Text Generation
Transformers
Safetensors
English
llama
small
cpu
supra
v5
tiny
mini
open
open-source
text-generation-inference
Instructions to use SupraLabs/Supra-Mini-v5-8M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SupraLabs/Supra-Mini-v5-8M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="SupraLabs/Supra-Mini-v5-8M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("SupraLabs/Supra-Mini-v5-8M") model = AutoModelForCausalLM.from_pretrained("SupraLabs/Supra-Mini-v5-8M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use SupraLabs/Supra-Mini-v5-8M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "SupraLabs/Supra-Mini-v5-8M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "SupraLabs/Supra-Mini-v5-8M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/SupraLabs/Supra-Mini-v5-8M
- SGLang
How to use SupraLabs/Supra-Mini-v5-8M 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 "SupraLabs/Supra-Mini-v5-8M" \ --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": "SupraLabs/Supra-Mini-v5-8M", "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 "SupraLabs/Supra-Mini-v5-8M" \ --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": "SupraLabs/Supra-Mini-v5-8M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use SupraLabs/Supra-Mini-v5-8M with Docker Model Runner:
docker model run hf.co/SupraLabs/Supra-Mini-v5-8M
File size: 12,009 Bytes
d467b07 | 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 | [*] Loading libraries...
[*] Loading tokenizer...
[*] Preparing 5,000,000,000 tokens (streaming, memmap-backed)...
[=] Reusing existing token file: ./tokens.bin
[+] Dataset ready: 4,882,812 chunks of 1024 tokens
[*] Setting up model...
[*] Model parameters: 7,867,584
[*] Defining training arguments...
[transformers] warmup_ratio is deprecated and will be removed in v5.2. Use `warmup_steps` instead.
[*] Starting training...
0%| | 0/9538 [00:00<?, ?it/s]W0515 19:05:07.214000 41242 torch/_inductor/utils.py:1731] [0/0] Not enough SMs to use max_autotune_gemm mode
{'loss': '9.401', 'grad_norm': '1.098', 'learning_rate': '4.151e-05', 'epoch': '0.02097'}
{'loss': '8.45', 'grad_norm': '1.012', 'learning_rate': '8.344e-05', 'epoch': '0.04194'}
{'loss': '7.463', 'grad_norm': '1.007', 'learning_rate': '0.0001254', 'epoch': '0.06291'}
{'loss': '6.763', 'grad_norm': '1.135', 'learning_rate': '0.0001673', 'epoch': '0.08389'}
{'loss': '6.296', 'grad_norm': '0.8444', 'learning_rate': '0.0002', 'epoch': '0.1049'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 45.64it/s]
{'loss': '5.963', 'grad_norm': '1.129', 'learning_rate': '0.0001999', 'epoch': '0.1258'}
{'loss': '5.732', 'grad_norm': '1.432', 'learning_rate': '0.0001997', 'epoch': '0.1468'}
{'loss': '5.555', 'grad_norm': '1.714', 'learning_rate': '0.0001994', 'epoch': '0.1678'}
{'loss': '5.407', 'grad_norm': '1.082', 'learning_rate': '0.0001989', 'epoch': '0.1887'}
{'loss': '5.281', 'grad_norm': '1.087', 'learning_rate': '0.0001984', 'epoch': '0.2097'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 51.79it/s]
{'loss': '5.176', 'grad_norm': '1.031', 'learning_rate': '0.0001977', 'epoch': '0.2307'}
{'loss': '5.081', 'grad_norm': '1.037', 'learning_rate': '0.0001969', 'epoch': '0.2517'}
{'loss': '4.997', 'grad_norm': '1.259', 'learning_rate': '0.000196', 'epoch': '0.2726'}
{'loss': '4.919', 'grad_norm': '1.149', 'learning_rate': '0.0001949', 'epoch': '0.2936'}
{'loss': '4.848', 'grad_norm': '1.25', 'learning_rate': '0.0001938', 'epoch': '0.3146'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 53.17it/s]
{'loss': '4.782', 'grad_norm': '1.465', 'learning_rate': '0.0001925', 'epoch': '0.3355'}
{'loss': '4.717', 'grad_norm': '1.792', 'learning_rate': '0.0001912', 'epoch': '0.3565'}
{'loss': '4.656', 'grad_norm': '1.379', 'learning_rate': '0.0001897', 'epoch': '0.3775'}
{'loss': '4.598', 'grad_norm': '1.669', 'learning_rate': '0.0001881', 'epoch': '0.3985'}
{'loss': '4.55', 'grad_norm': '1.305', 'learning_rate': '0.0001864', 'epoch': '0.4194'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 53.48it/s]
