Text Generation
Transformers
Safetensors
English
q
causal-lm
custom-architecture
pretrained
base-model
grouped-query-attention
qk-norm
gated-residuals
tiny-model
custom_code
Instructions to use q-project/Q-50M-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use q-project/Q-50M-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="q-project/Q-50M-Base", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("q-project/Q-50M-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use q-project/Q-50M-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "q-project/Q-50M-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "q-project/Q-50M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/q-project/Q-50M-Base
- SGLang
How to use q-project/Q-50M-Base 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 "q-project/Q-50M-Base" \ --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": "q-project/Q-50M-Base", "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 "q-project/Q-50M-Base" \ --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": "q-project/Q-50M-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use q-project/Q-50M-Base with Docker Model Runner:
docker model run hf.co/q-project/Q-50M-Base
| { | |
| "architectures": [ | |
| "QForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "attention_scalar_gate": true, | |
| "bos_token_id": 1, | |
| "dtype": "float32", | |
| "eos_token_id": 2, | |
| "gate_multiplier": 2.0, | |
| "head_dim": 64, | |
| "hidden_act": "silu", | |
| "hidden_size": 512, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 1792, | |
| "max_position_embeddings": 2048, | |
| "mlp_scalar_gate": true, | |
| "model_type": "q", | |
| "nope_every_n": null, | |
| "nope_layers": [], | |
| "num_attention_heads": 8, | |
| "num_hidden_layers": 10, | |
| "num_key_value_heads": 2, | |
| "pad_token_id": 0, | |
| "qk_norm": true, | |
| "rms_norm_eps": 1e-05, | |
| "rope_parameters": { | |
| "rope_theta": 10000.0, | |
| "rope_type": "default" | |
| }, | |
| "sliding_window": 4096, | |
| "tie_word_embeddings": true, | |
| "transformers_version": "5.5.0", | |
| "use_cache": true, | |
| "vocab_size": 32768, | |
| "auto_map": { | |
| "AutoConfig": "configuration_q.QConfig", | |
| "AutoModel": "modeling_q.QModel", | |
| "AutoModelForCausalLM": "modeling_q.QForCausalLM" | |
| } | |
| } | |