Instructions to use AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF", filename="Hy-MT1.5-1.8B-2bit.gguf", )
llm.create_chat_completion( messages = "\"Меня зовут Вольфганг и я живу в Берлине\"" )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF # Run inference directly in the terminal: llama-cli -hf AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF # Run inference directly in the terminal: llama-cli -hf AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF
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 AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF # Run inference directly in the terminal: ./llama-cli -hf AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF
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 AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF
Use Docker
docker model run hf.co/AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF
- LM Studio
- Jan
- Ollama
How to use AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF with Ollama:
ollama run hf.co/AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF
- Unsloth Studio new
How to use AngelSlim/Hy-MT1.5-1.8B-2bit-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 AngelSlim/Hy-MT1.5-1.8B-2bit-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 AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF to start chatting
- Docker Model Runner
How to use AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF with Docker Model Runner:
docker model run hf.co/AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF
- Lemonade
How to use AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AngelSlim/Hy-MT1.5-1.8B-2bit-GGUF
Run and chat with the model
lemonade run user.Hy-MT1.5-1.8B-2bit-GGUF-{{QUANT_TAG}}List all available models
lemonade list
it can not be loaded by the newest version llama.cpp..... which version do you use when developing ?
rm -f /wwwFS.out/unix.socket.llama.sock ; /ai02/binLLM/llama-server --host /wwwFS.out/unix.socket.llama.sock --timeout 3609 -m /ai01/llama-models/Hy-MT1.5-1.8B-2bit.gguf --threads 5 --parallel 2
build_info: b8985-27aef3dd9
system_info: n_threads = 5 (n_threads_batch = 5) / 6 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | BMI2 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |
Running without SSL
init: using 6 threads for HTTP server
start: setting address family to AF_UNIX
main: loading model
srv load_model: loading model '/ai01/llama-models/Hy-MT1.5-1.8B-2bit.gguf'
common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
common_params_fit_impl: getting device memory data for initial parameters:
gguf_init_from_file_ptr: tensor 'blk.0.attn_k_norm.weight' has offset 203248672, expected 203129888
gguf_init_from_file_ptr: failed to read tensor data
llama_model_load: error loading model: llama_model_loader: failed to load model from /ai01/llama-models/Hy-MT1.5-1.8B-2bit.gguf
llama_model_load_from_file_impl: failed to load model
common_fit_params: encountered an error while trying to fit params to free device memory: failed to load model
common_fit_params: fitting params to free memory took 0.04 seconds
gguf_init_from_file_ptr: tensor 'blk.0.attn_k_norm.weight' has offset 203248672, expected 203129888
gguf_init_from_file_ptr: failed to read tensor data
llama_model_load: error loading model: llama_model_loader: failed to load model from /ai01/llama-models/Hy-MT1.5-1.8B-2bit.gguf
llama_model_load_from_file_impl: failed to load model
common_init_from_params: failed to load model '/ai01/llama-models/Hy-MT1.5-1.8B-2bit.gguf'
srv load_model: failed to load model, '/ai01/llama-models/Hy-MT1.5-1.8B-2bit.gguf'
srv operator(): operator(): cleaning up before exit...
main: exiting due to model loading error
The 2-bit GGUF file is corrupt — the tensor offset table doesn't match the actual data layout. Re-downloading or re-quantizing it should resolve the issue entirely, I think.
We have used our custom kernel for llama.cpp, which will be released soon.
手机上效果很好也很快,要不先放一个win版的llama呢,感觉是很棒的模型