Instructions to use Prajna1999/Prajna-gpt-neo-1.3B-fitbot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Prajna1999/Prajna-gpt-neo-1.3B-fitbot with Transformers:

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

pipe = pipeline("text-generation", model="Prajna1999/Prajna-gpt-neo-1.3B-fitbot")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Prajna1999/Prajna-gpt-neo-1.3B-fitbot")
model = AutoModelForCausalLM.from_pretrained("Prajna1999/Prajna-gpt-neo-1.3B-fitbot", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Prajna1999/Prajna-gpt-neo-1.3B-fitbot with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Prajna1999/Prajna-gpt-neo-1.3B-fitbot"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Prajna1999/Prajna-gpt-neo-1.3B-fitbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Prajna1999/Prajna-gpt-neo-1.3B-fitbot

SGLang

How to use Prajna1999/Prajna-gpt-neo-1.3B-fitbot 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 "Prajna1999/Prajna-gpt-neo-1.3B-fitbot" \
    --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": "Prajna1999/Prajna-gpt-neo-1.3B-fitbot",
		"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 "Prajna1999/Prajna-gpt-neo-1.3B-fitbot" \
        --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": "Prajna1999/Prajna-gpt-neo-1.3B-fitbot",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Prajna1999/Prajna-gpt-neo-1.3B-fitbot with Docker Model Runner:
```
docker model run hf.co/Prajna1999/Prajna-gpt-neo-1.3B-fitbot
```

LoRA +Finetuned with 50 pairs of GPT-3.5-turbo generated workout QA pair on T4 Google Collab using GPT-neo-1.3B base model. Finetuned on synthetic data.

Collab Link https://colab.research.google.com/drive/12uv_PocrcDmvOhjPD9SGAqcXbpZAZh2a?authuser=2#scrollTo=PNcc7C51VWHV

How to use the model

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Prajna1999/Prajna-gpt-neo-1.3B-fitbot") model = AutoModelForCausalLM.from_pretrained("Prajna1999/Prajna-gpt-neo-1.3B-fitbot")

input_ids = tokenizer.encode("Suggest some workouts for weight loss", return_tensors="pt") output = model.generate(input_ids, max_length=128, temperature=0, top_p=0.7, top_k=2) output_text = tokenizer.decode(output[0], skip_special_tokens=True)

print(output_text)

Downloads last month: 5