{'loss': '4.508', 'grad_norm': '1.443', 'learning_rate': '0.0001846', 'epoch': '0.4404'}
{'loss': '4.47', 'grad_norm': '1.677', 'learning_rate': '0.0001827', 'epoch': '0.4614'}
{'loss': '4.437', 'grad_norm': '1.25', 'learning_rate': '0.0001807', 'epoch': '0.4823'}
{'loss': '4.403', 'grad_norm': '1.595', 'learning_rate': '0.0001786', 'epoch': '0.5033'}
{'loss': '4.375', 'grad_norm': '1.593', 'learning_rate': '0.0001764', 'epoch': '0.5243'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 53.04it/s]
{'loss': '4.35', 'grad_norm': '1.411', 'learning_rate': '0.0001741', 'epoch': '0.5453'}
{'loss': '4.328', 'grad_norm': '2.014', 'learning_rate': '0.0001718', 'epoch': '0.5662'}
{'loss': '4.303', 'grad_norm': '1.523', 'learning_rate': '0.0001693', 'epoch': '0.5872'}
{'loss': '4.285', 'grad_norm': '1.343', 'learning_rate': '0.0001668', 'epoch': '0.6082'}
{'loss': '4.264', 'grad_norm': '1.376', 'learning_rate': '0.0001641', 'epoch': '0.6291'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 53.00it/s]
{'loss': '4.247', 'grad_norm': '1.62', 'learning_rate': '0.0001614', 'epoch': '0.6501'}
{'loss': '4.232', 'grad_norm': '1.243', 'learning_rate': '0.0001587', 'epoch': '0.6711'}
{'loss': '4.214', 'grad_norm': '1.226', 'learning_rate': '0.0001558', 'epoch': '0.6921'}
{'loss': '4.199', 'grad_norm': '1.243', 'learning_rate': '0.0001529', 'epoch': '0.713'}
{'loss': '4.186', 'grad_norm': '1.813', 'learning_rate': '0.0001499', 'epoch': '0.734'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 52.44it/s]
{'loss': '4.171', 'grad_norm': '1.488', 'learning_rate': '0.0001469', 'epoch': '0.755'}
{'loss': '4.158', 'grad_norm': '1.535', 'learning_rate': '0.0001438', 'epoch': '0.7759'}
{'loss': '4.148', 'grad_norm': '1.208', 'learning_rate': '0.0001407', 'epoch': '0.7969'}
{'loss': '4.136', 'grad_norm': '1.366', 'learning_rate': '0.0001375', 'epoch': '0.8179'}
{'loss': '4.125', 'grad_norm': '1.289', 'learning_rate': '0.0001343', 'epoch': '0.8389'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 52.97it/s]
{'loss': '4.115', 'grad_norm': '1.306', 'learning_rate': '0.000131', 'epoch': '0.8598'}
{'loss': '4.104', 'grad_norm': '1.121', 'learning_rate': '0.0001277', 'epoch': '0.8808'}
{'loss': '4.096', 'grad_norm': '1.608', 'learning_rate': '0.0001243', 'epoch': '0.9018'}
{'loss': '4.089', 'grad_norm': '1.192', 'learning_rate': '0.0001209', 'epoch': '0.9227'}
{'loss': '4.078', 'grad_norm': '1.291', 'learning_rate': '0.0001175', 'epoch': '0.9437'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 53.15it/s]
{'loss': '4.073', 'grad_norm': '1.054', 'learning_rate': '0.0001141', 'epoch': '0.9647'}
{'loss': '4.066', 'grad_norm': '1.141', 'learning_rate': '0.0001107', 'epoch': '0.9857'}
{'loss': '4.057', 'grad_norm': '1.703', 'learning_rate': '0.0001072', 'epoch': '1.007'}
{'loss': '4.051', 'grad_norm': '1.104', 'learning_rate': '0.0001038', 'epoch': '1.027'}
{'loss': '4.042', 'grad_norm': '1.058', 'learning_rate': '0.0001003', 'epoch': '1.048'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 22.14it/s]
{'loss': '4.038', 'grad_norm': '1.095', 'learning_rate': '9.683e-05', 'epoch': '1.069'}
{'loss': '4.032', 'grad_norm': '1.074', 'learning_rate': '9.337e-05', 'epoch': '1.09'}
{'loss': '4.027', 'grad_norm': '1.18', 'learning_rate': '8.991e-05', 'epoch': '1.111'}
{'loss': '4.02', 'grad_norm': '1.193', 'learning_rate': '8.647e-05', 'epoch': '1.132'}
{'loss': '4.015', 'grad_norm': '1.291', 'learning_rate': '8.304e-05', 'epoch': '1.153'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 59.08it/s]
{'loss': '4.012', 'grad_norm': '1.045', 'learning_rate': '7.964e-05', 'epoch': '1.174'}
{'loss': '4.008', 'grad_norm': '1.35', 'learning_rate': '7.625e-05', 'epoch': '1.195'}
{'loss': '4.002', 'grad_norm': '1.086', 'learning_rate': '7.29e-05', 'epoch': '1.216'}
{'loss': '3.999', 'grad_norm': '0.8626', 'learning_rate': '6.958e-05', 'epoch': '1.237'}
