Instructions to use DedeProGames/dialochess-v3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DedeProGames/dialochess-v3 with Transformers:

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

pipe = pipeline("text-generation", model="DedeProGames/dialochess-v3")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DedeProGames/dialochess-v3")
model = AutoModelForCausalLM.from_pretrained("DedeProGames/dialochess-v3")
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 DedeProGames/dialochess-v3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DedeProGames/dialochess-v3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "DedeProGames/dialochess-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DedeProGames/dialochess-v3

SGLang

How to use DedeProGames/dialochess-v3 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 "DedeProGames/dialochess-v3" \
    --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": "DedeProGames/dialochess-v3",
		"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 "DedeProGames/dialochess-v3" \
        --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": "DedeProGames/dialochess-v3",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DedeProGames/dialochess-v3 with Docker Model Runner:
```
docker model run hf.co/DedeProGames/dialochess-v3
```

dialochess-v3

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: 0.8843
Accuracy: 0.0002

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: 5e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.3128	0.1616	200	1.1914	0.0002
1.1935	0.3231	400	1.0974	0.0002
1.1181	0.4847	600	1.0419	0.0
1.0778	0.6462	800	1.0080	0.0
1.0426	0.8078	1000	0.9828	0.0002
1.0185	0.9693	1200	0.9612	0.0002
1.0075	1.1309	1400	0.9458	0.0001
0.9765	1.2924	1600	0.9348	0.0002
0.9806	1.4540	1800	0.9248	0.0001
0.9542	1.6155	2000	0.9132	0.0002
0.9684	1.7771	2200	0.9059	0.0002
0.9525	1.9386	2400	0.9015	0.0002
0.9396	2.1002	2600	0.8960	0.0002
0.9342	2.2617	2800	0.8896	0.0002
0.9327	2.4233	3000	0.8874	0.0002
0.9344	2.5848	3200	0.8848	0.0002
0.9272	2.7464	3400	0.8848	0.0002
0.9288	2.9079	3600	0.8843	0.0002

Framework versions

Transformers 4.57.2
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: 4

Safetensors

Model size

0.1B params

Tensor type

F32

Model tree for DedeProGames/dialochess-v3

Base model

microsoft/DialoGPT-small

Finetuned

(53)

this model