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

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

Mark-Engineer-1.5B / config.json

miceworks

fix: add config.json specifying exact parameter count

ed694b2 verified about 9 hours ago

Raw History Blame Contribute Delete

1.26 kB

{
  "architectures": [
    "MarkForCausalLM"
  ],
  "model_type": "mark",
  "model_name": "Mark Engineer 1.5B",
  "model_version": "1.0.0",
  "total_parameters": 1502301456,
  "active_parameters_per_token": "315M (padrao) a 440M (top-4)",
  "vocab_size": 4377,
  "hidden_size": 1280,
  "intermediate_size": 3840,
  "num_hidden_layers": 16,
  "num_attention_heads": 16,
  "num_key_value_heads": 4,
  "use_mla": true,
  "q_lora_rank": 640,
  "kv_lora_rank": 320,
  "qk_rope_head_dim": 32,
  "v_head_dim": 96,
  "n_routed_experts": 16,
  "n_shared_experts": 1,
  "num_experts_per_tok": 2,
  "moe_intermediate_size": 1504,
  "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",
  "target_platforms": ["LM Studio", "Ollama", "llama.cpp", "PyTorch"],
  "download": {
    "gguf_url": "https://github.com/thu12dta3/Mark-AI/releases/download/v1.0.0-engineer-1.5b/mark_engineer_1_5b_iq4_nl.gguf",
    "pt_url": "https://github.com/thu12dta3/Mark-AI/releases/download/v1.0.0-engineer-1.5b/mark_engineer_1_5b_iq4_nl.pt",
    "sha256": "b89e7c51480f27918ca193f40e325141ef31174092b774e1d9361be92d472280"
  }
}

	{
	"architectures": [
	"MarkForCausalLM"
	],
	"model_type": "mark",
	"model_name": "Mark Engineer 1.5B",
	"model_version": "1.0.0",
	"total_parameters": 1502301456,
	"active_parameters_per_token": "315M (padrao) a 440M (top-4)",
	"vocab_size": 4377,
	"hidden_size": 1280,
	"intermediate_size": 3840,
	"num_hidden_layers": 16,
	"num_attention_heads": 16,
	"num_key_value_heads": 4,
	"use_mla": true,
	"q_lora_rank": 640,
	"kv_lora_rank": 320,
	"qk_rope_head_dim": 32,
	"v_head_dim": 96,
	"n_routed_experts": 16,
	"n_shared_experts": 1,
	"num_experts_per_tok": 2,
	"moe_intermediate_size": 1504,
	"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",
	"target_platforms": ["LM Studio", "Ollama", "llama.cpp", "PyTorch"],
	"download": {
	"gguf_url": "https://github.com/thu12dta3/Mark-AI/releases/download/v1.0.0-engineer-1.5b/mark_engineer_1_5b_iq4_nl.gguf",
	"pt_url": "https://github.com/thu12dta3/Mark-AI/releases/download/v1.0.0-engineer-1.5b/mark_engineer_1_5b_iq4_nl.pt",
	"sha256": "b89e7c51480f27918ca193f40e325141ef31174092b774e1d9361be92d472280"
	}
	}