{'loss': '3.992', 'grad_norm': '1.381', 'learning_rate': '6.63e-05', 'epoch': '1.258'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 59.27it/s]
{'loss': '3.99', 'grad_norm': '1.229', 'learning_rate': '6.305e-05', 'epoch': '1.279'}
{'loss': '3.987', 'grad_norm': '0.8244', 'learning_rate': '5.985e-05', 'epoch': '1.3'}
{'loss': '3.982', 'grad_norm': '0.9264', 'learning_rate': '5.67e-05', 'epoch': '1.321'}
{'loss': '3.982', 'grad_norm': '1.037', 'learning_rate': '5.36e-05', 'epoch': '1.342'}
{'loss': '3.976', 'grad_norm': '0.9665', 'learning_rate': '5.056e-05', 'epoch': '1.363'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 60.27it/s]
{'loss': '3.975', 'grad_norm': '0.8869', 'learning_rate': '4.758e-05', 'epoch': '1.384'}
{'loss': '3.971', 'grad_norm': '0.7576', 'learning_rate': '4.466e-05', 'epoch': '1.405'}
{'loss': '3.968', 'grad_norm': '0.8313', 'learning_rate': '4.18e-05', 'epoch': '1.426'}
{'loss': '3.965', 'grad_norm': '0.7926', 'learning_rate': '3.902e-05', 'epoch': '1.447'}
{'loss': '3.963', 'grad_norm': '0.9134', 'learning_rate': '3.631e-05', 'epoch': '1.468'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 58.90it/s]
{'loss': '3.963', 'grad_norm': '0.7194', 'learning_rate': '3.367e-05', 'epoch': '1.489'}
{'loss': '3.96', 'grad_norm': '0.6361', 'learning_rate': '3.112e-05', 'epoch': '1.51'}
{'loss': '3.957', 'grad_norm': '0.927', 'learning_rate': '2.865e-05', 'epoch': '1.531'}
{'loss': '3.957', 'grad_norm': '0.6016', 'learning_rate': '2.626e-05', 'epoch': '1.552'}
{'loss': '3.954', 'grad_norm': '0.6197', 'learning_rate': '2.397e-05', 'epoch': '1.573'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 59.64it/s]
{'loss': '3.952', 'grad_norm': '0.577', 'learning_rate': '2.176e-05', 'epoch': '1.594'}
{'loss': '3.948', 'grad_norm': '0.5791', 'learning_rate': '1.965e-05', 'epoch': '1.615'}
{'loss': '3.951', 'grad_norm': '0.5636', 'learning_rate': '1.763e-05', 'epoch': '1.636'}
{'loss': '3.949', 'grad_norm': '0.5653', 'learning_rate': '1.572e-05', 'epoch': '1.657'}
{'loss': '3.948', 'grad_norm': '0.5782', 'learning_rate': '1.39e-05', 'epoch': '1.678'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 59.44it/s]
{'loss': '3.946', 'grad_norm': '0.4793', 'learning_rate': '1.219e-05', 'epoch': '1.699'}
{'loss': '3.946', 'grad_norm': '0.4931', 'learning_rate': '1.058e-05', 'epoch': '1.72'}
{'loss': '3.945', 'grad_norm': '0.5097', 'learning_rate': '9.086e-06', 'epoch': '1.741'}
{'loss': '3.945', 'grad_norm': '0.5356', 'learning_rate': '7.697e-06', 'epoch': '1.761'}
{'loss': '3.945', 'grad_norm': '0.5223', 'learning_rate': '6.419e-06', 'epoch': '1.782'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 59.33it/s]
{'loss': '3.944', 'grad_norm': '0.4148', 'learning_rate': '5.253e-06', 'epoch': '1.803'}
{'loss': '3.943', 'grad_norm': '0.4304', 'learning_rate': '4.201e-06', 'epoch': '1.824'}
{'loss': '3.943', 'grad_norm': '0.4192', 'learning_rate': '3.265e-06', 'epoch': '1.845'}
{'loss': '3.942', 'grad_norm': '0.4074', 'learning_rate': '2.444e-06', 'epoch': '1.866'}
{'loss': '3.941', 'grad_norm': '0.44', 'learning_rate': '1.741e-06', 'epoch': '1.887'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 20.57it/s]
{'loss': '3.942', 'grad_norm': '0.4061', 'learning_rate': '1.156e-06', 'epoch': '1.908'}
{'loss': '3.941', 'grad_norm': '0.3939', 'learning_rate': '6.899e-07', 'epoch': '1.929'}
{'loss': '3.942', 'grad_norm': '0.3792', 'learning_rate': '3.431e-07', 'epoch': '1.95'}
{'loss': '3.944', 'grad_norm': '0.3599', 'learning_rate': '1.161e-07', 'epoch': '1.971'}
{'loss': '3.942', 'grad_norm': '0.3671', 'learning_rate': '9.142e-09', 'epoch': '1.992'}
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 59.80it/s]
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 59.16it/s]
{'train_runtime': '3.949e+04', 'train_samples_per_second': '247.3', 'train_steps_per_second': '0.242', 'train_loss': '4.414', 'epoch': '2'}
100%|ββββββββββββββββββββββββββββββββββββ| 9538/9538 [10:58:05<00:00, 4.14s/it]
Writing model shards: 100%|βββββββββββββββββββββββ| 1/1 [00:00<00:00, 57.38it/s]
[*] Training finished. |