Instructions to use alpindale/pygm-350m-experimental with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alpindale/pygm-350m-experimental with Transformers:

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

pipe = pipeline("text-generation", model="alpindale/pygm-350m-experimental")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alpindale/pygm-350m-experimental")
model = AutoModelForCausalLM.from_pretrained("alpindale/pygm-350m-experimental")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use alpindale/pygm-350m-experimental with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alpindale/pygm-350m-experimental"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alpindale/pygm-350m-experimental",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/alpindale/pygm-350m-experimental

SGLang

How to use alpindale/pygm-350m-experimental 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 "alpindale/pygm-350m-experimental" \
    --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": "alpindale/pygm-350m-experimental",
		"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 "alpindale/pygm-350m-experimental" \
        --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": "alpindale/pygm-350m-experimental",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use alpindale/pygm-350m-experimental with Docker Model Runner:
```
docker model run hf.co/alpindale/pygm-350m-experimental
```

pygmalion-350m

This model is a fine-tuned version of PygmalionAI/pygmalion-350m on a 2.4MB dataset. It achieves the following results on the evaluation set:

Loss: 2.2731
Accuracy: 0.5187

Model description

A proof-of-concept model based on PygmalionAI/pygmalion-350m, which was in turn based on OPT-350m.

This model was fine-tuned purely for testing purposes.

Fine-tuning process

Fine-tuned on an A100-80GB with HF's run_clm.py script. It was run through 3 epochs with 8 batch size using 2.4MB dataset (split 75/25 between training and validation sets).

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-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: 3.0

Framework versions

Transformers 4.27.0.dev0
Pytorch 1.13.1+cu117
Datasets 2.10.0
Tokenizers 0.13.2

Downloads last month: 3

Safetensors

Model size

0.3B params

Tensor type

F32