Text Generation
Transformers
Safetensors
GGUF
Chinese
English
qwen3
minimind
llama-cpp
qwen3-compatible
text-generation-inference
Instructions to use diverWayne/mikky-64m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use diverWayne/mikky-64m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="diverWayne/mikky-64m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("diverWayne/mikky-64m") model = AutoModelForCausalLM.from_pretrained("diverWayne/mikky-64m") - llama-cpp-python
How to use diverWayne/mikky-64m with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="diverWayne/mikky-64m", filename="mikky-64m-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 diverWayne/mikky-64m with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf diverWayne/mikky-64m:BF16 # Run inference directly in the terminal: llama-cli -hf diverWayne/mikky-64m:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf diverWayne/mikky-64m:BF16 # Run inference directly in the terminal: llama-cli -hf diverWayne/mikky-64m:BF16
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 diverWayne/mikky-64m:BF16 # Run inference directly in the terminal: ./llama-cli -hf diverWayne/mikky-64m:BF16
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 diverWayne/mikky-64m:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf diverWayne/mikky-64m:BF16
Use Docker
docker model run hf.co/diverWayne/mikky-64m:BF16
- LM Studio
- Jan
- vLLM
How to use diverWayne/mikky-64m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "diverWayne/mikky-64m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "diverWayne/mikky-64m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/diverWayne/mikky-64m:BF16
- SGLang
How to use diverWayne/mikky-64m 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 "diverWayne/mikky-64m" \ --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": "diverWayne/mikky-64m", "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 "diverWayne/mikky-64m" \ --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": "diverWayne/mikky-64m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Ollama
How to use diverWayne/mikky-64m with Ollama:
ollama run hf.co/diverWayne/mikky-64m:BF16
- Unsloth Studio
How to use diverWayne/mikky-64m 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 diverWayne/mikky-64m 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 diverWayne/mikky-64m to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for diverWayne/mikky-64m to start chatting
- Docker Model Runner
How to use diverWayne/mikky-64m with Docker Model Runner:
docker model run hf.co/diverWayne/mikky-64m:BF16
- Lemonade
How to use diverWayne/mikky-64m with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull diverWayne/mikky-64m:BF16
Run and chat with the model
lemonade run user.mikky-64m-BF16
List all available models
lemonade list
| { | |
| "add_bos_token": false, | |
| "add_eos_token": false, | |
| "add_prefix_space": false, | |
| "added_tokens_decoder": { | |
| "0": { | |
| "content": "<|endoftext|>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
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| "content": "<|im_start|>", | |
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| "special": true | |
| }, | |
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| }, | |
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| }, | |
| "6": { | |
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| }, | |
| "7": { | |
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| "18": { | |
| "content": "<tts_text_bos>", | |
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| "special": true | |
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| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "20": { | |
| "content": "<tts_text_bos_single>", | |
| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": true | |
| }, | |
| "21": { | |
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| "rstrip": false, | |
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| "special": false | |
| }, | |
| "22": { | |
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| }, | |
| "23": { | |
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| "single_word": false, | |
| "special": false | |
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| "lstrip": false, | |
| "normalized": false, | |
| "rstrip": false, | |
| "single_word": false, | |
| "special": false | |
| } | |
| }, | |
| "additional_special_tokens": [ | |
| "<|im_start|>", | |
| "<|im_end|>", | |
| "<|object_ref_start|>", | |
| "<|object_ref_end|>", | |
| "<|box_start|>", | |
| "<|box_end|>", | |
| "<|quad_start|>", | |
| "<|quad_end|>", | |
| "<|vision_start|>", | |
| "<|vision_end|>", | |
| "<|vision_pad|>", | |
| "<|image_pad|>", | |
| "<|video_pad|>", | |
| "<|audio_start|>", | |
| "<|audio_end|>", | |
| "<|audio_pad|>", | |
| "<tts_pad>", | |
| "<tts_text_bos>", | |
| "<tts_text_eod>", | |
| "<tts_text_bos_single>" | |
| ], | |
| "audio_bos_token": "<|audio_start|>", | |
| "audio_eos_token": "<|audio_end|>", | |
| "audio_token": "<|audio_pad|>", | |
| "bos_token": "<|im_start|>", | |
| "clean_up_tokenization_spaces": false, | |
| "eos_token": "<|im_end|>", | |
| "extra_special_tokens": {}, | |
| "image_token": "<|image_pad|>", | |
| "legacy": true, | |
| "model_max_length": 131072, | |
| "pad_token": "<|endoftext|>", | |
| "sp_model_kwargs": {}, | |
| "spaces_between_special_tokens": false, | |
| "tokenizer_class": "PreTrainedTokenizerFast", | |
| "unk_token": "<|endoftext|>", | |
| "video_token": "<|video_pad|>", | |
| "vision_bos_token": "<|vision_start|>", | |
| "vision_eos_token": "<|vision_end|>" | |
| } | |