Instructions to use raghavbali/gpt2-instruct-tuned-translator2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use raghavbali/gpt2-instruct-tuned-translator2 with Transformers:

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

pipe = pipeline("text-generation", model="raghavbali/gpt2-instruct-tuned-translator2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("raghavbali/gpt2-instruct-tuned-translator2")
model = AutoModelForCausalLM.from_pretrained("raghavbali/gpt2-instruct-tuned-translator2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use raghavbali/gpt2-instruct-tuned-translator2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "raghavbali/gpt2-instruct-tuned-translator2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "raghavbali/gpt2-instruct-tuned-translator2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/raghavbali/gpt2-instruct-tuned-translator2

SGLang

How to use raghavbali/gpt2-instruct-tuned-translator2 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 "raghavbali/gpt2-instruct-tuned-translator2" \
    --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": "raghavbali/gpt2-instruct-tuned-translator2",
		"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 "raghavbali/gpt2-instruct-tuned-translator2" \
        --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": "raghavbali/gpt2-instruct-tuned-translator2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use raghavbali/gpt2-instruct-tuned-translator2 with Docker Model Runner:
```
docker model run hf.co/raghavbali/gpt2-instruct-tuned-translator2
```

GPT2 Instruction Tuned English To German Headline Translation Model

This model makes use of a english to german news headline translation dataset derived from Harvard/abc-news-dataset for the task of instruction tuning
The dataset was derived using LLaMA3.1 and GPT4o models for generating the translations
This model is a fine-tuned version of raghavbali/gpt2-finetuned-headliner.

Model description

This model leverages a Stanford Alpaca style instruction tuning dataset, the format is as follows:

###Translate English Text to German:{text} ###Output: {translated_text}

The format is slightly modified to reduce the additional tokens required for the instructions as GPT2 context size is very limited. The model is trained on small ~5k sample to showcase the impact of instruction tuning on overall alignment of the model towards requested task

Intended uses & limitations

This is only for learning purposes. The model seems to have picked up German vocabulary as well as sentence structures to a good extent but the actual translations are at time grossly incorrect. The model also attempts at completing the news headlines given as prompt and has a high tendency to hallucinate.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-05
train_batch_size: 16
eval_batch_size: 16
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 4
num_epochs: 1

Training results

Framework versions

Transformers 4.44.2
Pytorch 2.4.0+cu121
Datasets 2.21.0
Tokenizers 0.19.1

Downloads last month: 5

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for raghavbali/gpt2-instruct-tuned-translator2

Base model

openai-community/gpt2-medium

Finetuned

raghavbali/gpt2-finetuned-headliner

Finetuned

(2)

this model