Instructions to use Myashka/gpt-imdb-ipo-beta_0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Myashka/gpt-imdb-ipo-beta_0.1 with Transformers:

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

pipe = pipeline("text-generation", model="Myashka/gpt-imdb-ipo-beta_0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Myashka/gpt-imdb-ipo-beta_0.1")
model = AutoModelForCausalLM.from_pretrained("Myashka/gpt-imdb-ipo-beta_0.1")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Myashka/gpt-imdb-ipo-beta_0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Myashka/gpt-imdb-ipo-beta_0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Myashka/gpt-imdb-ipo-beta_0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Myashka/gpt-imdb-ipo-beta_0.1

SGLang

How to use Myashka/gpt-imdb-ipo-beta_0.1 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 "Myashka/gpt-imdb-ipo-beta_0.1" \
    --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": "Myashka/gpt-imdb-ipo-beta_0.1",
		"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 "Myashka/gpt-imdb-ipo-beta_0.1" \
        --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": "Myashka/gpt-imdb-ipo-beta_0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Myashka/gpt-imdb-ipo-beta_0.1 with Docker Model Runner:
```
docker model run hf.co/Myashka/gpt-imdb-ipo-beta_0.1
```

gpt-imdb-ipo-beta_0.1

This model is a fine-tuned version of lvwerra/gpt2-imdb on an unknown dataset. It achieves the following results on the evaluation set:

Step: 6500
Loss: 11.7007
Rewards/chosen: -0.0805
Rewards/rejected: -0.4417
Rewards/accuracies: 0.9000
Rewards/margins: 0.3612
Logps/rejected: -268.1027
Logps/chosen: -236.0704
Logits/rejected: -31.0790
Logits/chosen: -31.2840

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: 1e-05
train_batch_size: 24
eval_batch_size: 24
seed: 42
optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 150
training_steps: 7197

Training results

Training Loss	Epoch	Step	Validation Loss	Rewards/chosen	Rewards/rejected	Rewards/accuracies	Rewards/margins	Logps/rejected	Logps/chosen	Logits/rejected	Logits/chosen
18.812	0.21	500	29.2155	0.0458	-0.2317	0.7875	0.2775	-266.0027	-234.8074	-33.9160	-34.3504
13.7881	0.42	1000	24.1460	-0.0697	-0.3582	0.7625	0.2885	-267.2670	-235.9622	-35.0526	-35.3757
27.0047	0.63	1500	39.7182	-0.1370	-0.4692	0.7875	0.3322	-268.3775	-236.6354	-32.1933	-32.4137
19.7751	0.83	2000	40.6223	-0.0674	-0.4210	0.7729	0.3536	-267.8954	-235.9392	-31.7349	-31.9095
9.5381	1.04	2500	20.9269	-0.1155	-0.4866	0.8146	0.3712	-268.5513	-236.4198	-32.1382	-32.3448
20.3498	1.25	3000	29.2158	-0.0629	-0.4040	0.8208	0.3410	-267.7249	-235.8945	-31.7900	-32.1080
20.4018	1.46	3500	20.8452	-0.0350	-0.3582	0.8271	0.3232	-267.2670	-235.6155	-31.3911	-31.6578
17.4506	1.67	4000	16.4207	-0.1258	-0.4841	0.8438	0.3583	-268.5259	-236.5234	-31.5718	-31.7727
7.7045	1.88	4500	14.3286	-0.0659	-0.4275	0.875	0.3616	-267.9600	-235.9239	-31.3055	-31.4702
9.4274	2.08	5000	12.6249	-0.1037	-0.4565	0.8687	0.3528	-268.2499	-236.3019	-31.4025	-31.6122
7.7699	2.29	5500	12.3366	-0.0787	-0.4337	0.8708	0.3550	-268.0224	-236.0526	-30.8436	-31.0563
9.2038	2.5	6000	12.2158	-0.0882	-0.4430	0.8937	0.3548	-268.1148	-236.1471	-30.7819	-30.9884
11.4596	2.71	6500	11.7007	-0.0852	-0.4480	0.9000	0.3628	-268.1655	-236.1172	-31.0236	-31.2283
9.6351	2.92	7000	12.0082	-0.0805	-0.4417	0.8958	0.3612	-268.1027	-236.0704	-31.0790	-31.2840

Framework versions

Transformers 4.35.2
Pytorch 2.1.1
Datasets 2.15.0
Tokenizers 0.15.0

Downloads last month: 2

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for Myashka/gpt-imdb-ipo-beta_0.1

Base model

lvwerra/gpt2-imdb

Finetuned

(101)

this model