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@@ -51,7 +51,7 @@ This is an **instruction-tuned** checkpoint, fine-tuned via SFT on top of a base
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  | **Languages** | English, Italian |
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  | **License** | MIT |
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  | **Finetuned from** | Quark-72M base (pre-trained on math/code/reasoning mix) |
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- | **Repository** | [ThingAI/Quark-72M-Instruct](https://huggingface.co/ThingAI/Quark-72M-Instruct) |
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  Quark-72M-Instruct is part of a broader effort at ThingAI to build small, self-hostable language models that can be trained, fine-tuned, and served entirely on personal infrastructure — without dependency on third-party APIs. It trades raw capability for transparency, inspectability, and low resource requirements.
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@@ -59,7 +59,7 @@ Quark-72M-Instruct is part of a broader effort at ThingAI to build small, self-h
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  ## Architecture
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- Quark-72M-Instruct uses a standard decoder-only Transformer stack with several efficiency-oriented design choices common in modern small LLMs:
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  | Component | Detail |
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  |---|---|
@@ -86,7 +86,7 @@ Quark-72M-Instruct uses a standard decoder-only Transformer stack with several e
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  ## Intended Use
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- Quark-72M-Instruct is intended primarily as:
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  - A **research and educational artifact** for studying small-scale LLM architecture, training, and inference.
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  - A **lightweight conversational base** for hobbyist or self-hosted projects where running a multi-billion-parameter model is impractical.
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  ## Prompt Format
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- Quark-72M-Instruct was fine-tuned using a ChatML-style template with `<|im_start|>` / `<|im_end|>` role delimiters:
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  ```
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  <|im_start|>user
@@ -285,7 +285,7 @@ Because this model relies on custom modeling code rather than a built-in `transf
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  ## Citation
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- If you use Quark-72M-Instruct in your work, please consider citing the repository:
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  ```bibtex
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  @misc{quark72m,
 
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  | **Languages** | English, Italian |
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  | **License** | MIT |
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  | **Finetuned from** | Quark-72M base (pre-trained on math/code/reasoning mix) |
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+ | **Repository** | [ThingAI/Quark-72M](https://huggingface.co/ThingAI/Quark-72M) |
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  Quark-72M-Instruct is part of a broader effort at ThingAI to build small, self-hostable language models that can be trained, fine-tuned, and served entirely on personal infrastructure — without dependency on third-party APIs. It trades raw capability for transparency, inspectability, and low resource requirements.
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  ## Architecture
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+ Quark-72M uses a standard decoder-only Transformer stack with several efficiency-oriented design choices common in modern small LLMs:
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  | Component | Detail |
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  |---|---|
 
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  ## Intended Use
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+ Quark-72M is intended primarily as:
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  - A **research and educational artifact** for studying small-scale LLM architecture, training, and inference.
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  - A **lightweight conversational base** for hobbyist or self-hosted projects where running a multi-billion-parameter model is impractical.
 
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  ## Prompt Format
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+ Quark-72M was fine-tuned using a ChatML-style template with `<|im_start|>` / `<|im_end|>` role delimiters:
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  ```
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  <|im_start|>user
 
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  ## Citation
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+ If you use Quark-72M in your work, please consider citing the repository:
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  ```bibtex
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  @misc{quark72m,