Instructions to use tangledgroup/tangled-alpha-0.12-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tangledgroup/tangled-alpha-0.12-core with Transformers:

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

pipe = pipeline("text-generation", model="tangledgroup/tangled-alpha-0.12-core")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tangledgroup/tangled-alpha-0.12-core", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tangledgroup/tangled-alpha-0.12-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-0.12-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-0.12-core",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tangledgroup/tangled-alpha-0.12-core

SGLang

How to use tangledgroup/tangled-alpha-0.12-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-0.12-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-0.12-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-0.12-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-0.12-core",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tangledgroup/tangled-alpha-0.12-core with Docker Model Runner:
```
docker model run hf.co/tangledgroup/tangled-alpha-0.12-core
```

mtasic85 commited on Apr 6, 2025

Commit

2f59073

1 Parent(s): 24a69e8

pretrain

Browse files

Files changed (1) hide show

scripts/base_instruct_datasets.py +7 -2

scripts/base_instruct_datasets.py CHANGED Viewed

@@ -38,9 +38,14 @@ base_instruct_datasets = [
     ]},
     # 21.1 MB, 1,000
-    {'kind': 'instruct', 'path': 'simplescaling/s1K-1.1', 'split': 'train', 'transform': lambda r: [
         {'role': 'system', 'content': R1_SYSTEM_PROMPT},
         {'role': 'user', 'content': r.get('question') or ''},
         {'role': 'assistant', 'content': '<think>\n' + (r.get('deepseek_thinking_trajectory') or '') + '\n</think>\n' + (r.get('solution') or '')},
-    ]}
 ]

     ]},
     # 21.1 MB, 1,000
+    {'kind': 'instruct', 'path': 'simplescaling/s1K-1.1', 'split': 'train[0%:50%]', 'transform': lambda r: [
         {'role': 'system', 'content': R1_SYSTEM_PROMPT},
         {'role': 'user', 'content': r.get('question') or ''},
         {'role': 'assistant', 'content': '<think>\n' + (r.get('deepseek_thinking_trajectory') or '') + '\n</think>\n' + (r.get('solution') or '')},
+    ]},
+    {'kind': 'instruct', 'path': 'simplescaling/s1K-1.1', 'split': 'train[50%:100%]', 'transform': lambda r: [
+        {'role': 'system', 'content': R1_SYSTEM_PROMPT},
+        {'role': 'user', 'content': r.get('question') or ''},
+        {'role': 'assistant', 'content': '<question>\n' + (r.get('question') or '') + '\n</question>\n<think>\n' + (r.get('deepseek_thinking_trajectory') or '') + '\n</think>\n<answer>\n' + (r.get('solution') or '') + '\n</answer>'},
+    ]},
 ]