Instructions to use Harshit110/harshit-ai-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Harshit110/harshit-ai-v2 with Transformers:

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

pipe = pipeline("text-generation", model="Harshit110/harshit-ai-v2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Harshit110/harshit-ai-v2")
model = AutoModelForCausalLM.from_pretrained("Harshit110/harshit-ai-v2", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Harshit110/harshit-ai-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Harshit110/harshit-ai-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Harshit110/harshit-ai-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Harshit110/harshit-ai-v2

SGLang

How to use Harshit110/harshit-ai-v2 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 "Harshit110/harshit-ai-v2" \
    --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": "Harshit110/harshit-ai-v2",
		"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 "Harshit110/harshit-ai-v2" \
        --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": "Harshit110/harshit-ai-v2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Harshit110/harshit-ai-v2 with Docker Model Runner:
```
docker model run hf.co/Harshit110/harshit-ai-v2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

harshit-ai-v2

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

Loss: 3.2182

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: 2
eval_batch_size: 2
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: linear
lr_scheduler_warmup_steps: 50
num_epochs: 4
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
6.8591	0.2273	10	6.6235
5.889	0.4545	20	5.4552
4.9775	0.6818	30	4.4243
4.0926	0.9091	40	3.7713
3.1841	1.1364	50	3.4910
2.966	1.3636	60	3.2945
2.8815	1.5909	70	3.2089
2.5642	1.8182	80	3.0903
2.4465	2.0455	90	3.0532
1.9558	2.2727	100	3.1496
2.0124	2.5	110	3.0998
1.8665	2.7273	120	3.0610
1.7315	2.9545	130	3.1380
1.5431	3.1818	140	3.1873
1.2887	3.4091	150	3.2724
1.3797	3.6364	160	3.2327
1.5645	3.8636	170	3.2182

Framework versions

Transformers 4.57.1
Pytorch 2.9.0+cu126
Datasets 4.0.0
Tokenizers 0.22.1

Downloads last month: 18

Safetensors

Model size

0.4B params

Tensor type

F32

Model tree for Harshit110/harshit-ai-v2

Base model

microsoft/DialoGPT-medium

Finetuned

(93)

this model

Quantizations

1 model