Text Generation
Transformers
Safetensors
English
Italian
quark
causal-lm
small-language-model
gqa
rope
swiglu
bash
code
custom_code
Instructions to use ThingAI/ARK-72M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ThingAI/ARK-72M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ThingAI/ARK-72M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("ThingAI/ARK-72M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ThingAI/ARK-72M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ThingAI/ARK-72M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ThingAI/ARK-72M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/ThingAI/ARK-72M
- SGLang
How to use ThingAI/ARK-72M 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 "ThingAI/ARK-72M" \ --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": "ThingAI/ARK-72M", "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 "ThingAI/ARK-72M" \ --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": "ThingAI/ARK-72M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use ThingAI/ARK-72M with Docker Model Runner:
docker model run hf.co/ThingAI/ARK-72M
Commit History
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feat: repetition penalty in generate_text
Verified signature3a388e1 -
fix: inv_freq calcolato runtime, non buffer (evita corruzione meta-device)
Verified signature38feb38 -
fix: RotaryEmbedding lazy cache build (evita garbage da meta-device init)
Verified signature4e1e026 -
fix: inv_freq persistent=False, safetensors solo named_parameters
Verified signature257acf0
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fix: cast float32 prima di RoPE per evitare overflow
Verified signature9716697 -
fix: SDPA in float32 per stabilità numerica
Verified signature23eadd7 -
fix: cast q,k a dtype di v dopo RoPE — identico a train.py
Verified signature434cb12 -
fix: inv_freq persistent=True + incluso nel safetensors
Verified signature5f72285 -
fix: weight tying via embed_tokens.weight.T, rimuove lm_head
Verified signatured660488 -
fix: cast q,k,v a dtype originale dopo RoPE
Verified signatureb0ed64c -
fix: v.to(q.dtype) prima di SDPA
Verified signature85e1731 -
fix: copia esatta architettura da train.py, RoPE senza cast dtype
Verified signature5c0671e -
fix: NaN guard in generate_text + _keys_to_ignore_on_load_missing
Verified signature91da9fd -
fix: tie_weights(**kwargs) per compatibilità transformers 4.40+
Verified signaturee0d9139 -
fix: dtype cast in SDPA + tie_weights() esplicito
Verified signaturecd6716a -
Export Quark Instruct checkpoint
Verified signature92a7d9d