Instructions to use ethzanalytics/distilgpt2-tiny-conversational with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ethzanalytics/distilgpt2-tiny-conversational with Transformers:

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

pipe = pipeline("text-generation", model="ethzanalytics/distilgpt2-tiny-conversational")

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

tokenizer = AutoTokenizer.from_pretrained("ethzanalytics/distilgpt2-tiny-conversational")
model = AutoModelForCausalLM.from_pretrained("ethzanalytics/distilgpt2-tiny-conversational", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ethzanalytics/distilgpt2-tiny-conversational with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ethzanalytics/distilgpt2-tiny-conversational"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ethzanalytics/distilgpt2-tiny-conversational",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ethzanalytics/distilgpt2-tiny-conversational

SGLang

How to use ethzanalytics/distilgpt2-tiny-conversational 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 "ethzanalytics/distilgpt2-tiny-conversational" \
    --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": "ethzanalytics/distilgpt2-tiny-conversational",
		"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 "ethzanalytics/distilgpt2-tiny-conversational" \
        --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": "ethzanalytics/distilgpt2-tiny-conversational",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ethzanalytics/distilgpt2-tiny-conversational with Docker Model Runner:
```
docker model run hf.co/ethzanalytics/distilgpt2-tiny-conversational
```

pszemraj commited on Jan 31, 2022

Commit

152ca25

1 Parent(s): 0afa3fd

update model card README.md

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README.md +2 -2

README.md CHANGED Viewed

@@ -3,14 +3,14 @@ license: apache-2.0
 tags:
 - generated_from_trainer
 model-index:
-- name: checkpoints
   results: []
 ---
 <!-- 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. -->
-# checkpoints
 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
 It achieves the following results on the evaluation set:

 tags:
 - generated_from_trainer
 model-index:
+- name: distilgpt2-Converse_DS-WoW_Ep-30_Bs-32
   results: []
 ---
 <!-- 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. -->
+# distilgpt2-Converse_DS-WoW_Ep-30_Bs-32
 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
 It achieves the following results on the evaluation set: