Instructions to use LuangMV97/DialoGPT_EmpAI_final2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LuangMV97/DialoGPT_EmpAI_final2 with Transformers:

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

pipe = pipeline("text-generation", model="LuangMV97/DialoGPT_EmpAI_final2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LuangMV97/DialoGPT_EmpAI_final2")
model = AutoModelForCausalLM.from_pretrained("LuangMV97/DialoGPT_EmpAI_final2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LuangMV97/DialoGPT_EmpAI_final2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LuangMV97/DialoGPT_EmpAI_final2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LuangMV97/DialoGPT_EmpAI_final2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/LuangMV97/DialoGPT_EmpAI_final2

SGLang

How to use LuangMV97/DialoGPT_EmpAI_final2 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 "LuangMV97/DialoGPT_EmpAI_final2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LuangMV97/DialoGPT_EmpAI_final2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "LuangMV97/DialoGPT_EmpAI_final2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LuangMV97/DialoGPT_EmpAI_final2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use LuangMV97/DialoGPT_EmpAI_final2 with Docker Model Runner:
```
docker model run hf.co/LuangMV97/DialoGPT_EmpAI_final2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

DialoGPT_EmpAI_final2

This model is a fine-tuned version of microsoft/DialoGPT-small on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 2.7547

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 0.0001
train_batch_size: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 64
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 25000
num_epochs: 15
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
4.5766	1.0	1500	3.9055
3.6326	2.0	3000	3.3553
3.3738	3.0	4500	3.1776
3.2193	4.0	6000	3.0785
3.1067	5.0	7500	3.0097
3.0286	6.0	9000	2.9714
2.961	7.0	10500	2.9407
2.8925	8.0	12000	2.9111
2.8291	9.0	13500	2.8815
2.7617	10.0	15000	2.8577
2.7061	11.0	16500	2.8126
2.6515	12.0	18000	2.7981
2.6165	13.0	19500	2.7822
2.5813	14.0	21000	2.7689
2.5213	15.0	22500	2.7547

Framework versions

Transformers 4.38.2
Pytorch 2.2.1+cu121
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 3

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for LuangMV97/DialoGPT_EmpAI_final2

Base model

microsoft/DialoGPT-small

Finetuned

(52)

this model

Quantizations

2 models