Text Generation
MLX
Safetensors
English
Korean
Japanese
solar_open2
solar
solar-open2
Mixture of Experts
quantized
4bit
conversational
4-bit precision
Instructions to use TensorFold/Solar-Open2-250B-MLX-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use TensorFold/Solar-Open2-250B-MLX-4bit 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("TensorFold/Solar-Open2-250B-MLX-4bit") 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 Settings
- LM Studio
- Pi
How to use TensorFold/Solar-Open2-250B-MLX-4bit with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TensorFold/Solar-Open2-250B-MLX-4bit"
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "TensorFold/Solar-Open2-250B-MLX-4bit" } ] } } }Run Pi
# Start Pi in your project directory: pi
- MLX LM
How to use TensorFold/Solar-Open2-250B-MLX-4bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "TensorFold/Solar-Open2-250B-MLX-4bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "TensorFold/Solar-Open2-250B-MLX-4bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TensorFold/Solar-Open2-250B-MLX-4bit", "messages": [ {"role": "user", "content": "Hello"} ] }' - Hermes Agent
How to use TensorFold/Solar-Open2-250B-MLX-4bit 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 "TensorFold/Solar-Open2-250B-MLX-4bit"
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 TensorFold/Solar-Open2-250B-MLX-4bit
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use TensorFold/Solar-Open2-250B-MLX-4bit with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "TensorFold/Solar-Open2-250B-MLX-4bit"
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "TensorFold/Solar-Open2-250B-MLX-4bit" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
File size: 1,371 Bytes
c444e5d | 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 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 | {
"architectures": [
"SolarOpen2ForCausalLM"
],
"bos_token_id": 1,
"eos_token_id": [
2,
129
],
"first_k_dense_replace": 0,
"gqa_interval": 3,
"gqa_layers": [
0,
4,
8,
12,
16,
20,
24,
28,
32,
36,
40,
44
],
"head_dim": 128,
"hidden_size": 4096,
"intermediate_size": 10240,
"kda_allow_neg_eigval": true,
"kda_use_full_proj": false,
"linear_attn_config": {
"short_conv_kernel_size": 4,
"head_dim": 128,
"num_heads": 64,
"num_kv_heads": null
},
"max_position_embeddings": 1048576,
"model_type": "solar_open2",
"moe_intermediate_size": 1280,
"n_routed_experts": 320,
"n_shared_experts": 1,
"norm_topk_prob": true,
"num_attention_heads": 64,
"num_experts_per_tok": 8,
"num_hidden_layers": 48,
"num_key_value_heads": 8,
"pad_token_id": 2,
"partial_rotary_factor": 1.0,
"quantization": {
"group_size": 64,
"bits": 4,
"mode": "affine"
},
"quantization_config": {
"group_size": 64,
"bits": 4,
"mode": "affine"
},
"rms_norm_eps": 1e-05,
"rope_theta": 10000,
"routed_scaling_factor": 1.0,
"tie_word_embeddings": false,
"torch_dtype": "bfloat16",
"transformers_version": "5.5.4",
"use_gqa_gate": true,
"use_rope": false,
"vocab_size": 196608,
"model_file": "solar_open2.py",
"library_name": "mlx"
}
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