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, dtype="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
File size: 844 Bytes
55856f7 9632173 55856f7 9632173 55856f7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | {
"model_type": "tinyqwen3_novelty",
"architectures": [
"TinyQwen3NoveltyForCausalLM"
],
"auto_map": {
"AutoConfig": "modeling_tinyqwen3_novelty.TinyQwen3NoveltyConfig",
"AutoModelForCausalLM": "modeling_tinyqwen3_novelty.TinyQwen3NoveltyForCausalLM"
},
"vocab_size": 4098,
"hidden_size": 256,
"intermediate_size": 896,
"num_hidden_layers": 8,
"num_attention_heads": 8,
"num_key_value_heads": 4,
"head_dim": 32,
"rms_norm_eps": 1e-06,
"rope_theta": 2500.0,
"max_position_embeddings": 512,
"tie_word_embeddings": true,
"initializer_range": 0.02,
"torch_dtype": "float32",
"bos_token_id": 1,
"eos_token_id": 2,
"pad_token_id": 2,
"novelty_gate_floor": 0.05,
"novelty_gate_type": "math_rms_abs_delta",
"im_start_token_id": 4096,
"im_end_token_id": 4097
}
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