Text Generation
MLX
English
next-concept-modeling
ncm
non-autoregressive
coarse-to-fine
apple-silicon
tinyworld
Eval Results (legacy)
Instructions to use janmusic/ncm-mlx-tinyworld with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use janmusic/ncm-mlx-tinyworld with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("janmusic/ncm-mlx-tinyworld") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use janmusic/ncm-mlx-tinyworld with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "janmusic/ncm-mlx-tinyworld" --prompt "Once upon a time"
- Atomic Chat
File size: 2,216 Bytes
8ed7b11 | 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 | {
"stage": "codec",
"steps": 3000,
"train_seconds": 52.21,
"tokenizer": {
"alphabet": " ._abcdefghiklnoprstuvwxy",
"pad": "_",
"seq_len": 32
},
"history": [
{
"step": 250,
"loss": 0.7816118597984314,
"ce": 0.0183502696454525,
"vq": 0.763261616230011
},
{
"step": 500,
"loss": 0.7766135931015015,
"ce": 0.008087735623121262,
"vq": 0.7685258388519287
},
{
"step": 750,
"loss": 1.9127604961395264,
"ce": 1.155642032623291,
"vq": 0.7571184635162354
},
{
"step": 1000,
"loss": 1.7280001640319824,
"ce": 0.9738318920135498,
"vq": 0.7541682720184326
},
{
"step": 1250,
"loss": 0.7600001096725464,
"ce": 0.0029663536697626114,
"vq": 0.7570337653160095
},
{
"step": 1500,
"loss": 1.7674469947814941,
"ce": 1.0144059658050537,
"vq": 0.7530410289764404
},
{
"step": 1750,
"loss": 1.321763038635254,
"ce": 0.5726578235626221,
"vq": 0.7491052746772766
},
{
"step": 2000,
"loss": 0.7995526790618896,
"ce": 0.028942842036485672,
"vq": 0.7706098556518555
},
{
"step": 2250,
"loss": 0.7662097811698914,
"ce": 0.004673249088227749,
"vq": 0.7615365386009216
},
{
"step": 2500,
"loss": 0.7780300974845886,
"ce": 0.004726271145045757,
"vq": 0.7733038067817688
},
{
"step": 2750,
"loss": 0.932603120803833,
"ce": 0.16295668482780457,
"vq": 0.7696464657783508
},
{
"step": 3000,
"loss": 0.7746670842170715,
"ce": 0.0031447401270270348,
"vq": 0.7715223431587219
}
],
"val": {
"char_acc_by_levels_used": [
0.7497,
0.8812,
0.9568,
0.9865,
0.9942,
1.0
],
"seq_exact_by_levels_used": [
0.0,
0.05,
0.366,
0.76,
0.878,
1.0
],
"codebook_utilization": [
1.0,
1.0,
1.0,
1.0,
1.0,
1.0
],
"code_perplexity": [
114.54,
106.31,
97.86,
104.75,
103.75,
96.55
]
}
}
|