Text Generation
GGUF
English
Portuguese
mark
mark-ai
stem
self-rag
reasoning
generalist
epistemic-search
ollama
conversational
Instructions to use miceworks/Mark-General-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-General-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-General-1.5B:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-General-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-General-1.5B:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-General-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-General-1.5B:IQ4_NL # Run inference directly in the terminal: ./llama-cli -hf miceworks/Mark-General-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-General-1.5B:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama-cli -hf miceworks/Mark-General-1.5B:IQ4_NL
Use Docker
docker model run hf.co/miceworks/Mark-General-1.5B:IQ4_NL
- Llama new
- LM Studio
- Jan
- vLLM
How to use miceworks/Mark-General-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-General-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-General-1.5B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miceworks/Mark-General-1.5B:IQ4_NL
- Ollama
How to use miceworks/Mark-General-1.5B with Ollama:
ollama run hf.co/miceworks/Mark-General-1.5B:IQ4_NL
- Unsloth Desktop
- Docker Model Runner
How to use miceworks/Mark-General-1.5B with Docker Model Runner:
docker model run hf.co/miceworks/Mark-General-1.5B:IQ4_NL
- Lemonade
How to use miceworks/Mark-General-1.5B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miceworks/Mark-General-1.5B:IQ4_NL
Run and chat with the model
lemonade run user.Mark-General-1.5B-IQ4_NL
List all available models
lemonade list
- Atomic Chat
File size: 1,258 Bytes
c62b8b4 | 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 40 | {
"architectures": [
"MarkForCausalLM"
],
"model_type": "mark",
"model_name": "Mark General 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-general-1.5b/mark_general_1_5b_iq4_nl.gguf",
"pt_url": "https://github.com/thu12dta3/Mark-AI/releases/download/v1.0.0-general-1.5b/mark_general_1_5b_iq4_nl.pt",
"sha256": "cb573604d2d0990208fc84e47027e4104c870399063b0566121674e5850de124"
}
}
|