Instructions to use jploski/mpt-mini-shakespeare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jploski/mpt-mini-shakespeare with Transformers:

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

pipe = pipeline("text-generation", model="jploski/mpt-mini-shakespeare", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("jploski/mpt-mini-shakespeare", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jploski/mpt-mini-shakespeare", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use jploski/mpt-mini-shakespeare with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/jploski/mpt-mini-shakespeare

SGLang

How to use jploski/mpt-mini-shakespeare 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 "jploski/mpt-mini-shakespeare" \
    --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": "jploski/mpt-mini-shakespeare",
		"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 "jploski/mpt-mini-shakespeare" \
        --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": "jploski/mpt-mini-shakespeare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jploski/mpt-mini-shakespeare with Docker Model Runner:
```
docker model run hf.co/jploski/mpt-mini-shakespeare
```

jploski commited on May 13, 2023

Commit

8d09300

1 Parent(s): d7c4243

Added link to Colab notebook used for model training

Browse files

Files changed (1) hide show

README.md +5 -3

README.md CHANGED Viewed

@@ -11,7 +11,7 @@ should probably proofread and complete it, then remove this comment. -->
 # mpt-mini-shakespeare
-This model was trained from scratch on https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt.
 ## Model description
@@ -23,11 +23,13 @@ Intended just to aid debugging efforts of a GGML port of mpt-7b-storywriter.
 ## Training and evaluation data
-More information needed
 ## Training procedure
-Just use the single tinyshakespeare text file as both training and validation set (splitting into paragraphs).
 ### Training hyperparameters

 # mpt-mini-shakespeare
+This model was trained from scratch on "tinyshakespeare" text file.
 ## Model description
 ## Training and evaluation data
+https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt
 ## Training procedure
+Just used the single tinyshakespeare text file as both the training and validation set (split up into paragraphs). See:
+https://colab.research.google.com/drive/19tKIegIr0IThbItQnY2m7Y7B6AKbz6Cw?usp=sharing
 ### Training hyperparameters