Instructions to use ZenPuzzle/my_awesome_eli5_clm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ZenPuzzle/my_awesome_eli5_clm-model with Transformers:

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

pipe = pipeline("text-generation", model="ZenPuzzle/my_awesome_eli5_clm-model")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ZenPuzzle/my_awesome_eli5_clm-model")
model = AutoModelForCausalLM.from_pretrained("ZenPuzzle/my_awesome_eli5_clm-model")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ZenPuzzle/my_awesome_eli5_clm-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZenPuzzle/my_awesome_eli5_clm-model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ZenPuzzle/my_awesome_eli5_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ZenPuzzle/my_awesome_eli5_clm-model

SGLang

How to use ZenPuzzle/my_awesome_eli5_clm-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 "ZenPuzzle/my_awesome_eli5_clm-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": "ZenPuzzle/my_awesome_eli5_clm-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 "ZenPuzzle/my_awesome_eli5_clm-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": "ZenPuzzle/my_awesome_eli5_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ZenPuzzle/my_awesome_eli5_clm-model with Docker Model Runner:
```
docker model run hf.co/ZenPuzzle/my_awesome_eli5_clm-model
```

my_awesome_eli5_clm-model

This model is a fine-tuned version of ai-forever/rugpt3small_based_on_gpt2 on the None dataset. It achieves the following results on the evaluation set:

Loss: 2.7020

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss
No log	1.0	5	3.7450
No log	2.0	10	3.4688
No log	3.0	15	3.2667
No log	4.0	20	3.1067
No log	5.0	25	2.9798
No log	6.0	30	2.8797
No log	7.0	35	2.8035
No log	8.0	40	2.7485
No log	9.0	45	2.7147
No log	10.0	50	2.7020

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu117
Datasets 2.13.1
Tokenizers 0.13.3

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