Text Generation
GGUF
Rust
English
Portuguese
mark_engineer_v2_8b
mark-ai
systems-engineering
linux-kernel
cpp
c
reverse-engineering
robotics
electrical-engineering
mla
Mixture of Experts
mtp
ollama
custom_code
conversational
Instructions to use miceworks/Mark-Engineer-V2-8B 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-V2-8B 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-V2-8B:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-Engineer-V2-8B: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-V2-8B:IQ4_NL # Run inference directly in the terminal: llama cli -hf miceworks/Mark-Engineer-V2-8B: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-Engineer-V2-8B:IQ4_NL # Run inference directly in the terminal: ./build/bin/llama cli -hf miceworks/Mark-Engineer-V2-8B:IQ4_NL
Use Docker
docker model run hf.co/miceworks/Mark-Engineer-V2-8B:IQ4_NL
- Llama new
- LM Studio
- Jan
- vLLM
How to use miceworks/Mark-Engineer-V2-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "miceworks/Mark-Engineer-V2-8B" # 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-V2-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/miceworks/Mark-Engineer-V2-8B:IQ4_NL
- Ollama
How to use miceworks/Mark-Engineer-V2-8B with Ollama:
ollama run hf.co/miceworks/Mark-Engineer-V2-8B:IQ4_NL
- Unsloth Desktop
- Docker Model Runner
How to use miceworks/Mark-Engineer-V2-8B with Docker Model Runner:
docker model run hf.co/miceworks/Mark-Engineer-V2-8B:IQ4_NL
- Lemonade
How to use miceworks/Mark-Engineer-V2-8B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull miceworks/Mark-Engineer-V2-8B:IQ4_NL
Run and chat with the model
lemonade run user.Mark-Engineer-V2-8B-IQ4_NL
List all available models
lemonade list
- Atomic Chat
File size: 1,315 Bytes
4d72a51 | 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 41 42 43 44 45 46 47 48 49 50 51 52 | {
"architectures": [
"MarkEngineerV2ForCausalLM"
],
"auto_map": {
"AutoConfig": "configuration_mark_engineer_v2_8b.MarkEngineerV2_8BConfig",
"AutoModelForCausalLM": "modeling_mark_engineer_v2_8b.MarkEngineerV2ForCausalLM"
},
"model_type": "mark_engineer_v2_8b",
"model_name": "Mark Engineer V2 8B",
"model_version": "2.0.0",
"total_parameters": 8005905408,
"active_parameters_per_token": 1626500000,
"vocab_size": 49152,
"hidden_size": 2560,
"intermediate_size": 7680,
"num_hidden_layers": 32,
"num_attention_heads": 32,
"num_key_value_heads": 8,
"use_mla": true,
"q_lora_rank": 1024,
"kv_lora_rank": 512,
"qk_nope_head_dim": 64,
"qk_rope_head_dim": 32,
"v_head_dim": 80,
"n_dense_layers": 2,
"n_routed_experts": 24,
"n_shared_experts": 1,
"num_experts_per_tok": 2,
"moe_intermediate_size": 1254,
"recursive_depth_loops": 2,
"mtp_depth": 1,
"max_position_embeddings": 8192,
"rms_norm_eps": 1e-05,
"rope_theta": 500000.0,
"tie_word_embeddings": true,
"torch_dtype": "bfloat16",
"quantization_formats": [
"IQ4_NL",
"Q4_K_M",
"Q8_0",
"F16"
],
"target_platforms": [
"Ollama",
"LM Studio",
"Transformers",
"vLLM"
],
"evaluation_status": "TRAINED_BUT_UNTESTED",
"training_convergence_loss": 1.9047
} |