Text Generation
Transformers
GGUF
Safetensors
mistral
quantized
2-bit
3-bit
4-bit precision
5-bit
6-bit
8-bit precision
GGUF
Merge
mergekit
lazymergekit
automerger
text-generation-inference
Instructions to use MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use MaziyarPanahi/T3qm7xpMultiverseex26-7B-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/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MaziyarPanahi/T3qm7xpMultiverseex26-7B-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/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf MaziyarPanahi/T3qm7xpMultiverseex26-7B-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/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MaziyarPanahi/T3qm7xpMultiverseex26-7B-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/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaziyarPanahi/T3qm7xpMultiverseex26-7B-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/T3qm7xpMultiverseex26-7B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M
- SGLang
How to use MaziyarPanahi/T3qm7xpMultiverseex26-7B-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/T3qm7xpMultiverseex26-7B-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/T3qm7xpMultiverseex26-7B-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/T3qm7xpMultiverseex26-7B-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/T3qm7xpMultiverseex26-7B-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF with Ollama:
ollama run hf.co/MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M
- Unsloth Desktop
- Docker Model Runner
How to use MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF with Docker Model Runner:
docker model run hf.co/MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M
- Lemonade
How to use MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.T3qm7xpMultiverseex26-7B-GGUF-Q4_K_M
List all available models
lemonade list
- Atomic Chat
Upload folder using huggingface_hub
#1
by MaziyarPanahi - opened
- .gitattributes +11 -0
- README.md +59 -0
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- T3qm7xpMultiverseex26-7B.Q6_K.gguf +3 -0
- T3qm7xpMultiverseex26-7B.Q8_0.gguf +3 -0
- T3qm7xpMultiverseex26-7B.fp16.gguf +3 -0
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---
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tags:
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- quantized
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- 2-bit
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- 3-bit
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- 4-bit
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- 5-bit
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- 6-bit
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- 8-bit
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- GGUF
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- transformers
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- safetensors
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- mistral
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- text-generation
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- merge
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- mergekit
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- lazymergekit
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- automerger
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- base_model:allknowingroger/MultiverseEx26-7B-slerp
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- license:apache-2.0
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- autotrain_compatible
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- endpoints_compatible
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- text-generation-inference
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- region:us
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- text-generation
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model_name: T3qm7xpMultiverseex26-7B-GGUF
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base_model: automerger/T3qm7xpMultiverseex26-7B
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inference: false
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model_creator: automerger
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pipeline_tag: text-generation
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quantized_by: MaziyarPanahi
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---
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# [MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF](https://huggingface.co/MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF)
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- Model creator: [automerger](https://huggingface.co/automerger)
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- Original model: [automerger/T3qm7xpMultiverseex26-7B](https://huggingface.co/automerger/T3qm7xpMultiverseex26-7B)
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## Description
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[MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF](https://huggingface.co/MaziyarPanahi/T3qm7xpMultiverseex26-7B-GGUF) contains GGUF format model files for [automerger/T3qm7xpMultiverseex26-7B](https://huggingface.co/automerger/T3qm7xpMultiverseex26-7B).
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### About GGUF
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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.
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Here is an incomplete list of clients and libraries that are known to support GGUF:
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* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
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* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
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* [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.
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* [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.
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* [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.
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* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
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* [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.
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* [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.
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* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
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* [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.
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## Special thanks
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🙏 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.
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