Text Generation
Transformers
PyTorch
Safetensors
English
qed
causal-lm
decoder-only
rope
rmsnorm
swiglu
custom-architecture
custom_code
Instructions to use levossadtchi/QED-75M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use levossadtchi/QED-75M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="levossadtchi/QED-75M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("levossadtchi/QED-75M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use levossadtchi/QED-75M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "levossadtchi/QED-75M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "levossadtchi/QED-75M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/levossadtchi/QED-75M
- SGLang
How to use levossadtchi/QED-75M 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 "levossadtchi/QED-75M" \ --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": "levossadtchi/QED-75M", "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 "levossadtchi/QED-75M" \ --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": "levossadtchi/QED-75M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use levossadtchi/QED-75M with Docker Model Runner:
docker model run hf.co/levossadtchi/QED-75M
| { | |
| "vocab_size": 49152, | |
| "max_seq_len": 8192, | |
| "d_model": 384, | |
| "n_layers": 32, | |
| "n_heads": 6, | |
| "ffn_hidden_dim": 1024, | |
| "rope_theta": 10000.0, | |
| "rms_norm_eps": 1e-05, | |
| "initializer_range": 0.02, | |
| "dropout": 0.0, | |
| "tie_word_embeddings": true, | |
| "bias": false, | |
| "pad_token_id": 0, | |
| "bos_token_id": 1, | |
| "eos_token_id": 2, | |
| "model_type": "qed", | |
| "architectures": [ | |
| "QEDForCausalLM" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "modeling_qed.QEDConfig", | |
| "AutoModelForCausalLM": "modeling_qed.QEDForCausalLM" | |
| } | |
| } |