Instructions to use tangledgroup/tangled-alpha-0.3-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tangledgroup/tangled-alpha-0.3-core with Transformers:

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

pipe = pipeline("text-generation", model="tangledgroup/tangled-alpha-0.3-core")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tangledgroup/tangled-alpha-0.3-core", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tangledgroup/tangled-alpha-0.3-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-0.3-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-0.3-core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tangledgroup/tangled-alpha-0.3-core

SGLang

How to use tangledgroup/tangled-alpha-0.3-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-0.3-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-0.3-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-0.3-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-0.3-core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tangledgroup/tangled-alpha-0.3-core with Docker Model Runner:
```
docker model run hf.co/tangledgroup/tangled-alpha-0.3-core
```

tangled-alpha-0.3-core / README.md

mtasic85

readme

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	---
	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-0.3-core

	![logo](./misc/logo.jpg)

	```bash
	time python -B prepare_core_datasets.py
	```

	```
	i=0, min_len=0, max_len=1048576, block_size=2049, chunk_size=16392000, len(dataset)=3134311, len(dataset) * block_size=6422203239
	Total number of tokens in the optimized dataset '../core-data-0-0-1048576-2049-8000' is 6422203239

	i=1, min_len=2049, max_len=8193, block_size=8193, chunk_size=16386000, len(dataset)=179944, len(dataset) * block_size=1474281192
	Total number of tokens in the optimized dataset '../core-data-1-2049-8193-8193-2000' is 1474281192

	i=2, min_len=8193, max_len=1048577, block_size=32769, chunk_size=16384500, len(dataset)=48261, len(dataset) * block_size=1581464709
	Total number of tokens in the optimized dataset '../core-data-2-8193-1048577-32769-500' is 1581464709
	```

