Instructions to use Premchan369/Q-TensorFormer with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Premchan369/Q-TensorFormer with Transformers:

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

pipe = pipeline("text-generation", model="Premchan369/Q-TensorFormer", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Premchan369/Q-TensorFormer", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Premchan369/Q-TensorFormer with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Premchan369/Q-TensorFormer"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Premchan369/Q-TensorFormer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Premchan369/Q-TensorFormer

SGLang

How to use Premchan369/Q-TensorFormer 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 "Premchan369/Q-TensorFormer" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Premchan369/Q-TensorFormer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Premchan369/Q-TensorFormer" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Premchan369/Q-TensorFormer",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Premchan369/Q-TensorFormer with Docker Model Runner:
```
docker model run hf.co/Premchan369/Q-TensorFormer
```

Premchandyadav369 commited on 26 days ago

Commit

72bef29

1 Parent(s): 02dab15

fix(app): sync demo module-level instance and queue with space

Browse files

Files changed (1) hide show

app.py +8 -4

app.py CHANGED Viewed

@@ -102,7 +102,8 @@ def generate_with_telemetry(prompt: str, max_tokens: int = 64, temperature: floa
     for step in range(max_tokens):
         with torch.no_grad():
-            outputs = MODEL(curr_ids[:, -1:], past_key_values=past_key_values, use_cache=True)
             logits = outputs.logits[:, -1, :]
             past_key_values = outputs.past_key_values
@@ -283,7 +284,10 @@ def build_app():
     return demo
 if __name__ == "__main__":
-    app = build_app()
-    if app:
-        app.launch(server_name="0.0.0.0", server_port=7860, share=False)

     for step in range(max_tokens):
         with torch.no_grad():
+            model_in = curr_ids if step == 0 else curr_ids[:, -1:]
+            outputs = MODEL(model_in, past_key_values=past_key_values, use_cache=True)
             logits = outputs.logits[:, -1, :]
             past_key_values = outputs.past_key_values
     return demo
+# Instantiate demo at module level for Hugging Face Spaces & Gradio discovery
+demo = build_app()
 if __name__ == "__main__":
+    if demo is not None:
+        demo.queue().launch(server_name="0.0.0.0", server_port=7860)