Instructions to use tangledgroup/tangled-alpha-0.4-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tangledgroup/tangled-alpha-0.4-core with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="tangledgroup/tangled-alpha-0.4-core")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tangledgroup/tangled-alpha-0.4-core", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use tangledgroup/tangled-alpha-0.4-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-0.4-core"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tangledgroup/tangled-alpha-0.4-core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tangledgroup/tangled-alpha-0.4-core

SGLang

How to use tangledgroup/tangled-alpha-0.4-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-0.4-core" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tangledgroup/tangled-alpha-0.4-core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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-0.4-core" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tangledgroup/tangled-alpha-0.4-core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tangledgroup/tangled-alpha-0.4-core with Docker Model Runner:
```
docker model run hf.co/tangledgroup/tangled-alpha-0.4-core
```

tangled-alpha-0.4-core

time python -B prepare_core_datasets.py

i=0, min_len=0, max_len=1048576, block_size=4097, chunk_size=16388000, len(dataset)=1567386, len(dataset) * block_size=6421580442
Total number of tokens in the optimized dataset '../core-data-0-0-1048576-4097-4000' is 6421580442

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

Seed set to 23
Time to instantiate model: 0.23 seconds.
Total parameters: 185,631,232
Verifying settings ...
Measured TFLOPs: 7047.32
Epoch 1 | iter 256 step 1 | loss train: 11.714, val: n/a | iter time: 370.39 ms (step) remaining time: 4 days, 1:24:16
Epoch 1 | iter 512 step 2 | loss train: 11.711, val: n/a | iter time: 311.97 ms (step) remaining time: 3 days, 8:48:48
Epoch 1 | iter 768 step 3 | loss train: 11.708, val: n/a | iter time: 313.48 ms (step) remaining time: 3 days, 3:22:46
Epoch 1 | iter 1024 step 4 | loss train: 11.704, val: n/a | iter time: 313.71 ms (step) remaining time: 3 days, 0:41:32
Epoch 1 | iter 1280 step 5 | loss train: 11.694, val: n/a | iter time: 314.42 ms (step) remaining time: 2 days, 23:05:08
Epoch 1 | iter 1536 step 6 | loss train: 11.687, val: n/a | iter time: 314.62 ms (step) remaining time: 2 days, 22:00:35
Epoch 1 | iter 1792 step 7 | loss train: 11.668, val: n/a | iter time: 314.94 ms (step) remaining time: 2 days, 21:14:06
Epoch 1 | iter 2048 step 8 | loss train: 11.645, val: n/a | iter time: 316.28 ms (step) remaining time: 2 days, 20:39:12
Epoch 1 | iter 2304 step 9 | loss train: 11.630, val: n/a | iter time: 315.29 ms (step) remaining time: 2 days, 20:11:52
Epoch 1 | iter 2560 step 10 | loss train: 11.609, val: n/a | iter time: 315.53 ms (step) remaining time: 2 days, 19:49:36
Epoch 1 | iter 2816 step 11 | loss train: 11.564, val: n/a | iter time: 314.95 ms (step) remaining time: 2 days, 19:31:09
Epoch 1 | iter 3072 step 12 | loss train: 11.510, val: n/a | iter time: 314.23 ms (step) remaining time: 2 days, 19:15:24
Epoch 1 | iter 3328 step 13 | loss train: 11.453, val: n/a | iter time: 315.71 ms (step) remaining time: 2 days, 19:02:02
Epoch 1 | iter 3584 step 14 | loss train: 11.411, val: n/a | iter time: 316.43 ms (step) remaining time: 2 days, 18:50:24
Epoch 1 | iter 3840 step 15 | loss train: 11.346, val: n/a | iter time: 314.83 ms (step) remaining time: 2 days, 18:40:08
Epoch 1 | iter 4096 step 16 | loss train: 11.300, val: n/a | iter time: 314.94 ms (step) remaining time: 2 days, 18:30:57
Epoch 1 | iter 4352 step 17 | loss train: 11.237, val: n/a | iter time: 314.13 ms (step) remaining time: 2 days, 18:22:39
Epoch 1 | iter 4608 step 18 | loss train: 11.193, val: n/a | iter time: 314.85 ms (step) remaining time: 2 days, 18:15:08
Epoch 1 | iter 4864 step 19 | loss train: 11.131, val: n/a | iter time: 315.23 ms (step) remaining time: 2 days, 18:08:16
Epoch 1 | iter 5120 step 20 | loss train: 11.084, val: n/a | iter time: 314.08 ms (step) remaining time: 2 days, 18:03:14
# ...
Epoch 1 | iter 780800 step 3050 | loss train: 3.176, val: 3.554 | iter time: 314.97 ms (step) remaining time: 0:15:21
Epoch 1 | iter 781056 step 3051 | loss train: 3.207, val: 3.554 | iter time: 315.53 ms (step) remaining time: 0:14:05
Epoch 1 | iter 781312 step 3052 | loss train: 3.186, val: 3.554 | iter time: 315.74 ms (step) remaining time: 0:12:48
Epoch 1 | iter 781568 step 3053 | loss train: 3.189, val: 3.554 | iter time: 315.17 ms (step) remaining time: 0:11:32
Epoch 1 | iter 781824 step 3054 | loss train: 3.305, val: 3.554 | iter time: 315.29 ms (step) remaining time: 0:10:15
Epoch 1 | iter 782080 step 3055 | loss train: 3.173, val: 3.554 | iter time: 315.11 ms (step) remaining time: 0:08:59
Epoch 1 | iter 782336 step 3056 | loss train: 3.223, val: 3.554 | iter time: 315.35 ms (step) remaining time: 0:07:42
Epoch 1 | iter 782592 step 3057 | loss train: 3.182, val: 3.554 | iter time: 315.18 ms (step) remaining time: 0:06:26
Epoch 1 | iter 782848 step 3058 | loss train: 3.196, val: 3.554 | iter time: 316.37 ms (step) remaining time: 0:05:09
Epoch 1 | iter 783104 step 3059 | loss train: 3.187, val: 3.554 | iter time: 315.86 ms (step) remaining time: 0:03:53
Epoch 1 | iter 783360 step 3060 | loss train: 3.163, val: 3.554 | iter time: 314.81 ms (step) remaining time: 0:02:36
Epoch 1 | iter 783616 step 3061 | loss train: 3.190, val: 3.554 | iter time: 315.23 ms (step) remaining time: 0:01:20
Epoch 2 | iter 783872 step 3062 | loss train: 3.239, val: 3.554 | iter time: 317.71 ms (step) remaining time: 0:00:03
Validating ...
Final evaluation | val loss: 3.552 | val ppl: 34.896
Saving checkpoint to '../out/pretrain-core/final/lit_model.pth'
----------------------------------------
| Performance
| - Total tokens  : 6,421,577,728
| - Training Time : 234340.96 s
| - Tok/sec       : 17286.07 tok/s
| ----------------------------------------
| Memory Usage
| - Memory Used   : 17.30 GB
----------------------------------------

Backup wandb:

mv wandb wandb-pretrain-core

Chat with model:

CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt chat ../out/pretrain-core/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-core-0/leaderboard/' --batch_size 1 --dtype 'bfloat16' '../out/pretrain-core/final'

# ...

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tangled-alpha-0.4-core

tangled-alpha-0.4-core

Datasets used to train tangledgroup/tangled-alpha-0.4-core