Instructions to use tatjr13/damascus-t1a-v4l1 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 tatjr13/damascus-t1a-v4l1 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 tatjr13/damascus-t1a-v4l1 # Run inference directly in the terminal: llama cli -hf tatjr13/damascus-t1a-v4l1
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf tatjr13/damascus-t1a-v4l1 # Run inference directly in the terminal: llama cli -hf tatjr13/damascus-t1a-v4l1
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 tatjr13/damascus-t1a-v4l1 # Run inference directly in the terminal: ./llama-cli -hf tatjr13/damascus-t1a-v4l1
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 tatjr13/damascus-t1a-v4l1 # Run inference directly in the terminal: ./build/bin/llama-cli -hf tatjr13/damascus-t1a-v4l1
Use Docker
docker model run hf.co/tatjr13/damascus-t1a-v4l1
- LM Studio
- Jan
- Ollama
How to use tatjr13/damascus-t1a-v4l1 with Ollama:
ollama run hf.co/tatjr13/damascus-t1a-v4l1
- Unsloth Desktop
- Docker Model Runner
How to use tatjr13/damascus-t1a-v4l1 with Docker Model Runner:
docker model run hf.co/tatjr13/damascus-t1a-v4l1
- Lemonade
How to use tatjr13/damascus-t1a-v4l1 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull tatjr13/damascus-t1a-v4l1
Run and chat with the model
lemonade run user.damascus-t1a-v4l1-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
README: source + T1a template variant
Browse files
README.md
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# damascus-t1a-v4l1
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GGUF artifact for DAMASCUS test T1a (test plan v2.1).
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- **Source**: tpn-004 candidate v4 final, L1-im layout — the bytes behind the official Fluid 0.5679 submission (`L1-im.gguf`, sha256 `13d6a4a6a28d8c33...`). Q4_K_M quantized by llama.cpp b10020 with the evaluator-shaped imatrix; Q8_0 for attn_q/k/v/output, ffn_down, token embeddings and output tensor.
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- **Template variant T1a**: the shipped 305-char Mistral `[INST]` chat template with one edit — the system instruction is prepended as its own `[INST] ... [/INST]` block right after `bos_token` (byte-identical to how the shipped template renders a system message). Instruction: "Answer directly and briefly. If the question offers lettered answer options, think for at most two short sentences, then end your reply with exactly: The answer is (X). where X is the letter. Otherwise answer the task exactly as asked."
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- **Weights**: byte-identical to the source (all 363 tensor sha256s verified equal; only `tokenizer.chat_template` differs). Template sha256 `d338a0af9865562ac73b6f67e82fc80a7edd4e86d384a0c0b10caba784f5ae11`.
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The single file `model.gguf` is the complete artifact.
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