Instructions to use tangledgroup/tangled-alpha-0.2-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tangledgroup/tangled-alpha-0.2-core with Transformers:

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

pipe = pipeline("text-generation", model="tangledgroup/tangled-alpha-0.2-core")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tangledgroup/tangled-alpha-0.2-core", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tangledgroup/tangled-alpha-0.2-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-0.2-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-0.2-core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tangledgroup/tangled-alpha-0.2-core

SGLang

How to use tangledgroup/tangled-alpha-0.2-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-0.2-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-0.2-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-0.2-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-0.2-core",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use tangledgroup/tangled-alpha-0.2-core with Docker Model Runner:
```
docker model run hf.co/tangledgroup/tangled-alpha-0.2-core
```

tangled-alpha-0.2-core

File size: 1,374 Bytes

5b81e55

roles_map = {
    'system': 'system',
    'user': 'user',
    'human': 'user',
    'assistant': 'assistant',
    'gpt': 'assistant',
    'AI': 'assistant',
}


pretrain_reflection_datasets = [
    #
    # reflection
    #
    # 4.17 MB, 1,000
    {'kind': 'instruct', 'path': 'dvilasuero/reflection-v1-gpt-4o-judge', 'transform': lambda r: [
        {'role': 'system', 'content': r['system']},
        {'role': 'user', 'content': r['prompt']},
        {'role': 'assistant', 'content': r['response']},
    ]},
    # 12.4 MB, 3,000
    {'kind': 'instruct', 'path': 'dvilasuero/reflection-v1-openai-o-mini-judge', 'transform': lambda r: [
        {'role': 'system', 'content': r['system']},
        {'role': 'user', 'content': r['prompt']},
        {'role': 'assistant', 'content': r['response']},
    ]},
    # 70.8 MB, 36,549
    {'kind': 'instruct', 'path': 'dvilasuero/reflection-v1-final-dedup', 'transform': lambda r: [
        {'role': 'system', 'content': r['system']},
        {'role': 'user', 'content': r['prompt']},
        {'role': 'assistant', 'content': r['response']},
    ]},
    # 30.6 MB, 25,391
    {'kind': 'instruct', 'path': 'flozi00/reflection-qwen2.5-72b-260924', 'transform': lambda r: [
        r['system'][0],
        {'role': 'user', 'content': r['input']},
        {'role': 'assistant', 'content': r['reflection'] + '\n' + r['output']},
    ]},
]