Instructions to use Sashvat/HQQ-270M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Sashvat/HQQ-270M with Transformers:

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

pipe = pipeline("text-generation", model="Sashvat/HQQ-270M")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Sashvat/HQQ-270M")
model = AutoModelForCausalLM.from_pretrained("Sashvat/HQQ-270M")
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 Sashvat/HQQ-270M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Sashvat/HQQ-270M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Sashvat/HQQ-270M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Sashvat/HQQ-270M

SGLang

How to use Sashvat/HQQ-270M 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 "Sashvat/HQQ-270M" \
    --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": "Sashvat/HQQ-270M",
		"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 "Sashvat/HQQ-270M" \
        --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": "Sashvat/HQQ-270M",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Sashvat/HQQ-270M with Docker Model Runner:
```
docker model run hf.co/Sashvat/HQQ-270M
```

Akshat-Dwivedi commited on Mar 29

Commit

9367810

verified ·

1 Parent(s): 1ccf9de

Update README.md

Browse files

Files changed (1) hide show

README.md +12 -12

README.md CHANGED Viewed

@@ -19,7 +19,7 @@ license: other
 ## Overview :-
-**HQQ-270M** model is developed by **Nukeverse AI** by finetuning [Gemma-3](https://huggingface.co/google/gemma-3-270m-it)
 It specializes in **transforming complex, multi-layered user queries into optimized, high-quality Google search queries** .
 ⚠️ **Usage Requirement :**
@@ -65,8 +65,8 @@ pip install transformers accelerate huggingface_hub
 # Load model directly
 from transformers import AutoTokenizer, AutoModelForCausalLM
-tokenizer = AutoTokenizer.from_pretrained("NukeverseAi/HQQ-270M")
-model = AutoModelForCausalLM.from_pretrained("NukeverseAi/HQQ-270M")
 system_prompt = """
 Convert text after "HQQ: " into an optimized Google search query. Extract key terms, remove filler words, focus on searchable keywords.
@@ -110,7 +110,7 @@ print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))
 `Note` : **500 Steps / ~6 Epochs**
-<img src="https://huggingface.co/NukeverseAi/HQQ-270M/resolve/main/Metric.png">
 ---
@@ -128,19 +128,19 @@ This model is intended for :
 ## 📜 License
-This model is released under the **Nukeverse AI License v1.0**.
 You may freely use, modify, and distribute this model, including for commercial purposes .
 However, any use **must clearly state** :
-**"Made by Nukeverse AI"**
-📄 Full license: [LICENSE](https://huggingface.co/NukeverseAi/HQQ-270M/blob/main/LICENSE)
 ---
-## 🏢 About Nukeverse AI
-We are **Nukeverse AI**, from BHARAT 🕉️ . building next-generation productivity tools, AI agents, and research accelerators .
 ---
@@ -149,11 +149,11 @@ We are **Nukeverse AI**, from BHARAT 🕉️ . building next-generation producti
 If you use this model, please cite :
 ```
-@misc{2025-NukeverseAi-HQQ-270M,
   title   = {HQQ-270M},
-  author  = {NukeverseAi},
   year    = {2025},
-  url     = {https://huggingface.co/NukeverseAi/HQQ-270M}
 }
 ```
 ---

 ## Overview :-
+**HQQ-270M** model is developed by **Sashvat AI** by finetuning [Gemma-3](https://huggingface.co/google/gemma-3-270m-it)
 It specializes in **transforming complex, multi-layered user queries into optimized, high-quality Google search queries** .
 ⚠️ **Usage Requirement :**
 # Load model directly
 from transformers import AutoTokenizer, AutoModelForCausalLM
+tokenizer = AutoTokenizer.from_pretrained("Sashvat/HQQ-270M")
+model = AutoModelForCausalLM.from_pretrained("Sashvat/HQQ-270M")
 system_prompt = """
 Convert text after "HQQ: " into an optimized Google search query. Extract key terms, remove filler words, focus on searchable keywords.
 `Note` : **500 Steps / ~6 Epochs**
+<img src="https://huggingface.co/Sashvat/HQQ-270M/resolve/main/Metric.png">
 ---
 ## 📜 License
+This model is released under the **Sashvat AI License v1.0**.
 You may freely use, modify, and distribute this model, including for commercial purposes .
 However, any use **must clearly state** :
+**"Made by Sashvat AI"**
+📄 Full license: [LICENSE](https://huggingface.co/Sashvat/HQQ-270M/blob/main/LICENSE)
 ---
+## 🏢 About Sashvat AI
+We are **Sashvat AI**, from BHARAT 🕉️ . building next-generation productivity tools, AI agents, and research accelerators .
 ---
 If you use this model, please cite :
 ```
+@misc{2025-SashvatAI-HQQ-270M,
   title   = {HQQ-270M},
+  author  = {SashvatAI},
   year    = {2025},
+  url     = {https://huggingface.co/Sashvat/HQQ-270M}
 }
 ```
 ---