Instructions to use ashokdevatwal/tractorgpt-gpt2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ashokdevatwal/tractorgpt-gpt2 with Transformers:

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

pipe = pipeline("text-generation", model="ashokdevatwal/tractorgpt-gpt2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ashokdevatwal/tractorgpt-gpt2")
model = AutoModelForCausalLM.from_pretrained("ashokdevatwal/tractorgpt-gpt2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ashokdevatwal/tractorgpt-gpt2 with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ashokdevatwal/tractorgpt-gpt2

SGLang

How to use ashokdevatwal/tractorgpt-gpt2 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 "ashokdevatwal/tractorgpt-gpt2" \
    --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": "ashokdevatwal/tractorgpt-gpt2",
		"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 "ashokdevatwal/tractorgpt-gpt2" \
        --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": "ashokdevatwal/tractorgpt-gpt2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ashokdevatwal/tractorgpt-gpt2 with Docker Model Runner:
```
docker model run hf.co/ashokdevatwal/tractorgpt-gpt2
```

TractorGPT (GPT-2)

Fine-tuned GPT-2 for tractor Q&A style text (TractorJunction-style data).

Intended use

Continue / polish answers in a Question: … / Answer: format.
Best used with retrieval over a tractor knowledge base.
Standalone generation may hallucinate prices or specs — prefer grounded retrieval for facts.

Load

from transformers import GPT2LMHeadModel, GPT2Tokenizer

repo = "ashokdevatwal/tractorgpt-gpt2"
tokenizer = GPT2Tokenizer.from_pretrained(repo)
model = GPT2LMHeadModel.from_pretrained(repo)
tokenizer.pad_token = tokenizer.eos_token

Training

Base: gpt2
Objective: causal LM on paraphrased tractor Q&A
See project config/model_config.json and scripts/train_gpt.py

Downloads last month: 17

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for ashokdevatwal/tractorgpt-gpt2

Base model

openai-community/gpt2

Finetuned

(2250)

this model