Instructions to use Roaoch/CyberClassic-Generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Roaoch/CyberClassic-Generator with Transformers:

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

pipe = pipeline("text-generation", model="Roaoch/CyberClassic-Generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Roaoch/CyberClassic-Generator")
model = AutoModelForCausalLM.from_pretrained("Roaoch/CyberClassic-Generator")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Roaoch/CyberClassic-Generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Roaoch/CyberClassic-Generator

SGLang

How to use Roaoch/CyberClassic-Generator 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 "Roaoch/CyberClassic-Generator" \
    --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": "Roaoch/CyberClassic-Generator",
		"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 "Roaoch/CyberClassic-Generator" \
        --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": "Roaoch/CyberClassic-Generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Roaoch/CyberClassic-Generator with Docker Model Runner:
```
docker model run hf.co/Roaoch/CyberClassic-Generator
```

Roaoch commited on Jul 1, 2024

Commit

0cb477c

verified ·

1 Parent(s): 0e9eab5

Update README.md

Browse files

Files changed (1) hide show

README.md +29 -1

README.md CHANGED Viewed

@@ -8,4 +8,32 @@ metrics:
 - rouge
 library_name: transformers
 pipeline_tag: text-generation
----

 - rouge
 library_name: transformers
 pipeline_tag: text-generation
+---
+This text generator is based on OpenAI GPT2 model from HuggingFace
+Base model went through two step of learning
+## First - Finetining of base model
+On this step model is finetuned on dataset of single sentence from the texts of Dostovesky F.M.
+Training parameters:
+* Epoch = 10
+* Learning Rate = 1e-3
+* Optimizer = AdamW
+* Scheduler = OneCycleLR
+* Training env = PyTorch
+![image.png](https://cdn-uploads.huggingface.co/production/uploads/645ced0dce72244df7b0f6d2/Mvp2ouBpVkK8t4CfuBvx7.png)
+![image.png](https://cdn-uploads.huggingface.co/production/uploads/645ced0dce72244df7b0f6d2/_AiPUPQOi5-raYSyZNohh.png)
+## Second - RL
+On this step finetuned model went trough reinforcement learning pipline with TRL library.
+Training parameters:
+* Epoch = 30
+* Trainer = PPO
+* Query texts = first 100 texts from dataset, trimmed by first 3 words
+* Reward = score from [binary classifier](https://huggingface.co/Roaoch/CyberClassic-Discriminator) multiplied by 10
+![image.png](https://cdn-uploads.huggingface.co/production/uploads/645ced0dce72244df7b0f6d2/Mv_8ZAfml6tPcdXBOXcQG.png)
+![image.png](https://cdn-uploads.huggingface.co/production/uploads/645ced0dce72244df7b0f6d2/ZbeI0Tq6eSy-_Tl_gK-sz.png)