Instructions to use Breakintelligence/cveparrot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Breakintelligence/cveparrot with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf Breakintelligence/cveparrot # Run inference directly in the terminal: llama cli -hf Breakintelligence/cveparrot
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf Breakintelligence/cveparrot # Run inference directly in the terminal: llama cli -hf Breakintelligence/cveparrot
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf Breakintelligence/cveparrot # Run inference directly in the terminal: ./llama-cli -hf Breakintelligence/cveparrot
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf Breakintelligence/cveparrot # Run inference directly in the terminal: ./build/bin/llama-cli -hf Breakintelligence/cveparrot
Use Docker
docker model run hf.co/Breakintelligence/cveparrot
- LM Studio
- Jan
- vLLM
How to use Breakintelligence/cveparrot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Breakintelligence/cveparrot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Breakintelligence/cveparrot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Breakintelligence/cveparrot
- Ollama
How to use Breakintelligence/cveparrot with Ollama:
ollama run hf.co/Breakintelligence/cveparrot
- Unsloth Studio
How to use Breakintelligence/cveparrot with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Breakintelligence/cveparrot to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for Breakintelligence/cveparrot to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Breakintelligence/cveparrot to start chatting
- Docker Model Runner
How to use Breakintelligence/cveparrot with Docker Model Runner:
docker model run hf.co/Breakintelligence/cveparrot
- Lemonade
How to use Breakintelligence/cveparrot with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Breakintelligence/cveparrot
Run and chat with the model
lemonade run user.cveparrot-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
File size: 1,465 Bytes
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"architectures": [
"T5ForConditionalGeneration"
],
"classifier_dropout": 0.0,
"d_ff": 2048,
"d_kv": 64,
"d_model": 512,
"decoder_start_token_id": 0,
"dense_act_fn": "relu",
"dropout_rate": 0.1,
"dtype": "float32",
"eos_token_id": 1,
"feed_forward_proj": "relu",
"initializer_factor": 1.0,
"is_encoder_decoder": true,
"is_gated_act": false,
"layer_norm_epsilon": 1e-06,
"model_type": "t5",
"n_positions": 512,
"num_decoder_layers": 6,
"num_heads": 8,
"num_layers": 6,
"output_past": true,
"pad_token_id": 0,
"relative_attention_max_distance": 128,
"relative_attention_num_buckets": 32,
"task_specific_params": {
"summarization": {
"early_stopping": true,
"length_penalty": 2.0,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
},
"transformers_version": "4.57.1",
"use_cache": true,
"vocab_size": 32128
}
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