Instructions to use deqing/lstm-window-4-v5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use deqing/lstm-window-4-v5 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="deqing/lstm-window-4-v5", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("deqing/lstm-window-4-v5", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use deqing/lstm-window-4-v5 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "deqing/lstm-window-4-v5" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "deqing/lstm-window-4-v5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/deqing/lstm-window-4-v5
- SGLang
How to use deqing/lstm-window-4-v5 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 "deqing/lstm-window-4-v5" \ --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": "deqing/lstm-window-4-v5", "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 "deqing/lstm-window-4-v5" \ --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": "deqing/lstm-window-4-v5", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use deqing/lstm-window-4-v5 with Docker Model Runner:
docker model run hf.co/deqing/lstm-window-4-v5
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fda26b8 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | {
"architectures": [
"LSTMForCausalLM"
],
"auto_map": {
"AutoConfig": "lstm.LSTMLanguageModelConfig",
"AutoModelForCausalLM": "lstm.LSTMForCausalLM"
},
"bos_token_id": 128000,
"dropout": 0.1,
"dtype": "float32",
"embed_dim": 1024,
"eos_token_id": 128001,
"hidden_size": 1024,
"model_type": "lstm_lm",
"num_layers": 4,
"tie_word_embeddings": true,
"transformers_version": "5.3.0",
"use_cache": false,
"vocab_size": 128256
}
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