Text Generation
Transformers
Safetensors
PyTorch
English
ac_swiglu
custom_code
causal-lm
channelmix-swiglu
channel-mixing
qana
qana-mini-5m
generalist
4k-tokenizer
fromziro
Instructions to use fromziro/Qana-mini-5M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fromziro/Qana-mini-5M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="fromziro/Qana-mini-5M", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("fromziro/Qana-mini-5M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use fromziro/Qana-mini-5M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "fromziro/Qana-mini-5M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "fromziro/Qana-mini-5M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/fromziro/Qana-mini-5M
- SGLang
How to use fromziro/Qana-mini-5M 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 "fromziro/Qana-mini-5M" \ --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": "fromziro/Qana-mini-5M", "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 "fromziro/Qana-mini-5M" \ --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": "fromziro/Qana-mini-5M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use fromziro/Qana-mini-5M with Docker Model Runner:
docker model run hf.co/fromziro/Qana-mini-5M
Move RoPE parameters into model config
#2
by User01110 - opened
Expose the trained split-half RoPE settings in config.json, source rope_theta from ACSwiGLUConfig, validate the fixed no-scaling layout, and rebuild deterministic RoPE caches instead of storing them as checkpoint buffers.
User01110 changed pull request status to open
Validated by Python compilation and JSON parsing. Merging the configuration-only RoPE cleanup; model weights and trained numerical behavior remain unchanged.
User01110 changed pull request status to merged