Instructions to use omdeep22/Gonyai-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use omdeep22/Gonyai-v1 with Transformers:

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

pipe = pipeline("text-generation", model="omdeep22/Gonyai-v1", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("omdeep22/Gonyai-v1", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use omdeep22/Gonyai-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "omdeep22/Gonyai-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "omdeep22/Gonyai-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/omdeep22/Gonyai-v1

SGLang

How to use omdeep22/Gonyai-v1 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 "omdeep22/Gonyai-v1" \
    --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": "omdeep22/Gonyai-v1",
		"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 "omdeep22/Gonyai-v1" \
        --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": "omdeep22/Gonyai-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use omdeep22/Gonyai-v1 with Docker Model Runner:
```
docker model run hf.co/omdeep22/Gonyai-v1
```

Gonyai-v1: A Poetic Konkani Language Model

Gonyai-v1 is a 160M parameter transformer model specifically designed for Konkani text generation. It features a custom architecture (KonkanGPT) utilizing Rotary Positional Embeddings (RoPE), RMSNorm, and SwiGLU activation functions.

Unlike general-purpose models, Gonyai-v1 is a linguistic specialist focused on the cultural and poetic nuances of the Konkani language.

Model Details

Architecture: KonkanGPT (Custom Transformer)
Parameters: ~160 Million
Tokenizer: Custom 32k Byte-Level BPE (Optimized for Devanagari/Konkani)
Training Data: Curated Konkani literature, news, and artistic works.

📊 Benchmarks (Sub-1B Category)

In Feb 2026 benchmarks, Gonyai-v1 was tested against global heavyweights SmolLM2-360M and Qwen2.5-0.5B. Despite its smaller size, Gonyai-v1 demonstrates superior linguistic efficiency for Konkani.

Metric	Gonyai-v1 (160M)	SmolLM2-360M	Qwen2.5-0.5B
Token Efficiency (Lower is Better)	5.00	7.85	6.57
Generation Speed (Tokens/Sec)	65.96	27.00	33.27
Vocabulary Diversity	0.80	0.91	0.93

Key Takeaway: Gonyai-v1 is 2x faster and significantly more token-efficient than larger generic models when handling Konkani script.

⚠️ Known Limitations

Factual Accuracy: At 160M parameters, the model is a creative artist, not an encyclopedia. It may hallucinate historical facts or dates.
Logical Reasoning: Not suitable for complex math or coding tasks.
Topic Drift: In long-form generations, the model may drift from the prompt into poetic repetition.

🚀 How to Use

Use the script below for optimal inference. Note: You must set trust_remote_code=True to load the custom architecture.

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "omdeep22/Gonyai-v1"
tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")

response = model.chat(tokenizer, "गोंयच्या निसर्गाविशीं एक ओळ बरय.")
print(response)

Downloads last month: 1,208