Text Generation
GGUF
Safetensors
English
deepseek2
llama.cpp
mixture-of-experts
multi-head-latent-attention
iq4_nl
mark-ai
systems-engineering
custom_code
conversational
Instructions to use miceworks/Mark-SLM-V3-150M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use miceworks/Mark-SLM-V3-150M 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 miceworks/Mark-SLM-V3-150M:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-SLM-V3-150M:IQ4_NL
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf miceworks/Mark-SLM-V3-150M:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-SLM-V3-150M:IQ4_NL
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf miceworks/Mark-SLM-V3-150M:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama cli -hf miceworks/Mark-SLM-V3-150M:IQ4_NL
Use Docker
docker model run hf.co/miceworks/Mark-SLM-V3-150M:IQ4_NL
- Llama new
- LM Studio
- Jan
- vLLM
How to use miceworks/Mark-SLM-V3-150M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miceworks/Mark-SLM-V3-150M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "miceworks/Mark-SLM-V3-150M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miceworks/Mark-SLM-V3-150M:IQ4_NL
- Ollama
How to use miceworks/Mark-SLM-V3-150M with Ollama:
ollama run hf.co/miceworks/Mark-SLM-V3-150M:IQ4_NL
- Unsloth Desktop
- Docker Model Runner
How to use miceworks/Mark-SLM-V3-150M with Docker Model Runner:
docker model run hf.co/miceworks/Mark-SLM-V3-150M:IQ4_NL
- Lemonade
How to use miceworks/Mark-SLM-V3-150M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miceworks/Mark-SLM-V3-150M:IQ4_NL
Run and chat with the model
lemonade run user.Mark-SLM-V3-150M-IQ4_NL
List all available models
lemonade list
- Atomic Chat
File size: 1,581 Bytes
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"vocab_size": 32032,
"bos_token_id": 1,
"eos_token_id": 2,
"special_tokens": {
"<|pad|>": 0,
"<|bos|>": 1,
"<|eos|>": 2,
"<think>": 3,
"</think>": 4,
"[RETRIEVE]": 5,
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"[EVALUATE]": 7,
"[ACCEPT]": 8,
"[REJECT]": 9,
"<|vision_start|>": 10,
"<|vision_patch|>": 11,
"<|vision_end|>": 12,
"<|voice_stream|>": 13,
"[UNKNOWN]": 14,
"[WEB_SEARCH]": 15,
"[/WEB_SEARCH]": 16,
"<|rag_context|>": 17,
"<|/rag_context|>": 18,
"<|mcp_call|>": 19,
"<|mcp_response|>": 20,
"<|citation|>": 21,
"<|/citation|>": 22
},
"supported_languages": [
"Rust",
"Python",
"Ruby",
"JavaScript",
"TypeScript",
"CSS",
"PHP",
"Kotlin",
"Java",
"Go",
"C++",
"C",
"C#",
"Assembly (x86_64/ARM64)",
"SQL",
"Shell"
],
"natural_languages": [
"pt",
"en",
"es",
"fr",
"de",
"it",
"ru",
"ja",
"ko",
"ar",
"hi",
"zh-Hans"
],
"bos_token": "<|bos|>",
"eos_token": "<|eos|>",
"pad_token": "<|pad|>",
"pad_token_id": 0,
"model_max_length": 8192,
"additional_special_tokens": [
"<think>",
"</think>",
"[RETRIEVE]",
"[/RETRIEVE]",
"[EVALUATE]",
"[ACCEPT]",
"[REJECT]",
"<|vision_start|>",
"<|vision_patch|>",
"<|vision_end|>",
"<|voice_stream|>",
"<|rag_context|>",
"<|/rag_context|>",
"<|mcp_call|>",
"<|mcp_response|>",
"<|citation|>",
"<|/citation|>"
],
"tokenizer_class": "PreTrainedTokenizerFast"
}
|