit
	- lazymergekit
	- automerger
	- license:apache-2.0
	- autotrain_compatible
	- endpoints_compatible
	- text-generation-inference
	- region:us
	- text-generation
	model_name: T3qStrangemerges_32-7B-GGUF
	base_model: automerger/T3qStrangemerges_32-7B
	inference: false
	model_creator: automerger
	pipeline_tag: text-generation
	quantized_by: MaziyarPanahi
	---
	# [MaziyarPanahi/T3qStrangemerges_32-7B-GGUF](https://huggingface.co/MaziyarPanahi/T3qStrangemerges_32-7B-GGUF)
	- Model creator: [automerger](https://huggingface.co/automerger)
	- Original model: [automerger/T3qStrangemerges_32-7B](https://huggingface.co/automerger/T3qStrangemerges_32-7B)

	## Description
	[MaziyarPanahi/T3qStrangemerges_32-7B-GGUF](https://huggingface.co/MaziyarPanahi/T3qStrangemerges_32-7B-GGUF) contains GGUF format model files for [automerger/T3qStrangemerges_32-7B](https://huggingface.co/automerger/T3qStrangemerges_32-7B).

	### About GGUF

	GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

	Here is an incomplete list of clients and libraries that are known to support GGUF:

	* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
	* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
	* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
	* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
	* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
	* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
	* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
	* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
	* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
	* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

	## Special thanks

	🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.