Instructions to use MSey/tiny_BROLLLT_0001.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MSey/tiny_BROLLLT_0001.1 with Transformers:

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

pipe = pipeline("text-generation", model="MSey/tiny_BROLLLT_0001.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MSey/tiny_BROLLLT_0001.1")
model = AutoModelForCausalLM.from_pretrained("MSey/tiny_BROLLLT_0001.1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use MSey/tiny_BROLLLT_0001.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MSey/tiny_BROLLLT_0001.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MSey/tiny_BROLLLT_0001.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/MSey/tiny_BROLLLT_0001.1

SGLang

How to use MSey/tiny_BROLLLT_0001.1 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 "MSey/tiny_BROLLLT_0001.1" \
    --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": "MSey/tiny_BROLLLT_0001.1",
		"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 "MSey/tiny_BROLLLT_0001.1" \
        --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": "MSey/tiny_BROLLLT_0001.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MSey/tiny_BROLLLT_0001.1 with Docker Model Runner:
```
docker model run hf.co/MSey/tiny_BROLLLT_0001.1
```

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tiny Llama trained on BRO dataset with NER tags, Labels and Tokens.

WCETrainer r100_O10_f100 , run lemon-fog-11 checkpoint-1623.

EVAL

AVGf1 = 93%, overall_f1 = 82%

TEST

'DIAG': {'precision': 0.710079275198188, 'recall': 0.7674418604651163, 'f1': 0.7376470588235293, 'number': 817},

'MED': {'precision': 0.9379084967320261, 'recall': 0.959866220735786, 'f1': 0.9487603305785124, 'number': 299},

'TREAT': {'precision': 0.8542914171656687, 'recall': 0.856, 'f1': 0.8551448551448552, 'number': 500},

'overall_precision': 0.7672955974842768, 'overall_recall': 0.8304455445544554, 'overall_f1': 0.7976225854383358, 'overall_accuracy': 0.9280119624038735}

average_f1 = 0.8471840815156323

Prompt Format (see example):

### Context\n{Nachricht}\n\n### Answer

def context_text(text): return f"### Context\n{text}\n\n### Answer"

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Safetensors

Model size

1B params

Tensor type

F32

Collection including MSey/tiny_BROLLLT_0001.1

tinyBROLL

Collection

1 item • Updated Jul 29, 2024