Instructions to use tangledgroup/tangled-alpha-0.13-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

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
```

tangled-alpha-0.13-core

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

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:

mv wandb wandb-pretrain-base-0

Copy config:

cp ../config-0.json ../out/pretrain-base-0/final/config.json

Chat with model:

CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt chat ../out/pretrain-base-0/final

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'

litgpt convert_pretrained_checkpoint ../out/pretrain-base-0/final ../out/pretrain-base-0/checkpoint

CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_base_model_1.yaml

litgpt convert_pretrained_checkpoint ../out/pretrain-base-1/final ../out/pretrain-base-1/checkpoint

CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_base_model_2.yaml

litgpt convert_pretrained_checkpoint ../out/pretrain-base-2/final ../out/pretrain-base-2/checkpoint

CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_base_model_3.yaml

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'

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tangled-alpha-0.13-core

tangled-alpha-0.13-core

Datasets used to train tangledgroup/tangled-alpha-0.13-core