Text Generation
Transformers
Safetensors
tinyqwen3_novelty
qwen3
causal-lm
tiny-language-model
novelty-gated-attention
trust-remote-code
custom_code
Instructions to use User01110/tinyLM-8M-exp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use User01110/tinyLM-8M-exp with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="User01110/tinyLM-8M-exp", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("User01110/tinyLM-8M-exp", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use User01110/tinyLM-8M-exp with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "User01110/tinyLM-8M-exp" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "User01110/tinyLM-8M-exp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/User01110/tinyLM-8M-exp
- SGLang
How to use User01110/tinyLM-8M-exp 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 "User01110/tinyLM-8M-exp" \ --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": "User01110/tinyLM-8M-exp", "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 "User01110/tinyLM-8M-exp" \ --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": "User01110/tinyLM-8M-exp", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use User01110/tinyLM-8M-exp with Docker Model Runner:
docker model run hf.co/User01110/tinyLM-8M-exp
Persist RoPE inv_freq buffers
Browse files- model.safetensors +2 -2
- modeling_tinyqwen3_novelty.py +1 -1
model.safetensors
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modeling_tinyqwen3_novelty.py
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def __init__(self, head_dim, theta):
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super().__init__()
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inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
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self.register_buffer("inv_freq", inv_freq
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def forward(self, seq_len, device):
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pos = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
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def __init__(self, head_dim, theta):
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super().__init__()
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inv_freq = 1.0 / (theta ** (torch.arange(0, head_dim, 2).float() / head_dim))
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self.register_buffer("inv_freq", inv_freq)
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def forward(self, seq_len, device):
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pos = torch.arange(seq_len, device=device, dtype=self.inv_freq.dtype)
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