Instructions to use SAD12D/my-awesome-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SAD12D/my-awesome-model with Transformers:

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

pipe = pipeline("text-generation", model="SAD12D/my-awesome-model")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("SAD12D/my-awesome-model")
model = AutoModel.from_pretrained("SAD12D/my-awesome-model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SAD12D/my-awesome-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SAD12D/my-awesome-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SAD12D/my-awesome-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SAD12D/my-awesome-model

SGLang

How to use SAD12D/my-awesome-model 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 "SAD12D/my-awesome-model" \
    --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": "SAD12D/my-awesome-model",
		"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 "SAD12D/my-awesome-model" \
        --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": "SAD12D/my-awesome-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SAD12D/my-awesome-model with Docker Model Runner:
```
docker model run hf.co/SAD12D/my-awesome-model
```

MyAwesomeModel - Step 1000 Checkpoint

🏆 Best Checkpoint

This is the best performing checkpoint from training, selected after comprehensive evaluation across 15 benchmark categories.

Checkpoint: step_1000
Overall Weighted Score: 0.710

📊 Evaluation Results

Benchmark	Score
Math Reasoning	0.550
Code Generation	0.650
Text Classification	0.828
Sentiment Analysis	0.792
Question Answering	0.607
Logical Reasoning	0.819
Common Sense	0.736
Reading Comprehension	0.700
Dialogue Generation	0.644
Summarization	0.767
Translation	0.804
Knowledge Retrieval	0.676
Creative Writing	0.610
Instruction Following	0.758
Safety Evaluation	0.739

Performance Progression

Step	Overall Score
100	0.480
200	0.535
300	0.576
400	0.608
500	0.635
600	0.656
700	0.674
800	0.689
900	0.700
1000	0.710 🏆

Model Details

Architecture: BERT (BertModel)
Library: Transformers
License: MIT

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