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

Libraries

How to use lgaalves/gpt2-dolly with Transformers:

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

pipe = pipeline("text-generation", model="lgaalves/gpt2-dolly")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lgaalves/gpt2-dolly")
model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2-dolly")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lgaalves/gpt2-dolly with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lgaalves/gpt2-dolly

SGLang

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

Docker Model Runner
How to use lgaalves/gpt2-dolly with Docker Model Runner:
```
docker model run hf.co/lgaalves/gpt2-dolly
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GPT-2-dolly

GPT-2-dolly is an instruction fine-tuned model based on the GPT-2 transformer architecture.

Benchmark Metrics

Metric	GPT-2-dolly	GPT-2 (base)
Avg.	30.91	29.99
ARC (25-shot)	22.70	21.84
HellaSwag (10-shot)	30.15	31.6
MMLU (5-shot)	25.81	25.86
TruthfulQA (0-shot)	44.97	40.67

We use state-of-the-art Language Model Evaluation Harness to run the benchmark tests above, using the same version as the HuggingFace LLM Leaderboard. Please see below for detailed instructions on reproducing benchmark results.

Model Details

Trained by: Luiz G A Alves
Model type: GPT-2-dolly is an auto-regressive language model based on the GPT-2 transformer architecture.
Language(s): English

How to use:

# Use a pipeline as a high-level helper
>>> from transformers import pipeline
>>> pipe = pipeline("text-generation", model="lgaalves/gpt2-dolly")
>>> question = "What is a large language model?"
>>> answer = pipe(question)
>>> print(answer[0]['generated_text'])

or, you can load the model direclty using:

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lgaalves/gpt2-dolly")
model = AutoModelForCausalLM.from_pretrained("lgaalves/gpt2-dolly")

Training Dataset

lgaalves/gpt2-dolly trained using the Databricks Dolly dataset databricks/databricks-dolly-15k.

Training Procedure

lgaalves/gpt2-dolly was instruction fine-tuned using LoRA on 1 T4 GPU on Google Colab. It took about 1.5 hours to train it.

Intended uses, limitations & biases

You can use the raw model for text generation or fine-tune it to a downstream task. The model was not extensively tested and may produce false information. It contains a lot of unfiltered content from the internet, which is far from neutral.

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	25.53
ARC (25-shot)	22.7
HellaSwag (10-shot)	30.15
MMLU (5-shot)	25.81
TruthfulQA (0-shot)	44.97
Winogrande (5-shot)	51.46
GSM8K (5-shot)	0.15
DROP (3-shot)	3.45

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Safetensors

Model size

0.1B params

Tensor type

F16

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Quantizations

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