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yamura4/limite-1b-violetto-GGUF
limite-1b-violetto-GGUF is a text generation model from yamura4. Use it when you need the model to write or continue text. It is set up for gguf. The card lists the license as apache-2.0.
High-quality GGUF quantizations of Paradigma's Limite 1B - Violetto, built for high-throughput mathematical reasoning.
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From the Hugging Face model README
High-quality GGUF quantizations of Paradigma's Limite 1B - Violetto, built for high-throughput mathematical reasoning.
Limite is a 1-billion parameter dense autoregressive transformer, trained from scratch on a mixture of less than 300B highly curated tokens, capable of handling sequences up to 131k tokens long. It is deliberately lightly instruction-tuned and designed to respond in single turns, with extremely high mathematical capability per parameter count — rivaling models tens of times larger on competition-level math (e.g. 74.25% average on BeyondAIME).
See the original model card and the release blog for the full overview, evaluation tables, and prompting guidance.
| File | Size | Quality |
|---|---|---|
limite-1b-violetto-Q8_0.gguf | 1.09 GiB | Near-lossless |
limite-1b-violetto-Q6_K.gguf | 0.86 GiB | Excellent |
limite-1b-violetto-Q5_K_M.gguf | 0.77 GiB | Very good |
limite-1b-violetto-Q4_K_M.gguf | 0.68 GiB | Good — best size/quality trade-off |
limite-1b-violetto-Q3_K_M.gguf | 0.59 GiB | Degraded (small 1B model, low bits hurt) |
limite-1b-violetto-Q2_K_L.gguf | 0.46 GiB | Heavily degraded, size fallback only |
For a 1B model the quality cliff below Q4 is steep — Q8_0, Q6_K, Q5_K_M and Q4_K_M are the recommended files.
Following the current best practice for GGUF quantization (per-layer importance-aware K-quants in the spirit of Unsloth's Dynamic approach — pure post-training quantization, no QAT):
unsloth_zoo.llama_cpp.quantize_gguf); Q2_K_L uses the Unsloth mixed-precision preset (q2_k base with ffn_down upcasts, Q6_K output, Q4_K embeddings).Runtime requirement: Limite is a custom architecture (
limite). These files need a llama.cpp build with Limite support — stock llama.cpp / Ollama / LM Studio / Unsloth Desktop builds do not implement this architecture yet and will fail withunknown model architecture: 'limite'. Build from a patched llama.cpp (e.g. the communitylimite.patchagainst upstream5836771) withllama-completion,llama-server,llama-quantizetargets.
llama-completion \
-m limite-1b-violetto-Q4_K_M.gguf -c 16384 --no-context-shift \
-n 256 -p "What is 27 times 34? Show the steps."
For Violetto chat, use llama-server with the embedded Jinja template and user-only messages; preserve its fixed mathematical system prompt.
Sampling settings: temperature=0.6 and top_p=0.95 (from the upstream generation_config.json).
Limite is designed to respond in single turns with radically different response patterns than regular assistants — it can lose the scope of a prompt and reinterpret it as a different, often mathematical, task. Don't expect general instruction-following; see the release blog for examples.
Model weights: Apache-2.0, carried over from Paradigma's release. This is a community conversion, not an endorsed release from Paradigma. Original citation:
@misc{paradigma2026limite,
title = {{Limite 1B - Violetto}},
author = {Prignano, Mario and Cirillo, Gabriele and
Morosini, Alessio and Cerovaz, Luca and
Bartolocci, Alessandro and Rodolà, Emanuele and
Starace, Giulio and Pappone, Francesco},
year = {2026},
howpublished = {\url{https://paradigma.inc/blog/limite-1b-violetto/}}
}