Text Generation
MLX
Safetensors
Norwegian
Norwegian Bokmål
Norwegian Nynorsk
gemma3
conversational
instruct
experimental
8-bit precision
🇪🇺 Region: EU
Instructions to use NbAiLab/borealis-4b-instruct-preview-mlx-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use NbAiLab/borealis-4b-instruct-preview-mlx-8bit 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("NbAiLab/borealis-4b-instruct-preview-mlx-8bit") 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
- MLX LM
How to use NbAiLab/borealis-4b-instruct-preview-mlx-8bit with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "NbAiLab/borealis-4b-instruct-preview-mlx-8bit"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "NbAiLab/borealis-4b-instruct-preview-mlx-8bit" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NbAiLab/borealis-4b-instruct-preview-mlx-8bit", "messages": [ {"role": "user", "content": "Hello"} ] }'
File size: 2,764 Bytes
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"architectures": [
"Gemma3ForConditionalGeneration"
],
"boi_token_index": 255999,
"bos_token_id": 2,
"dtype": "bfloat16",
"eoi_token_index": 256000,
"eos_token_id": 106,
"hidden_size": 2560,
"image_token_index": 262144,
"initializer_range": 0.02,
"mm_tokens_per_image": 256,
"model_type": "gemma3",
"pad_token_id": 0,
"quantization": {
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"bits": 8,
"mode": "affine"
},
"quantization_config": {
"group_size": 64,
"bits": 8,
"mode": "affine"
},
"text_config": {
"_sliding_window_pattern": 6,
"attention_bias": false,
"attention_dropout": 0.0,
"attn_logit_softcapping": null,
"dtype": "bfloat16",
"final_logit_softcapping": null,
"head_dim": 256,
"hidden_activation": "gelu_pytorch_tanh",
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"initializer_range": 0.02,
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"full_attention",
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],
"max_position_embeddings": 131072,
"model_type": "gemma3_text",
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"num_key_value_heads": 4,
"query_pre_attn_scalar": 256,
"rms_norm_eps": 1e-06,
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"rope_type": "linear"
},
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"sliding_window": 1024,
"use_bidirectional_attention": false,
"use_cache": false,
"vocab_size": 262208
},
"transformers_version": "4.57.1",
"use_cache": false
} |