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
Download config.json from miceworks/Mark-Engineer-V2-8B: direct link, hf CLI and curl.
- Browser
- Download file 1.32 kB
-
https://huggingface.co/miceworks/Mark-Engineer-V2-8B/resolve/main/config.json
- Command line
-
hf download hf://miceworks/Mark-Engineer-V2-8B/config.json
-
curl -L -o config.json https://huggingface.co/miceworks/Mark-Engineer-V2-8B/resolve/main/config.json
1.32 kB
| { | |
| "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 | |
| } |