Instructions to use nlpproj2026/sft_opt1.3b_hh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nlpproj2026/sft_opt1.3b_hh with Transformers:

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

pipe = pipeline("text-generation", model="nlpproj2026/sft_opt1.3b_hh")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nlpproj2026/sft_opt1.3b_hh")
model = AutoModelForCausalLM.from_pretrained("nlpproj2026/sft_opt1.3b_hh")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nlpproj2026/sft_opt1.3b_hh with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nlpproj2026/sft_opt1.3b_hh"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nlpproj2026/sft_opt1.3b_hh",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/nlpproj2026/sft_opt1.3b_hh

SGLang

How to use nlpproj2026/sft_opt1.3b_hh 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 "nlpproj2026/sft_opt1.3b_hh" \
    --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": "nlpproj2026/sft_opt1.3b_hh",
		"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 "nlpproj2026/sft_opt1.3b_hh" \
        --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": "nlpproj2026/sft_opt1.3b_hh",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use nlpproj2026/sft_opt1.3b_hh with Docker Model Runner:
```
docker model run hf.co/nlpproj2026/sft_opt1.3b_hh
```

Model Card for sft_opt350m_hh

This model is a fine-tuned version of facebook/opt-1.3b. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

TRL: 1.5.1
Transformers: 5.9.0
Pytorch: 2.12.0
Datasets: 4.8.5
Tokenizers: 0.22.2

Citations

Cite TRL as:

@software{vonwerra2020trl,
  title   = {{TRL: Transformers Reinforcement Learning}},
  author  = {von Werra, Leandro and Belkada, Younes and Tunstall, Lewis and Beeching, Edward and Thrush, Tristan and Lambert, Nathan and Huang, Shengyi and Rasul, Kashif and Gallouédec, Quentin},
  license = {Apache-2.0},
  url     = {https://github.com/huggingface/trl},
  year    = {2020}
}

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Safetensors

Model size

1B params

Tensor type

F16

Model tree for nlpproj2026/sft_opt1.3b_hh

Base model

facebook/opt-1.3b

Finetuned

(79)

this model