Instructions to use breadlicker45/dough-base-001 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use breadlicker45/dough-base-001 with Transformers:

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

pipe = pipeline("text-generation", model="breadlicker45/dough-base-001")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("breadlicker45/dough-base-001")
model = AutoModelForCausalLM.from_pretrained("breadlicker45/dough-base-001")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use breadlicker45/dough-base-001 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "breadlicker45/dough-base-001"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "breadlicker45/dough-base-001",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/breadlicker45/dough-base-001

SGLang

How to use breadlicker45/dough-base-001 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 "breadlicker45/dough-base-001" \
    --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": "breadlicker45/dough-base-001",
		"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 "breadlicker45/dough-base-001" \
        --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": "breadlicker45/dough-base-001",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use breadlicker45/dough-base-001 with Docker Model Runner:
```
docker model run hf.co/breadlicker45/dough-base-001
```

dough-base-001

Commit History

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Commit History

Adding Evaluation Results a6c9247

Initial commit e42b651

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Create README.md c0911d8

Upload generation_config.json b63d86a

Delete tokenizer_config.json e720ce8

Upload config.json 03008bd

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Rename tokenizer_config (5).json to tokenizer_config.json 1528f06

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Upload 2 files 16dced9

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Upload config (2).json 722be55

Rename pytorch_model (10).bin to pytorch_model.bin 70a5223

Upload pytorch_model (10).bin edbb35a

initial commit 19bc9cf

Adding Evaluation Results

a6c9247

Initial commit

e42b651

Update README.md

0188850

Create README.md

c0911d8

Upload generation_config.json

b63d86a

Delete tokenizer_config.json

e720ce8

Upload config.json

03008bd

Delete tokenizer_config (4).json

811f3d7

Rename tokenizer_config (5).json to tokenizer_config.json

1528f06

Delete tokenizer_config.json

65075fc

Upload 2 files

16dced9

Rename pytorch_model (11).bin to pytorch_model.bin

0f030bd

Delete pytorch_model.bin

da389c2

Upload 2 files

0eb0501

Rename tokenizer_config (3).json to tokenizer_config.json

fd1faca

Rename tokenizer (8).json to tokenizer.json

31c8497

Upload 2 files

6b91718

Rename config (2).json to config.json

d89710b

Upload config (2).json

722be55

Rename pytorch_model (10).bin to pytorch_model.bin

70a5223

Upload pytorch_model (10).bin

edbb35a

initial commit

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