Instructions to use aaaacash/CowardCow with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aaaacash/CowardCow with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="aaaacash/CowardCow")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("aaaacash/CowardCow") model = AutoModelForCausalLM.from_pretrained("aaaacash/CowardCow") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use aaaacash/CowardCow with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aaaacash/CowardCow" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aaaacash/CowardCow", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/aaaacash/CowardCow
- SGLang
How to use aaaacash/CowardCow 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 "aaaacash/CowardCow" \ --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": "aaaacash/CowardCow", "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 "aaaacash/CowardCow" \ --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": "aaaacash/CowardCow", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use aaaacash/CowardCow with Docker Model Runner:
docker model run hf.co/aaaacash/CowardCow
Upload folder using huggingface_hub
Browse files- config.json +5 -5
- pytorch_model.bin +2 -2
- tokenizer.model +2 -2
- training.log +0 -0
config.json
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{
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"_name_or_path": "
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"architectures": [
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"LlamaForCausalLM"
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"hidden_size": 4096,
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"max_position_embeddings":
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"pretraining_tp": 1,
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"rope_theta":
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"tie_word_embeddings": false,
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"torch_dtype": "
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"transformers_version": "4.34.1",
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"use_cache": true,
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"vocab_size":
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}
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"_name_or_path": "codellama/CodeLlama-7b-hf",
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"architectures": [
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"LlamaForCausalLM"
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],
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 16384,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.34.1",
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"use_cache": true,
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"vocab_size": 32024
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}
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pytorch_model.bin
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tokenizer.model
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training.log
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