Instructions to use tangledgroup/tangled-alpha-0.10-core with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tangledgroup/tangled-alpha-0.10-core with Transformers:

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

pipe = pipeline("text-generation", model="tangledgroup/tangled-alpha-0.10-core")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("tangledgroup/tangled-alpha-0.10-core", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tangledgroup/tangled-alpha-0.10-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-0.10-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-0.10-core",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tangledgroup/tangled-alpha-0.10-core

SGLang

How to use tangledgroup/tangled-alpha-0.10-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-0.10-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-0.10-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-0.10-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-0.10-core",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tangledgroup/tangled-alpha-0.10-core with Docker Model Runner:
```
docker model run hf.co/tangledgroup/tangled-alpha-0.10-core
```

mtasic85 commited on Mar 28, 2025

Commit

72813f2

1 Parent(s): 9fbd906

pretrain core 1

Browse files

Files changed (2) hide show

README.md +4 -0
scripts/pretrain_core_model_1.yaml +1 -1

README.md CHANGED Viewed

@@ -197,3 +197,7 @@ Tasks                           |Version|Filter|n-shot|        Metric         |
 ```bash
 litgpt convert_pretrained_checkpoint ../out/pretrain-core-0/final ../out/pretrain-core-0/checkpoint
 ```

 ```bash
 litgpt convert_pretrained_checkpoint ../out/pretrain-core-0/final ../out/pretrain-core-0/checkpoint
 ```
+```bash
+CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_core_model_1.yaml
+```

scripts/pretrain_core_model_1.yaml CHANGED Viewed

@@ -85,7 +85,7 @@ train:
   max_norm: 1.0
   #   (type: float, default: 4e-05)
-  min_lr: 1e-6
 # Evaluation-related arguments. See ``litgpt.args.EvalArgs`` for details
 eval:

   max_norm: 1.0
   #   (type: float, default: 4e-05)
+  min_lr: 1e-5
 # Evaluation-related arguments. See ``litgpt.args.EvalArgs`` for details
 eval: