Update README.md

README.md

@@ -12,7 +12,7 @@ base_model:
 
 This model is a fine-tuned version of **Liquid AI**’s **LFM2.5‑1.2B‑Instruct**, adapted for Saudi dialect conversational generation.
 
-The base model belongs to the LFM2.5 family — hybrid state-space + attention language models designed for **fast on-device inference**, low memory usage, and strong performance relative to size. It contains ~1.17B parameters, 32k context length, and supports multilingual generation including Arabic. ([Hugging Face][1])
+The base model belongs to the LFM2.5 family — hybrid state-space + attention language models designed for **fast on-device inference**,low memory usage, and strong performance relative to size. It contains ~1.17B parameters, 32k context length, and supports multilingual generation including Arabic.
 
 This fine-tuned variant specializes the model for **Saudi dialect conversational patterns**, improving fluency, dialect authenticity, and instruction following for regional Arabic use cases.
 
@@ -46,7 +46,7 @@ This fine-tuned variant specializes the model for **Saudi dialect conversational
 * Parameters: ~1.17B
 * Context length: 32,768 tokens
 * Training tokens: ~28T
-* Languages: Multilingual including Arabic ([Hugging Face][1])
+* Languages: Multilingual including Arabic
 
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@@ -115,7 +115,7 @@ Qualitative evaluation indicates:
 * Better conversational tone
 * Higher lexical authenticity
 
-Dialect-specific fine-tuning is known to significantly increase dialect generation accuracy and reduce standard-Arabic drift in Arabic LLMs. ([arXiv][2])
+Dialect-specific fine-tuning is known to significantly increase dialect generation accuracy and reduce standard-Arabic drift in Arabic LLMs.
 
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@@ -160,7 +160,7 @@ Runs efficiently on:
 
 * CPU inference (<1GB memory quantized)
 * Mobile NPUs
-* Edge devices ([Hugging Face][1])
+* Edge devices
 
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