Instructions to use tangledgroup/tangled-alpha-0.13-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tangledgroup/tangled-alpha-0.13-core with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="tangledgroup/tangled-alpha-0.13-core")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tangledgroup/tangled-alpha-0.13-core", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use tangledgroup/tangled-alpha-0.13-core with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "tangledgroup/tangled-alpha-0.13-core" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "tangledgroup/tangled-alpha-0.13-core", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/tangledgroup/tangled-alpha-0.13-core
- SGLang
How to use tangledgroup/tangled-alpha-0.13-core 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 "tangledgroup/tangled-alpha-0.13-core" \ --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": "tangledgroup/tangled-alpha-0.13-core", "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 "tangledgroup/tangled-alpha-0.13-core" \ --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": "tangledgroup/tangled-alpha-0.13-core", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use tangledgroup/tangled-alpha-0.13-core with Docker Model Runner:
docker model run hf.co/tangledgroup/tangled-alpha-0.13-core
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Download README.md from tangledgroup/tangled-alpha-0.13-core: direct link, hf CLI and curl.
- Browser
- Download file 4.98 kB
-
https://huggingface.co/tangledgroup/tangled-alpha-0.13-core/resolve/main/README.md
- Command line
-
hf download hf://tangledgroup/tangled-alpha-0.13-core/README.md
-
curl -L -o README.md https://huggingface.co/tangledgroup/tangled-alpha-0.13-core/resolve/main/README.md
4.98 kB
| license: mit | |
| pipeline_tag: text-generation | |
| library_name: transformers | |
| language: [ | |
| 'en', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el', | |
| 'eo', 'es', 'et', 'eu', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gn', 'gu', 'ha', 'he', | |
| 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', | |
| 'ku', 'ky', 'la', 'lg', 'li', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', | |
| 'ne', 'nl', 'no', 'ns', 'om', 'or', 'pa', 'pl', 'ps', 'pt', 'qu', 'rm', 'ro', 'ru', 'sa', 'si', | |
| 'sc', 'sd', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'te', 'th', 'tl', 'tn', | |
| 'tr', 'ug', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zu', | |
| ] | |
| datasets: | |
| # core - base | |
| - ontocord/fineweb-permissive-multilingual-2m | |
| - distily/c4_multilingual_1M | |
| - data-silence/sumnews | |
| - xu-song/cc100-samples | |
| - badrex/llm-emoji-dataset | |
| - fblgit/simple-math | |
| - Gusarich/math-expressions-1m | |
| - neuralwork/arxiver | |
| - christopher/rosetta-code | |
| - nampdn-ai/tiny-codes | |
| - JeanKaddour/minipile | |
| # core - instruct | |
| - NousResearch/hermes-function-calling-v1 | |
| - simplescaling/s1K-1.1 | |
| # base - instruct | |
| - mlabonne/open-perfectblend | |
| - allenai/tulu-3-sft-mixture | |
| - rombodawg/Everything_Instruct_Multilingual | |
| # base - reason | |
| - open-r1/OpenR1-Math-220k | |
| - open-thoughts/OpenThoughts-114k | |
| - cognitivecomputations/dolphin-r1 | |
| - simplescaling/s1K-1.1 | |
| tags: | |
| - chat | |
| - core | |
| - base | |
| - instruct | |
| - reason | |
| # tangled-alpha-0.13-core | |
|  | |
| ```bash | |
| time python -B prepare_base_datasets.py | |
| ``` | |
| ``` | |
| i=0, min_len=0, max_len=1073741824, block_size=8193, chunk_size=16386000, len(dataset)=1496631, len(dataset) * block_size=12261897783 | |
| Total number of tokens in the optimized dataset '../base-data-0-0-1073741824-8193-2000' is 12261897783 | |
| i=1, min_len=8193, max_len=16385, block_size=16385, chunk_size=16385000, len(dataset)=78802, len(dataset) * block_size=1291170770 | |
| Total number of tokens in the optimized dataset '../base-data-1-8193-16385-16385-1000' is 1291170770 | |
| i=2, min_len=16385, max_len=32769, block_size=32769, chunk_size=16384500, len(dataset)=23511, len(dataset) * block_size=770431959 | |
| Total number of tokens in the optimized dataset '../base-data-2-16385-32769-32769-500' is 770431959 | |
| i=3, min_len=32769, max_len=65537, block_size=65537, chunk_size=16384250, len(dataset)=5128, len(dataset) * block_size=336073736 | |
| Total number of tokens in the optimized dataset '../base-data-3-32769-65537-65537-250' is 336073736 | |
| i=4, min_len=65537, max_len=131073, block_size=131073, chunk_size=16384125, len(dataset)=1169, len(dataset) * block_size=153224337 | |
| Total number of tokens in the optimized dataset '../base-data-4-65537-131073-131073-125' is 153224337 | |
| 46G ../base-data-0-0-1073741824-8193-2000 | |
| 4.9G ../base-data-1-8193-16385-16385-1000 | |
| 2.9G ../base-data-2-16385-32769-32769-500 | |
| 1.3G ../base-data-3-32769-65537-65537-250 | |
| 589M ../base-data-4-65537-131073-131073-125 | |
| ``` | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_base_model_0.yaml | |
| ``` | |
| ``` | |
| ``` | |
| Backup `wandb`: | |
| ```bash | |
| mv wandb wandb-pretrain-base-0 | |
| ``` | |
| Copy config: | |
| ```bash | |
| cp ../config-0.json ../out/pretrain-base-0/final/config.json | |
| ``` | |
| Chat with model: | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt chat ../out/pretrain-base-0/final | |
| ``` | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True time litgpt evaluate --tasks 'leaderboard' --out_dir '../evaluate/pretrain-base-0/leaderboard/' --batch_size '4' --dtype 'bfloat16' '../out/pretrain-base-0/final' | |
| ``` | |
| ``` | |
| ``` | |
| ```bash | |
| litgpt convert_pretrained_checkpoint ../out/pretrain-base-0/final ../out/pretrain-base-0/checkpoint | |
| ``` | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_base_model_1.yaml | |
| ``` | |
| ```bash | |
| litgpt convert_pretrained_checkpoint ../out/pretrain-base-1/final ../out/pretrain-base-1/checkpoint | |
| ``` | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_base_model_2.yaml | |
| ``` | |
| ```bash | |
| litgpt convert_pretrained_checkpoint ../out/pretrain-base-2/final ../out/pretrain-base-2/checkpoint | |
| ``` | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_base_model_3.yaml | |
| ``` | |
| ```bash | |
| CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True time litgpt evaluate --tasks 'leaderboard' --out_dir '../evaluate/pretrain-base-3/leaderboard/' --batch_size '4' --dtype 'bfloat16' '../out/pretrain-base-3/final' | |
| ``` | |
| ``` | |
| ``` | |