Instructions to use shibatch/tinymoe2m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use shibatch/tinymoe2m with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("shibatch/tinymoe2m", dtype="auto") - llama-cpp-python
How to use shibatch/tinymoe2m with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="shibatch/tinymoe2m", filename="tinymoe2m.BF16.gguf", )
output = llm( "Once upon a time,", max_tokens=512, echo=True ) print(output)
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use shibatch/tinymoe2m with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf shibatch/tinymoe2m:Q4_K_M # Run inference directly in the terminal: llama-cli -hf shibatch/tinymoe2m:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf shibatch/tinymoe2m:Q4_K_M # Run inference directly in the terminal: llama-cli -hf shibatch/tinymoe2m: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 shibatch/tinymoe2m:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf shibatch/tinymoe2m: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 shibatch/tinymoe2m:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf shibatch/tinymoe2m:Q4_K_M
Use Docker
docker model run hf.co/shibatch/tinymoe2m:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use shibatch/tinymoe2m with Ollama:
ollama run hf.co/shibatch/tinymoe2m:Q4_K_M
- Unsloth Studio
How to use shibatch/tinymoe2m 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 shibatch/tinymoe2m 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 shibatch/tinymoe2m to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for shibatch/tinymoe2m to start chatting
- Docker Model Runner
How to use shibatch/tinymoe2m with Docker Model Runner:
docker model run hf.co/shibatch/tinymoe2m:Q4_K_M
- Lemonade
How to use shibatch/tinymoe2m with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull shibatch/tinymoe2m:Q4_K_M
Run and chat with the model
lemonade run user.tinymoe2m-Q4_K_M
List all available models
lemonade list
File size: 827 Bytes
364c6be ad0e5ec 364c6be ad0e5ec 364c6be | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 | {
"architectures": [
"MixtralForCausalLM"
],
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "float32",
"eos_token_id": 2,
"head_dim": null,
"hidden_act": "silu",
"hidden_size": 128,
"initializer_range": 0.02,
"intermediate_size": 352,
"max_position_embeddings": 4096,
"model_type": "mixtral",
"num_attention_heads": 2,
"num_experts_per_tok": 2,
"num_hidden_layers": 3,
"num_key_value_heads": 2,
"num_local_experts": 4,
"output_router_logits": false,
"pad_token_id": 2,
"rms_norm_eps": 1e-05,
"rope_parameters": {
"rope_theta": 15000.0,
"rope_type": "default"
},
"router_aux_loss_coef": 0.001,
"router_jitter_noise": 0.0,
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "5.9.0",
"use_cache": false,
"vocab_size": 512
}
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