Text Generation
Transformers
Safetensors
English
lowonmind
tiny-lm
pretrained-from-scratch
scaling-limits
custom_code
Instructions to use DedeProGames/LowOnMind-1M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DedeProGames/LowOnMind-1M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DedeProGames/LowOnMind-1M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("DedeProGames/LowOnMind-1M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DedeProGames/LowOnMind-1M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DedeProGames/LowOnMind-1M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DedeProGames/LowOnMind-1M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/DedeProGames/LowOnMind-1M
- SGLang
How to use DedeProGames/LowOnMind-1M with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "DedeProGames/LowOnMind-1M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DedeProGames/LowOnMind-1M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "DedeProGames/LowOnMind-1M" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DedeProGames/LowOnMind-1M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use DedeProGames/LowOnMind-1M with Docker Model Runner:
docker model run hf.co/DedeProGames/LowOnMind-1M
File size: 4,401 Bytes
5082d41 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 | {
"data": "fineweb_edu_200M_v1024.uint16.bin",
"base": "LowOnMind-1M",
"derived_from": "DedeProGames/LowOnMind-300k",
"dataset": "HuggingFaceFW/fineweb-edu:sample-10BT",
"tokenizer": "byte-level BPE 1024",
"params": 985152,
"seq_len": 512,
"batch_size": 64,
"grad_accum": 1,
"max_steps": 6103,
"total_tokens": 199983104,
"lr": 0.0015,
"min_lr": 0.00015,
"warmup_steps": 250,
"weight_decay": 0.1,
"grad_clip": 1.0,
"val_tokens": 2000000,
"eval_every": 500,
"eval_batches": 40,
"device": "cuda",
"dtype": "float16",
"compile": true,
"seed": 1337,
"train_minutes": 10.6,
"final_val_loss": 2.9908,
"final_val_ppl": 19.901,
"bits_per_char": 1.8361,
"chars_per_token": 2.35,
"reused_tokenizer_from": "DedeProGames/LowOnMind-300k",
"baseline": {
"name": "LowOnMind-300k",
"params": 296960,
"val_loss": 3.2982,
"val_ppl": 27.06,
"bpc": 2.03,
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},
"lexicon": {
"model_real_word_rate": 0.9798,
"corpus_real_word_rate": 0.9839,
"words_scored": 5647,
"samples": 64,
"top_nonwords": [
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]
},
"samples": [
{
"prompt": "The ",
"text": "The veil-group, where is used to solve solar passwords and vegetables. It is often very important to reduce their treatments, including checkouts, labs, maintenance, mental groups, chemicals and maintenance costs. These literally confirmed elements are the most commonly used in the elementary front, solar energy, a"
},
{
"prompt": "Photosynthesis is ",
"text": "Photosynthesis is essential to prevent disease infection. These types of disease include:\n\u2022 Transformation of Brook Scientific World Organization\n\u2022 Effectiveness of a diagnosis is commonly diagnosed with diagnosis. The Cultural Disorder is an important factor in diagnosis. Analysis of this purpose can be considered."
},
{
"prompt": "In 1969, ",
"text": "In 1969, 8, 1999, Michael Kerzin, Anderson, 1997, Scotland, 1993, 1,359, p. 547\u2013414\nOur war was chosen in the West of Europe, Papua Party, Ottoman Economy, Pluto Mountains, and Evans, 1854, 1946, Maine, 1888-81, p"
},
{
"prompt": "Students should ",
"text": "Students should identify their learners and adults.\nThe United States has free quotations in the world that can help them with this curriculum and finding problem concerning these funding.\nOne major solutions for malnutrition in South East and Africa is a way to limit half its own loop. This is the best way to ensure that the drills of the state and"
}
],
"history": [
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]
} |