Update spellchecker model card and license

Adds the drafted spellchecker model card README and LFM Open License file.

LICENSE

@@ -0,0 +1,71 @@
+LFM Open License v1.0 
+
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README.md

@@ -1,5 +1,4 @@
 ---
-license: other
 language:
 - en
 - de
@@ -13,31 +12,72 @@ language:
 - zh
 - ko
 tags:
+- liquid
+- lfm2
+- lfm2.5
+- bidirectional
+- masked-lm
+- encoder
 - grammatical-error-correction
 - gec
 - spell-check
 - token-classification
 - gector
-pipeline_tag: token-classification
 library_name: transformers
+license: other
+license_name: lfm1.0
+license_link: LICENSE
+pipeline_tag: token-classification
+base_model:
+    - LiquidAI/LFM2.5-Encoder-350M
 ---
 
-# LFM2.5-Spellchecker-350M
+<div align="center">
+  <img 
+    src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/2b08LKpev0DNEk6DlnWkY.png" 
+    alt="Liquid AI" 
+    style="width: 100%; max-width: 100%; height: auto; display: inline-block; margin-bottom: 0.5em; margin-top: 0.5em;"
+  />
+  <div style="display: flex; justify-content: center; gap: 0.5em; margin-bottom: 1em;">
+    <a href="https://playground.liquid.ai/"><strong>Try LFM</strong></a> • 
+    <a href="https://docs.liquid.ai/lfm/getting-started/welcome"><strong>Docs</strong></a> • 
+    <a href="https://leap.liquid.ai/"><strong>LEAP</strong></a> • 
+    <a href="https://discord.com/invite/liquid-ai"><strong>Discord</strong></a>
+  </div>
+</div>
 
-A subword-level **GECToR-style grammatical-error-correction tagger** (~357M params) on the
-LiquidAI LFM2.5 bidirectional MLM encoder. It predicts a per-BPE-piece edit tag and applies
-edits iteratively (`KEEP / DELETE / REPLACE / APPEND`), covering grammar, spelling, punctuation, and
-casing across 11 languages. Selected automatically as the best checkpoint by the **MASTER composite**
-(a weighted aggregate over native, learner, robustness, precision and multilingual evals; leaderboard
-rank **#1**, source run `en/stage3_ptv2`).
+# LFM2.5-Encoder-350-Spellchecker
 
-> ⚠️ Loads custom code via `trust_remote_code=True` (the model wraps a `trust_remote_code` encoder).
+A full fine-tune of [LFM2.5-Encoder-350M](https://huggingface.co/LiquidAI/LFM2.5-Encoder-350M) with a subword-level **GECToR-style grammatical-error-correction tagger** (~357M params). It predicts a per-BPE-piece edit tag and applies edits iteratively (`KEEP / DELETE / REPLACE / APPEND`), covering grammar, spelling, punctuation, and casing across 11 languages. Selected automatically as the best checkpoint by the **MASTER composite** (a weighted aggregate over native, learner, robustness, precision and multilingual evals; leaderboard rank **#1**, source run `en/stage3_ptv2`).
+
+Find more details about our encoders in our [blog post](https://www.liquid.ai/blog/lfm2-5-encoders).
+
+> [!NOTE]
+> 💻 **Demos**: Try this fine-tuned model running in a CPU-only Hugging Face space:
+> **[Spell checking](https://huggingface.co/spaces/LiquidAI/spellchecker)** — correct misspellings token by token.
 
 ## Usage
 
+> ⚠️ Loads custom code via `trust_remote_code=True` (the model wraps a `trust_remote_code` encoder).
+
+Install the required packages:
+
+```bash
+pip install torch transformers
+```
+
+Run spell checking:
+
 ```python
 from transformers import AutoModel
-model = AutoModel.from_pretrained("LiquidAI/LFM2.5-Spellchecker-350M", trust_remote_code=True)
+
+model_id = "LiquidAI/LFM2.5-Encoder-350-Spellchecker"
+
+model = AutoModel.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+).float().eval()
+
 print(model.correct(["She go to school every day ."]))
 # ['She goes to school every day .']
 ```
@@ -70,10 +110,19 @@ Fixed inference setting: `max_iter=4`, precision knobs off. Headline [ERRANT](ht
 | `Their are many reason to study hard .` | `There are many reasons to study hard .` |
 | `He don&#x27;t like coffee but he like tea .` | `He does n&#x27;t like coffee , but he likes tea .` |
 
-## Files
+## 📬 Contact
 
-- `model.safetensors` — fp16 weights
-- `modeling_gectagger.py` — self-contained architecture + iterative decode (loaded via `trust_remote_code`)
-- INT4/GGUF variant — see repo files if present (encoder quantized to Q4_K_M for llama.cpp)
+- Got questions or want to connect? [Join our Discord community](https://discord.com/invite/liquid-ai)
+- If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact).
 
-*Auto-generated by `scripts/publish_best.py` from run `en/stage3_ptv2`.*
+## Citation
+
+```bibtex
+@article{liquidAI2026Encoders,
+  author = {Liquid AI},
+  title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
+  journal = {Liquid AI Blog},
+  year = {2026},
+  note = {www.liquid.ai/blog/lfm2-5-encoders},
+}
+```