Instructions to use ataeff/arianna2 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 ataeff/arianna2 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 ataeff/arianna2:Q4_0 # Run inference directly in the terminal: llama cli -hf ataeff/arianna2:Q4_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf ataeff/arianna2:Q4_0 # Run inference directly in the terminal: llama cli -hf ataeff/arianna2:Q4_0
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 ataeff/arianna2:Q4_0 # Run inference directly in the terminal: ./llama-cli -hf ataeff/arianna2:Q4_0
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 ataeff/arianna2:Q4_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ataeff/arianna2:Q4_0
Use Docker
docker model run hf.co/ataeff/arianna2:Q4_0
- LM Studio
- Jan
- Ollama
How to use ataeff/arianna2 with Ollama:
ollama run hf.co/ataeff/arianna2:Q4_0
- Unsloth Desktop
- Docker Model Runner
How to use ataeff/arianna2 with Docker Model Runner:
docker model run hf.co/ataeff/arianna2:Q4_0
- Lemonade
How to use ataeff/arianna2 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ataeff/arianna2:Q4_0
Run and chat with the model
lemonade run user.arianna2-Q4_0
List all available models
lemonade list
- Atomic Chat
Upload 6 files
Browse files- .gitattributes +2 -0
- qw1-5b/adapter_2000.safetensors +3 -0
- qw1-5b/adapter_2500.safetensors +3 -0
- qw1-5b/adapter_config.json +34 -0
- qw1-5b/arianna_qwen15_2000_q4_0.gguf +3 -0
- qw1-5b/arianna_qwen15_2500_q4_0.gguf +3 -0
- qw1-5b/tokenizer.json +0 -0
.gitattributes
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@@ -45,3 +45,5 @@ llama3b/q4_k/arianna_3b_2500_q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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llama3b/q4_k/arianna_3b_3000_q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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smollm2-1-7b/arianna_smol17_2000_q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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smollm2-1-7b/arianna_smol17_2500_q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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llama3b/q4_k/arianna_3b_3000_q4_k_m.gguf filter=lfs diff=lfs merge=lfs -text
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smollm2-1-7b/arianna_smol17_2000_q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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smollm2-1-7b/arianna_smol17_2500_q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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qw1-5b/arianna_qwen15_2000_q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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qw1-5b/arianna_qwen15_2500_q4_0.gguf filter=lfs diff=lfs merge=lfs -text
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qw1-5b/adapter_2000.safetensors
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{
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"alpha_pattern": {},
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"base_model_name_or_path": "Qwen/Qwen2.5-1.5B",
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"loftq_config": {},
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 64,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"v_proj",
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"gate_proj",
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"down_proj",
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"o_proj",
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"up_proj",
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"k_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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qw1-5b/tokenizer.json
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