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Instructions to use miceworks/Mark-Engineer-302M 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-302M 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-302M:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-Engineer-302M: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-302M:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-Engineer-302M: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-302M:IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf miceworks/Mark-Engineer-302M: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-302M:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf miceworks/Mark-Engineer-302M:IQ4_NL
Use Docker
docker model run hf.co/miceworks/Mark-Engineer-302M:IQ4_NL
- Llama new
- LM Studio
- Jan
- vLLM
How to use miceworks/Mark-Engineer-302M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miceworks/Mark-Engineer-302M" # 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-302M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miceworks/Mark-Engineer-302M:IQ4_NL
- Ollama
How to use miceworks/Mark-Engineer-302M with Ollama:
ollama run hf.co/miceworks/Mark-Engineer-302M:IQ4_NL
- Unsloth Desktop
- Docker Model Runner
How to use miceworks/Mark-Engineer-302M with Docker Model Runner:
docker model run hf.co/miceworks/Mark-Engineer-302M:IQ4_NL
- Lemonade
How to use miceworks/Mark-Engineer-302M with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miceworks/Mark-Engineer-302M:IQ4_NL
Run and chat with the model
lemonade run user.Mark-Engineer-302M-IQ4_NL
List all available models
lemonade list
- Atomic Chat
File size: 1,165 Bytes
231feba | 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 37 38 39 | {
"architectures": [
"MarkForCausalLM"
],
"model_type": "mark",
"model_name": "Mark Engineer 300M",
"model_version": "1.0.0",
"total_parameters": 302019848,
"active_parameters_per_token": "110M (padrao) a 165M (top-4)",
"vocab_size": 4311,
"hidden_size": 1024,
"intermediate_size": 3072,
"num_hidden_layers": 8,
"num_attention_heads": 16,
"num_key_value_heads": 4,
"use_mla": true,
"q_lora_rank": 512,
"kv_lora_rank": 256,
"qk_rope_head_dim": 32,
"v_head_dim": 64,
"n_routed_experts": 16,
"n_shared_experts": 1,
"num_experts_per_tok": 2,
"moe_intermediate_size": 1536,
"recursive_depth_loops": 2,
"mtp_depth": 1,
"max_position_embeddings": 4096,
"rms_norm_eps": 1e-05,
"rope_theta": 500000.0,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"quantization": "IQ4_NL",
"download": {
"gguf_url": "https://github.com/thu12dta3/Mark-AI/releases/download/v1.0.0-302m/mark_engineer_302m.gguf",
"pt_url": "https://github.com/thu12dta3/Mark-AI/releases/download/v1.0.0-302m/mark_engineer_302m_iq4_nl.pt",
"sha256": "daf05dd0dab4fc90f620d959cb6e5731c49b3f3c4ddce0ff28765e0751075fd9"
}
}
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