Text Generation
GGUF
mistral
quantized
2-bit
3-bit
4-bit precision
5-bit
6-bit
8-bit precision
GGUF
conversational
Instructions to use MaziyarPanahi/SmolLM2-135M-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use MaziyarPanahi/SmolLM2-135M-Instruct-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="MaziyarPanahi/SmolLM2-135M-Instruct-GGUF", filename="SmolLM2-135M-Instruct.Q2_K.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use MaziyarPanahi/SmolLM2-135M-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 MaziyarPanahi/SmolLM2-135M-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MaziyarPanahi/SmolLM2-135M-Instruct-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 MaziyarPanahi/SmolLM2-135M-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf MaziyarPanahi/SmolLM2-135M-Instruct-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/SmolLM2-135M-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf MaziyarPanahi/SmolLM2-135M-Instruct-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/SmolLM2-135M-Instruct-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf MaziyarPanahi/SmolLM2-135M-Instruct-GGUF:Q4_K_M
Use Docker
docker model run hf.co/MaziyarPanahi/SmolLM2-135M-Instruct-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use MaziyarPanahi/SmolLM2-135M-Instruct-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaziyarPanahi/SmolLM2-135M-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": "MaziyarPanahi/SmolLM2-135M-Instruct-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/MaziyarPanahi/SmolLM2-135M-Instruct-GGUF:Q4_K_M
- Ollama
How to use MaziyarPanahi/SmolLM2-135M-Instruct-GGUF with Ollama:
ollama run hf.co/MaziyarPanahi/SmolLM2-135M-Instruct-GGUF:Q4_K_M
- Unsloth Studio
How to use MaziyarPanahi/SmolLM2-135M-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 MaziyarPanahi/SmolLM2-135M-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 MaziyarPanahi/SmolLM2-135M-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 MaziyarPanahi/SmolLM2-135M-Instruct-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use MaziyarPanahi/SmolLM2-135M-Instruct-GGUF with Docker Model Runner:
docker model run hf.co/MaziyarPanahi/SmolLM2-135M-Instruct-GGUF:Q4_K_M
- Lemonade
How to use MaziyarPanahi/SmolLM2-135M-Instruct-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull MaziyarPanahi/SmolLM2-135M-Instruct-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.SmolLM2-135M-Instruct-GGUF-Q4_K_M
List all available models
lemonade list
Upload folder using huggingface_hub (#1)
Browse files- 5ea3b3d5a38eb6d8ce0163a80c55f1c73e8dcf80ddf84dd050983cc8bfd484f7 (9bcf8747c18c92a82fb9aca405108477ebc61114)
- .gitattributes +10 -0
- README.md +46 -0
- SmolLM2-135M-Instruct-GGUF_imatrix.dat +0 -0
- SmolLM2-135M-Instruct.Q2_K.gguf +3 -0
- SmolLM2-135M-Instruct.Q3_K_L.gguf +3 -0
- SmolLM2-135M-Instruct.Q3_K_M.gguf +3 -0
- SmolLM2-135M-Instruct.Q4_K_M.gguf +3 -0
- SmolLM2-135M-Instruct.Q4_K_S.gguf +3 -0
- SmolLM2-135M-Instruct.Q5_K_M.gguf +3 -0
- SmolLM2-135M-Instruct.Q5_K_S.gguf +3 -0
- SmolLM2-135M-Instruct.Q6_K.gguf +3 -0
- SmolLM2-135M-Instruct.Q8_0.gguf +3 -0
- SmolLM2-135M-Instruct.fp16.gguf +3 -0
.gitattributes
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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SmolLM2-135M-Instruct.Q2_K.gguf filter=lfs diff=lfs merge=lfs -text
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SmolLM2-135M-Instruct.fp16.gguf filter=lfs diff=lfs merge=lfs -text
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README.md
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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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- text-generation
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- text-generation
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model_name: SmolLM2-135M-Instruct-GGUF
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base_model: HuggingFaceTB/SmolLM2-135M-Instruct
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inference: false
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model_creator: HuggingFaceTB
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pipeline_tag: text-generation
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quantized_by: MaziyarPanahi
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---
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# [MaziyarPanahi/SmolLM2-135M-Instruct-GGUF](https://huggingface.co/MaziyarPanahi/SmolLM2-135M-Instruct-GGUF)
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- Model creator: [HuggingFaceTB](https://huggingface.co/HuggingFaceTB)
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- Original model: [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct)
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## Description
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[MaziyarPanahi/SmolLM2-135M-Instruct-GGUF](https://huggingface.co/MaziyarPanahi/SmolLM2-135M-Instruct-GGUF) contains GGUF format model files for [HuggingFaceTB/SmolLM2-135M-Instruct](https://huggingface.co/HuggingFaceTB/SmolLM2-135M-Instruct).
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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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SmolLM2-135M-Instruct-GGUF_imatrix.dat
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Binary file (606 kB). View file
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SmolLM2-135M-Instruct.Q2_K.gguf
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SmolLM2-135M-Instruct.Q3_K_L.gguf
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SmolLM2-135M-Instruct.Q3_K_M.gguf
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SmolLM2-135M-Instruct.Q4_K_M.gguf
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SmolLM2-135M-Instruct.Q6_K.gguf
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SmolLM2-135M-Instruct.Q8_0.gguf
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SmolLM2-135M-Instruct.fp16.gguf
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