Instructions to use arampacha/gpt-neo-therapist-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arampacha/gpt-neo-therapist-small with Transformers:

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

pipe = pipeline("text-generation", model="arampacha/gpt-neo-therapist-small")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arampacha/gpt-neo-therapist-small")
model = AutoModelForCausalLM.from_pretrained("arampacha/gpt-neo-therapist-small")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arampacha/gpt-neo-therapist-small with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/arampacha/gpt-neo-therapist-small

SGLang

How to use arampacha/gpt-neo-therapist-small 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 "arampacha/gpt-neo-therapist-small" \
    --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": "arampacha/gpt-neo-therapist-small",
		"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 "arampacha/gpt-neo-therapist-small" \
        --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": "arampacha/gpt-neo-therapist-small",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use arampacha/gpt-neo-therapist-small with Docker Model Runner:
```
docker model run hf.co/arampacha/gpt-neo-therapist-small
```

gpt-neo-therapist-small

This model is a fine-tuned version of EleutherAI/gpt-neo-125M on the None dataset. It achieves the following results on the evaluation set:

Loss: 4.6731
Rouge1: 39.5028
Rouge2: 6.43
Rougel: 24.0091
Rougelsum: 35.4481
Gen Len: 204.1329

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: 0.0001
train_batch_size: 4
eval_batch_size: 4
seed: 24
gradient_accumulation_steps: 64
total_train_batch_size: 256
optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 5
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Rouge1	Rouge2	Rougel	Rougelsum	Gen Len
9.9955	0.97	7	6.8195	18.6047	1.0194	14.8565	17.9774	212.0983
6.9729	1.97	14	5.6783	26.3789	3.0779	18.5195	24.8592	203.0925
5.2614	2.97	21	5.0506	34.9428	4.921	21.9741	32.1122	206.2775
5.0599	3.97	28	4.7372	38.5235	6.2251	23.5923	34.5633	204.2428
4.5479	4.97	35	4.6731	39.5028	6.43	24.0091	35.4481	204.1329

Framework versions

Transformers 4.17.0
Pytorch 1.10.0+cu111
Datasets 2.0.0
Tokenizers 0.11.6

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