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Instructions to use miceworks/Mark-Engineer-1.5B 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-Engineer-1.5B 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-Engineer-1.5B:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-Engineer-1.5B: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-Engineer-1.5B:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-Engineer-1.5B:IQ4_NL
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 miceworks/Mark-Engineer-1.5B:IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf miceworks/Mark-Engineer-1.5B:IQ4_NL
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 miceworks/Mark-Engineer-1.5B:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf miceworks/Mark-Engineer-1.5B:IQ4_NL
Use Docker
docker model run hf.co/miceworks/Mark-Engineer-1.5B:IQ4_NL
- Llama new
- LM Studio
- Jan
- vLLM
How to use miceworks/Mark-Engineer-1.5B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miceworks/Mark-Engineer-1.5B" # 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-Engineer-1.5B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miceworks/Mark-Engineer-1.5B:IQ4_NL
- Ollama
How to use miceworks/Mark-Engineer-1.5B with Ollama:
ollama run hf.co/miceworks/Mark-Engineer-1.5B:IQ4_NL
- Unsloth Desktop
- Docker Model Runner
How to use miceworks/Mark-Engineer-1.5B with Docker Model Runner:
docker model run hf.co/miceworks/Mark-Engineer-1.5B:IQ4_NL
- Lemonade
How to use miceworks/Mark-Engineer-1.5B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miceworks/Mark-Engineer-1.5B:IQ4_NL
Run and chat with the model
lemonade run user.Mark-Engineer-1.5B-IQ4_NL
List all available models
lemonade list
- Atomic Chat
fix: add config.json specifying exact parameter count
Browse files- config.json +39 -0
config.json
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{
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"architectures": [
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"MarkForCausalLM"
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],
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"model_type": "mark",
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"model_name": "Mark Engineer 1.5B",
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"model_version": "1.0.0",
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"total_parameters": 1502301456,
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"active_parameters_per_token": "315M (padrao) a 440M (top-4)",
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"vocab_size": 4377,
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"hidden_size": 1280,
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"intermediate_size": 3840,
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"num_hidden_layers": 16,
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"num_attention_heads": 16,
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"num_key_value_heads": 4,
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"use_mla": true,
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"q_lora_rank": 640,
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"kv_lora_rank": 320,
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"qk_rope_head_dim": 32,
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"v_head_dim": 96,
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"n_routed_experts": 16,
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"n_shared_experts": 1,
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"num_experts_per_tok": 2,
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"moe_intermediate_size": 1504,
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"recursive_depth_loops": 2,
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"mtp_depth": 1,
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"max_position_embeddings": 4096,
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"rms_norm_eps": 1e-05,
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "bfloat16",
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"quantization": "IQ4_NL",
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"target_platforms": ["LM Studio", "Ollama", "llama.cpp", "PyTorch"],
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"download": {
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"gguf_url": "https://github.com/thu12dta3/Mark-AI/releases/download/v1.0.0-engineer-1.5b/mark_engineer_1_5b_iq4_nl.gguf",
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"pt_url": "https://github.com/thu12dta3/Mark-AI/releases/download/v1.0.0-engineer-1.5b/mark_engineer_1_5b_iq4_nl.pt",
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"sha256": "b89e7c51480f27918ca193f40e325141ef31174092b774e1d9361be92d472280"
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}
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}
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