Instructions to use AIIT-Threshold/Tessera-1B 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 AIIT-Threshold/Tessera-1B 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 AIIT-Threshold/Tessera-1B:Q6_K # Run inference directly in the terminal: llama cli -hf AIIT-Threshold/Tessera-1B:Q6_K
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf AIIT-Threshold/Tessera-1B:Q6_K # Run inference directly in the terminal: llama cli -hf AIIT-Threshold/Tessera-1B:Q6_K
Build from source code
git clone https://github.com/ggml-org/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-app # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama serve -hf AIIT-Threshold/Tessera-1B:Q6_K # Run inference directly in the terminal: ./build/bin/llama cli -hf AIIT-Threshold/Tessera-1B:Q6_K
Use Docker
docker model run hf.co/AIIT-Threshold/Tessera-1B:Q6_K
- Llama new
- LM Studio
- Jan
- Ollama
How to use AIIT-Threshold/Tessera-1B with Ollama:
ollama run hf.co/AIIT-Threshold/Tessera-1B:Q6_K
- Unsloth Desktop
- Docker Model Runner
How to use AIIT-Threshold/Tessera-1B with Docker Model Runner:
docker model run hf.co/AIIT-Threshold/Tessera-1B:Q6_K
- Lemonade
How to use AIIT-Threshold/Tessera-1B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AIIT-Threshold/Tessera-1B:Q6_K
Run and chat with the model
lemonade run user.Tessera-1B-Q6_K
List all available models
lemonade list
- Atomic Chat
Commit History
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Update README.md
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Document the GGUF quant + required RMSNorm patch (already uploaded, just wasn't in the model card)
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Document GGUF files: base-model-only, needs patched llama.cpp, raw-completion usage
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Add llama.cpp RMSNorm patch required to load the GGUF correctly
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Fix GGUF: use RMSNorm (was incorrectly LayerNorm) + set pre-tokenizer metadata
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Fix GGUF: use RMSNorm (was incorrectly LayerNorm) + set pre-tokenizer metadata
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README: add stack cross-links (user-approved)
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Add f16 GGUF (source for further quantization)
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Add Q6_K GGUF quant for llama.cpp
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Restore interview section (was clobbered by an earlier sync) + add WADT proof-of-concept section with real loss curve and transcript quotes
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Add WADT dilemma fine-tune adapter — proof-of-concept SFT run, live on the shared 3090 under VRAM guard
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Link the tokenizer design doc from the card
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Remove old-named tokenizer.json (superseded by tessera_tokenizer.json)
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Docs: tessera_tokenizer.json
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Fix loader default path to tessera_tokenizer.json
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Docs: rename forge64k -> Tessera tokenizer
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Rename tokenizer.json -> tessera_tokenizer.json (drop internal codename)
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Tessera 1B: from-scratch 1.01B base + v12i/v7 SFT adapters, tokenizer, loader, USAGE (Apache-2.0)
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initial commit
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