Instructions to use postbot/gpt2-medium-emailgen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use postbot/gpt2-medium-emailgen with Transformers:

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

pipe = pipeline("text-generation", model="postbot/gpt2-medium-emailgen")

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

tokenizer = AutoTokenizer.from_pretrained("postbot/gpt2-medium-emailgen")
model = AutoModelForCausalLM.from_pretrained("postbot/gpt2-medium-emailgen", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use postbot/gpt2-medium-emailgen with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "postbot/gpt2-medium-emailgen"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "postbot/gpt2-medium-emailgen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/postbot/gpt2-medium-emailgen

SGLang

How to use postbot/gpt2-medium-emailgen 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 "postbot/gpt2-medium-emailgen" \
    --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": "postbot/gpt2-medium-emailgen",
		"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 "postbot/gpt2-medium-emailgen" \
        --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": "postbot/gpt2-medium-emailgen",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use postbot/gpt2-medium-emailgen with Docker Model Runner:
```
docker model run hf.co/postbot/gpt2-medium-emailgen
```

pszemraj commited on Sep 29, 2022

Commit

ef0b9de

1 Parent(s): de4b817

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@@ -1,16 +1,50 @@
 ---
 license: mit
 tags:
 - generated_from_trainer
-model-index:
-- name: gpt2-medium-emailgen_DS-multi-clean-100k_Ep-3_Bs-16
-  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. -->
-# gpt2-medium-emailgen_DS-multi-clean-100k_Ep-3_Bs-16
 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the None dataset.
 It achieves the following results on the evaluation set:

 ---
 license: mit
 tags:
+- text generation
 - generated_from_trainer
+- email generation
+- email
+datasets:
+- aeslc
+- postbot/multi-emails-100k
+widget:
+- text: "Good Morning Professor Beans,
+Hope you are doing well. I just wanted to reach out and ask if differential calculus will be on the exam"
+  example_title: "email to prof"
+- text: "Hey <NAME>,\n\nThank you for signing up for my weekly newsletter. Before we get started, you'll have to confirm your email address."
+  example_title: "newsletter"
+- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and ask about office hours"
+  example_title: "office hours"
+- text: "Greetings <NAME>,\n\nI hope you had a splendid evening at the Company sausage eating festival. I am reaching out because"
+  example_title: "festival"
+- text: "Good Morning Harold,\n\nI was wondering when the next"
+  example_title: "event"
+- text: "URGENT - I need the TPS reports"
+  example_title: "URGENT"
+- text: "Hi Archibald,\n\nI hope this email finds you extremely well."
+  example_title: "emails that find you"
+- text: "Hello there.\n\nI just wanted to reach out and check in to"
+  example_title: "checking in"
+- text: "Hello <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if you've enjoyed your time with us"
+  example_title: "work well"
+- text: "Hi <NAME>,\n\nI hope this email finds you well. I wanted to reach out and see if we could catch up"
+  example_title: "catch up"
+- text: "I'm <NAME> and I just moved into the area and wanted to reach out and get some details on where I could get groceries and"
+  example_title: "grocery"
+parameters:
+  min_length: 4
+  max_length: 128
+  length_penalty: 0.8
+  no_repeat_ngram_size: 2
+  do_sample: True
+  repetition_penalty: 2.5
 ---
+# gpt2-medium-emailgen
 This model is a fine-tuned version of [gpt2-medium](https://huggingface.co/gpt2-medium) on the None dataset.
 It achieves the following results on the evaluation set: