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

Libraries

How to use KnutJaegersberg/gpt2-chatbot with Transformers:

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

pipe = pipeline("text-generation", model="KnutJaegersberg/gpt2-chatbot")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("KnutJaegersberg/gpt2-chatbot")
model = AutoModelForCausalLM.from_pretrained("KnutJaegersberg/gpt2-chatbot")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use KnutJaegersberg/gpt2-chatbot with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/KnutJaegersberg/gpt2-chatbot

SGLang

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

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

GPT2-XL SFT on Deita dataset to change Sams mind. Supports multi-turn dialogue within the range of its capabilities.

Prompt Example:

### System:
You are an AI assistant. User will give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.
### User: 
How do you fine tune a large language model? 
### Assistant:

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	33.91
AI2 Reasoning Challenge (25-Shot)	29.69
HellaSwag (10-Shot)	50.27
MMLU (5-Shot)	26.42
TruthfulQA (0-shot)	40.38
Winogrande (5-shot)	56.67
GSM8k (5-shot)	0.00

Downloads last month: 82

Safetensors

Model size

2B params

Tensor type

F32

Model tree for KnutJaegersberg/gpt2-chatbot

Quantizations

1 model

Dataset used to train KnutJaegersberg/gpt2-chatbot

Spaces using KnutJaegersberg/gpt2-chatbot 3

Evaluation results

normalized accuracy on AI2 Reasoning Challenge (25-Shot)
test set Open LLM Leaderboard

29.690
normalized accuracy on HellaSwag (10-Shot)
validation set Open LLM Leaderboard

50.270
accuracy on MMLU (5-Shot)
test set Open LLM Leaderboard

26.420
mc2 on TruthfulQA (0-shot)
validation set Open LLM Leaderboard

40.380
accuracy on Winogrande (5-shot)
validation set Open LLM Leaderboard

56.670
accuracy on GSM8k (5-shot)
test set Open LLM Leaderboard

0.000