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

Libraries

How to use vpadaraju/testing with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("vpadaraju/testing")
model = AutoModelForCausalLM.from_pretrained("vpadaraju/testing", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use vpadaraju/testing with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/vpadaraju/testing

SGLang

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

Docker Model Runner
How to use vpadaraju/testing with Docker Model Runner:
```
docker model run hf.co/vpadaraju/testing
```

vpadaraju commited on Feb 12, 2025

Commit

b3f2c4a

verified ·

1 Parent(s): 83777fd

End of training

Browse files

Files changed (1) hide show

README.md +11 -7

README.md CHANGED Viewed

@@ -4,6 +4,8 @@ license: mit
 base_model: microsoft/DialoGPT-small
 tags:
 - generated_from_trainer
 model-index:
 - name: testing
   results: []
@@ -14,9 +16,9 @@ should probably proofread and complete it, then remove this comment. -->
 # testing
-This model is a fine-tuned version of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) on an unknown dataset.
 It achieves the following results on the evaluation set:
-- Loss: 2.8036
 ## Model description
@@ -41,18 +43,20 @@ The following hyperparameters were used during training:
 - seed: 42
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
-- training_steps: 10
 ### Training results
-| Training Loss | Epoch  | Step | Validation Loss |
-|:-------------:|:------:|:----:|:---------------:|
-| No log        | 0.3846 | 5    | 3.8458          |
-| No log        | 0.7692 | 10   | 2.8036          |
 ### Framework versions
 - Transformers 4.48.2
 - Pytorch 2.5.1+cu124
 - Tokenizers 0.21.0

 base_model: microsoft/DialoGPT-small
 tags:
 - generated_from_trainer
+datasets:
+- empathetic_dialogues
 model-index:
 - name: testing
   results: []
 # testing
+This model is a fine-tuned version of [microsoft/DialoGPT-small](https://huggingface.co/microsoft/DialoGPT-small) on the empathetic_dialogues dataset.
 It achieves the following results on the evaluation set:
+- Loss: 1.7011
 ## Model description
 - seed: 42
 - optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
 - lr_scheduler_type: linear
+- num_epochs: 3
 ### Training results
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| No log        | 1.0   | 13   | 1.8261          |
+| No log        | 2.0   | 26   | 1.7528          |
+| No log        | 3.0   | 39   | 1.7011          |
 ### Framework versions
 - Transformers 4.48.2
 - Pytorch 2.5.1+cu124
+- Datasets 3.2.0
 - Tokenizers 0.21.0