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

	```
	Seed set to 23
	Time to instantiate model: 0.30 seconds.
	Total parameters: 185,631,232
	Verifying settings ...
	Measured TFLOPs: 14094.64
	Epoch 1 \| iter 128 step 1 \| loss train: 11.709, val: n/a \| iter time: 341.75 ms (step) remaining time: 3 days, 20:04:36
	Epoch 1 \| iter 256 step 2 \| loss train: 11.716, val: n/a \| iter time: 287.55 ms (step) remaining time: 3 days, 3:29:34
	Epoch 1 \| iter 384 step 3 \| loss train: 11.711, val: n/a \| iter time: 290.88 ms (step) remaining time: 2 days, 22:16:53
	Epoch 1 \| iter 512 step 4 \| loss train: 11.706, val: n/a \| iter time: 291.81 ms (step) remaining time: 2 days, 19:34:34
	Epoch 1 \| iter 640 step 5 \| loss train: 11.696, val: n/a \| iter time: 291.37 ms (step) remaining time: 2 days, 17:59:17
	Epoch 1 \| iter 768 step 6 \| loss train: 11.687, val: n/a \| iter time: 290.50 ms (step) remaining time: 2 days, 16:55:49
	Epoch 1 \| iter 896 step 7 \| loss train: 11.675, val: n/a \| iter time: 291.08 ms (step) remaining time: 2 days, 16:10:38
	Epoch 1 \| iter 1024 step 8 \| loss train: 11.660, val: n/a \| iter time: 294.46 ms (step) remaining time: 2 days, 15:36:26
	Epoch 1 \| iter 1152 step 9 \| loss train: 11.640, val: n/a \| iter time: 292.26 ms (step) remaining time: 2 days, 15:09:28
	Epoch 1 \| iter 1280 step 10 \| loss train: 11.626, val: n/a \| iter time: 289.93 ms (step) remaining time: 2 days, 14:47:34
	Epoch 1 \| iter 1408 step 11 \| loss train: 11.584, val: n/a \| iter time: 292.15 ms (step) remaining time: 2 days, 14:29:19
	Epoch 1 \| iter 1536 step 12 \| loss train: 11.526, val: n/a \| iter time: 291.24 ms (step) remaining time: 2 days, 14:13:54
	Epoch 1 \| iter 1664 step 13 \| loss train: 11.483, val: n/a \| iter time: 291.11 ms (step) remaining time: 2 days, 14:00:48
	Epoch 1 \| iter 1792 step 14 \| loss train: 11.430, val: n/a \| iter time: 290.68 ms (step) remaining time: 2 days, 13:49:24
	Epoch 1 \| iter 1920 step 15 \| loss train: 11.392, val: n/a \| iter time: 290.37 ms (step) remaining time: 2 days, 13:39:22
	Epoch 1 \| iter 2048 step 16 \| loss train: 11.326, val: n/a \| iter time: 290.31 ms (step) remaining time: 2 days, 13:30:34
	Epoch 1 \| iter 2176 step 17 \| loss train: 11.279, val: n/a \| iter time: 290.33 ms (step) remaining time: 2 days, 13:22:34
	Epoch 1 \| iter 2304 step 18 \| loss train: 11.222, val: n/a \| iter time: 290.50 ms (step) remaining time: 2 days, 13:15:27
	Epoch 1 \| iter 2432 step 19 \| loss train: 11.163, val: n/a \| iter time: 290.39 ms (step) remaining time: 2 days, 13:09:11
	Epoch 1 \| iter 2560 step 20 \| loss train: 11.094, val: n/a \| iter time: 290.00 ms (step) remaining time: 2 days, 13:03:21
	# ...
	Epoch 1 \| iter 782592 step 6114 \| loss train: 3.080, val: 3.255 \| iter time: 288.91 ms (step) remaining time: 0:06:14
	Epoch 1 \| iter 782720 step 6115 \| loss train: 3.096, val: 3.255 \| iter time: 289.11 ms (step) remaining time: 0:05:39
	Epoch 1 \| iter 782848 step 6116 \| loss train: 2.977, val: 3.255 \| iter time: 289.28 ms (step) remaining time: 0:05:04
	Epoch 1 \| iter 782976 step 6117 \| loss train: 3.040, val: 3.255 \| iter time: 289.24 ms (step) remaining time: 0:04:29
	Epoch 1 \| iter 783104 step 6118 \| loss train: 3.062, val: 3.255 \| iter time: 290.49 ms (step) remaining time: 0:03:54
	Epoch 1 \| iter 783232 step 6119 \| loss train: 3.037, val: 3.255 \| iter time: 289.91 ms (step) remaining time: 0:03:19
	Epoch 1 \| iter 783360 step 6120 \| loss train: 3.028, val: 3.255 \| iter time: 289.49 ms (step) remaining time: 0:02:44
	Epoch 1 \| iter 783488 step 6121 \| loss train: 3.007, val: 3.255 \| iter time: 289.81 ms (step) remaining time: 0:02:09
	Epoch 2 \| iter 783616 step 6122 \| loss train: 3.007, val: 3.255 \| iter time: 289.34 ms (step) remaining time: 0:01:34
	Epoch 2 \| iter 783744 step 6123 \| loss train: 3.046, val: 3.255 \| iter time: 288.52 ms (step) remaining time: 0:00:59
	Epoch 2 \| iter 783872 step 6124 \| loss train: 3.140, val: 3.255 \| iter time: 288.66 ms (step) remaining time: 0:00:24
	Validating ...
	Final evaluation \| val loss: 3.254 \| val ppl: 25.904
	Saving checkpoint to '../out/pretrain-core-0/final/lit_model.pth'
	----------------------------------------
	\| Performance
	\| - Total tokens : 6,422,200,320
	\| - Training Time : 214857.29 s
	\| - Tok/sec : 109674.70 tok/s
	\| ----------------------------------------
	\| Memory Usage
	\| - Memory Used : 17.30 GB
	----------------------------------------
	```

	Backup `wandb`:

	```bash
	mv wandb wandb-pretrain-core
	```

	Chat with model:

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

	```
	# ...
	```