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
Download config.json from miceworks/Mark-SLM-V3-150M: direct link, hf CLI and curl.
- Browser
- Download file 1.23 kB
-
https://huggingface.co/miceworks/Mark-SLM-V3-150M/resolve/main/config.json
- Command line
-
hf download hf://miceworks/Mark-SLM-V3-150M/config.json
-
curl -L -o config.json https://huggingface.co/miceworks/Mark-SLM-V3-150M/resolve/main/config.json
1.23 kB
| { | |
| "architectures": [ | |
| "MarkV3ForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_mark_v3.MarkV3Config", | |
| "AutoModelForCausalLM": "modeling_mark_v3.MarkV3ForCausalLM" | |
| }, | |
| "model_type": "deepseek2", | |
| "model_name": "Mark-SLM-V3-150M", | |
| "model_version": "3.0.0", | |
| "vocab_size": 32032, | |
| "hidden_size": 576, | |
| "num_hidden_layers": 15, | |
| "num_attention_heads": 9, | |
| "num_key_value_heads": 9, | |
| "intermediate_size": 1536, | |
| "use_mla": true, | |
| "q_lora_rank": 256, | |
| "kv_lora_rank": 128, | |
| "qk_nope_head_dim": 64, | |
| "qk_rope_head_dim": 64, | |
| "v_head_dim": 64, | |
| "n_routed_experts": 16, | |
| "num_experts_per_tok": 2, | |
| "n_shared_experts": 1, | |
| "moe_intermediate_size": 256, | |
| "max_position_embeddings": 8192, | |
| "original_max_position_embeddings": 4096, | |
| "rope_theta": 10000.0, | |
| "rope_scaling_factor": 2.0, | |
| "rms_norm_eps": 1e-06, | |
| "tie_word_embeddings": true, | |
| "torch_dtype": "bfloat16", | |
| "quantization": "IQ4_NL", | |
| "total_parameters": 146899248, | |
| "active_parameters_per_token": 54001968, | |
| "weight_state": "TRAINED_CHECKPOINT", | |
| "sha256": "4bbfe2dc3fc7a6ac895d56e30352bda0de1322f9826afa28cafe7d24b0e99632", | |
| "target_platforms": [ | |
| "Ollama", | |
| "LM Studio", | |
| "llama.cpp", | |
| "PyTorch" | |
| ] | |
| } | |