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)# 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
fix: tie_weights(**kwargs) per compatibilità transformers 4.40+
Browse files- modeling_quark.py +1 -1
modeling_quark.py
CHANGED
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@@ -157,7 +157,7 @@ class QuarkForCausalLM(QuarkPreTrainedModel):
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def set_output_embeddings(self, value):
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self.lm_head = value
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-
def tie_weights(self):
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"""HF chiama questo metodo dopo il caricamento dei pesi."""
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.embed_tokens.weight
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def set_output_embeddings(self, value):
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self.lm_head = value
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+
def tie_weights(self, **kwargs):
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"""HF chiama questo metodo dopo il caricamento dei pesi."""
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if self.config.tie_word_embeddings:
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self.lm_head.weight = self.embed_tokens.weight
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