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fix review findings and additional clean up#116

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fix review findings and additional clean up#116
iamleonie wants to merge 2 commits into
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lm/review-findings-guides

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@iamleonie

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  • Addressed post-merge review feedback on the migration guide.
  • Simplified runtime guidance into GPU inference and edge/on-device deployment links. (Didn't contain much migration specific details)
  • Reworked migration guide sections for clearer navigation: Deployment runtimes, Chat Template, Sampling Configuration, Tool Use, and Fine-tuning.
  • Added section-level links from the intro and moved main documentation references to the bottom of each section.
  • Clarified sampling guidance and linked to the Prompting Guide for per-model recommendations.
  • Standardized tool-use guidance around the lfm2 parser and native Pythonic tool-call format. (confirmed its called lfm2 parser
  • Added reusable snippets for shared ChatML and tool-call examples.
  • Removed duplicated fine-tuning stack details and linked to the canonical fine-tuning docs.

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| Qwen3-4B/8B, Llama-3.1-8B, Gemma-3-12B | [LFM2.5-8B-A1B](https://huggingface.co/LiquidAI/LFM2.5-8B-A1B) | MoE with 8B total and 1.5B active parameters. |
| Qwen3-14B/32B and larger dense models | [LFM2-24B-A2B](https://huggingface.co/LiquidAI/LFM2-24B-A2B) | 24B total and roughly 2.3B active parameters. |
| Qwen3 thinking mode or DeepSeek distills | [LFM2.5-1.2B-Thinking](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Thinking) | Reasoning traces at 1.2B. |
| Qwen3-0.6B, Llama-3.2-1B, Gemma-3-270M/1B | [LFM2.5-230M](https://huggingface.co/LiquidAI/LFM2.5-230M) or [LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M) | Smallest text models for classification, extraction, routing, and tight memory budgets. |

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I noticed that we're talking about Qwen3 and Gemma 3 instead of Qwen3.5 and Gemma 4 everywhere. We should probably either update it when there's a clear replacement or have both when there's none (particularly true for Gemma 4).

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to reduce maintenance efforts for the future, i decided to remove all direct model IDs and replace them with more generic categories.

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