Instructions to use codeShare/Flux-Klein-SDNQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use codeShare/Flux-Klein-SDNQ-4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("codeShare/Flux-Klein-SDNQ-4bit", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps
- Draw Things
- DiffusionBee
File size: 3,195 Bytes
8a3edb1 | 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 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | {
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 151643,
"dtype": "bfloat16",
"eos_token_id": 151645,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2560,
"initializer_range": 0.02,
"intermediate_size": 9728,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 40960,
"max_window_layers": 36,
"model_type": "qwen3",
"num_attention_heads": 32,
"num_hidden_layers": 36,
"num_key_value_heads": 8,
"pad_token_id": null,
"quantization_config": {
"add_skip_keys": true,
"dequantize_fp32": true,
"dynamic_loss_threshold": null,
"group_size": 0,
"is_integer": true,
"is_training": false,
"modules_dtype_dict": {
"uint4": [
"lm_head"
]
},
"modules_quant_config": {
"embed_tokens_per_layer": {
"quantization_device": "cpu"
}
},
"modules_to_not_convert": [
"prediction_coefs",
".condition_embedder",
"wte",
".emb_in",
".vid_out",
"model.embed_tokens.weight",
"embedding_projection",
".txt_in",
".txt_out",
".y_embedder",
"patch_embed",
"lm_head.weight",
"multi_modal_projector",
"patch_embedding",
".final_layer",
".time_embed",
"lm_head",
".context_embedder",
".x_embedder",
".proj_out",
"correction_coefs",
".vid_in",
".t_embedder",
".norm_out",
".emb_out",
".img_in",
".img_out",
"patch_emb",
"time_text_embed"
],
"non_blocking": false,
"quant_conv": false,
"quant_embedding": false,
"quant_method": "sdnq",
"quantization_device": "cuda",
"quantized_matmul_dtype": null,
"return_device": "cpu",
"sdnq_version": "0.1.7",
"svd_rank": 32,
"svd_steps": 8,
"use_dynamic_quantization": false,
"use_grad_ckpt": true,
"use_quantized_matmul": true,
"use_quantized_matmul_conv": false,
"use_static_quantization": true,
"use_stochastic_rounding": false,
"use_svd": false,
"weights_dtype": "uint4"
},
"rms_norm_eps": 1e-06,
"rope_parameters": {
"rope_theta": 1000000,
"rope_type": "default"
},
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "5.0.0",
"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}
|