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
```

Q-TensorFormer

File size: 999 Bytes

{
  "architectures": [
    "QTensorFormerForCausalLM"
  ],
  "auto_map": {
    "AutoConfig": "configuration_qtensorformer.QTensorFormerConfig",
    "AutoModelForCausalLM": "modeling_qtensorformer.QTensorFormerForCausalLM"
  },
  "attention_sink_size": 4,
  "bos_token_id": 1,
  "eos_token_id": 2,
  "pad_token_id": null,
  "enable_dynamic_routing": true,
  "hidden_size": 2048,
  "hysteresis_threshold": 0.15,
  "initializer_range": 0.02,
  "intermediate_size": 5632,
  "kv_cache_window_size": 1024,
  "kv_quant_bits": 4,
  "max_position_embeddings": 4096,
  "model_type": "qtensorformer",
  "num_attention_heads": 16,
  "num_hidden_layers": 16,
  "num_key_value_heads": 4,
  "pid_kd": 0.05,
  "pid_ki": 0.01,
  "pid_kp": 0.1,
  "quantize_kv_cold": true,
  "rms_norm_eps": 1e-06,
  "rope_theta": 10000.0,
  "target_energy_budget": 1.0,
  "tie_word_embeddings": false,
  "transformers_version": "4.41.2",
  "tt_ranks": [
    1,
    16,
    16,
    1
  ],
  "use_cache": true,
  "vocab_size": 32000
}