Instructions to use alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B", filename="Hy-MT2-1.8B-SpecDraft-30B-Q2_K.gguf", )
llm.create_chat_completion( messages = "\"Меня зовут Вольфганг и я живу в Берлине\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B 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 alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M # Run inference directly in the terminal: llama cli -hf alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M # Run inference directly in the terminal: llama cli -hf alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B: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 alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B: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 alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M
Use Docker
docker model run hf.co/alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B with Ollama:
ollama run hf.co/alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M
- Unsloth Studio
How to use alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B 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 alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B 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 alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B to start chatting
- Atomic Chat new
- Docker Model Runner
How to use alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B with Docker Model Runner:
docker model run hf.co/alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M
- Lemonade
How to use alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B:Q4_K_M
Run and chat with the model
lemonade run user.Hy-MT2-1.8B-SpecDraft-30B-Q4_K_M
List all available models
lemonade list
Hy-MT2-1.8B-SpecDraft-30B
Experimental Speculative Decoding Draft Model for Hy-MT2-30B-A3B
This is an experimental project that modifies Hy-MT2-1.8B into a speculative decoding draft model for Hy-MT2-30B-A3B.
Acceleration is not guaranteed. This project is mainly for users who want to explore speculative decoding on MoE models.
Technical Details
The original vocabularies are different:
- Hy-MT2-1.8B: 120818 tokens
- Hy-MT2-30B-A3B: 120832 tokens
The model was modified by:
- Replacing tokenizer with the Hy-MT2-30B-A3B tokenizer.
- Expanding embedding and lm_head weights:
- [120818, 2048] -> [120832, 2048]
- Initializing new token embeddings with mean padding.
In speculative decoding, the draft model only proposes candidate tokens. The target model Hy-MT2-30B-A3B always performs final verification, so final output quality is not affected.
Usage
llama-server ^
-m Hy-MT2-30B-A3B-APEX-Imatrix-I-Quality.gguf ^
--spec-type draft-simple ^
--spec-draft-model Hy-MT2-1.8B-SpecDraft-30B-Q4_K_M.gguf ^
--spec-draft-ngl 999 ^
--spec-draft-n-max 4 ^
--spec-draft-n-min 3 ^
--flash-attn on ^
--gpu-layers 999
Performance Notes
Hy-MT2-30B-A3B is a MoE model with around 3B active parameters.
Because its decoding speed is already very high, speculative decoding may not always improve performance.
Possible results:
- faster
- similar speed
- slightly slower
depending on hardware and llama.cpp scheduling behavior.
This project is intended for: - llama.cpp enthusiasts - large VRAM users - MoE speculative decoding experiments
License
Follows the original Hy-MT2 license. For research and experimental use only.
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Model tree for alphaZimuth/Hy-MT2-1.8B-SpecDraft-30B
Base model
tencent/Hy-MT2-1.8B