Instructions to use leviviya/my_eli5_clm-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use leviviya/my_eli5_clm-model with Transformers:

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

pipe = pipeline("text-generation", model="leviviya/my_eli5_clm-model")

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

tokenizer = AutoTokenizer.from_pretrained("leviviya/my_eli5_clm-model")
model = AutoModelForCausalLM.from_pretrained("leviviya/my_eli5_clm-model", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use leviviya/my_eli5_clm-model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "leviviya/my_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": "leviviya/my_eli5_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/leviviya/my_eli5_clm-model

SGLang

How to use leviviya/my_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 "leviviya/my_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": "leviviya/my_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 "leviviya/my_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": "leviviya/my_eli5_clm-model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use leviviya/my_eli5_clm-model with Docker Model Runner:
```
docker model run hf.co/leviviya/my_eli5_clm-model
```

my_eli5_clm-model / README.md

leviviya

Update README.md

9b751d5 verified about 1 year ago

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	---
	library_name: transformers
	license: apache-2.0
	base_model: distilbert/distilgpt2
	tags:
	- generated_from_trainer
	model-index:
	- name: my_eli5_clm-model
	results: []
	datasets:
	- dany0407/eli5_category
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# my_eli5_clm-model

	This model is a fine-tuned version of [distilbert/distilgpt2](https://huggingface.co/distilbert/distilgpt2) on dany0407/eli5_category dataset.
	It achieves the following results on the evaluation set:
	- Loss: 3.8209

	## 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: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- num_epochs: 3.0

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| 3.916 \| 1.0 \| 1302 \| 3.8310 \|
	\| 3.8195 \| 2.0 \| 2604 \| 3.8218 \|
	\| 3.7851 \| 3.0 \| 3906 \| 3.8209 \|


	### Framework versions

	- Transformers 4.51.3
	- Pytorch 2.7.0+cu126
	- Datasets 3.5.0
	- Tokenizers 0.21.1