Text Generation
MLX
Safetensors
mimo_v2_flash
jang
jang-quantized
JANG_2M
mixed-precision
apple-silicon
conversational
custom_code
Instructions to use bearzi/MiMo-V2-Flash-JANG_2M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bearzi/MiMo-V2-Flash-JANG_2M with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("bearzi/MiMo-V2-Flash-JANG_2M") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Pi new
How to use bearzi/MiMo-V2-Flash-JANG_2M with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bearzi/MiMo-V2-Flash-JANG_2M"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bearzi/MiMo-V2-Flash-JANG_2M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bearzi/MiMo-V2-Flash-JANG_2M with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bearzi/MiMo-V2-Flash-JANG_2M"
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default bearzi/MiMo-V2-Flash-JANG_2M
Run Hermes
hermes
- MLX LM
How to use bearzi/MiMo-V2-Flash-JANG_2M with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "bearzi/MiMo-V2-Flash-JANG_2M"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "bearzi/MiMo-V2-Flash-JANG_2M" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bearzi/MiMo-V2-Flash-JANG_2M", "messages": [ {"role": "user", "content": "Hello"} ] }'
| { | |
| "architectures": [ | |
| "MiMoV2FlashForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_mimo_v2_flash.MiMoV2FlashConfig", | |
| "AutoModel": "modeling_mimo_v2_flash.MiMoV2FlashModel", | |
| "AutoModelForCausalLM": "modeling_mimo_v2_flash.MiMoV2FlashForCausalLM" | |
| }, | |
| "attention_dropout": 0.0, | |
| "attention_value_scale": 0.707, | |
| "hidden_act": "silu", | |
| "hidden_size": 4096, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 16384, | |
| "max_position_embeddings": 262144, | |
| "model_type": "mimo_v2_flash", | |
| "num_attention_heads": 64, | |
| "head_dim": 192, | |
| "num_hidden_layers": 48, | |
| "num_key_value_heads": 4, | |
| "layernorm_epsilon": 1e-05, | |
| "rope_theta": 5000000, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.40.1", | |
| "use_cache": true, | |
| "vocab_size": 152576, | |
| "partial_rotary_factor": 0.334, | |
| "sliding_window": 128, | |
| "swa_rope_theta": 10000, | |
| "attention_bias": false, | |
| "v_head_dim": 128, | |
| "hybrid_layer_pattern": [ | |
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| "add_swa_attention_sink_bias": true, | |
| "add_full_attention_sink_bias": false, | |
| "sliding_window_size": 128, | |
| "attention_chunk_size": 128, | |
| "moe_layer_freq": [ | |
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| "moe_intermediate_size": 2048, | |
| "n_routed_experts": 256, | |
| "n_shared_experts": null, | |
| "num_experts_per_tok": 8, | |
| "norm_topk_prob": true, | |
| "scoring_func": "sigmoid", | |
| "n_group": 1, | |
| "topk_group": 1, | |
| "topk_method": "noaux_tc", | |
| "routed_scaling_factor": null, | |
| "swa_num_attention_heads": 64, | |
| "swa_num_key_value_heads": 8, | |
| "swa_head_dim": 192, | |
| "swa_v_head_dim": 128, | |
| "quantization": { | |
| "group_size": 128, | |
| "bits": 2 | |
| } | |
| } | |