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Model: tigerisaac/meeko-1-preview
Source: Original Platform

README.md

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+---
+language:
+- en
+library_name: transformers
+pipeline_tag: text-generation
+license: other
+base_model: LiquidAI/LFM2.5-1.2B-Instruct
+tags:
+- lfm2
+- lfm2.5
+- transcript-cleanup
+- dictation
+- text-editing
+- unsloth
+- gguf
+- llama-cpp
+---
+
+# Meeko 1 Preview
+
+<p align="center">
+  <img src="assets/meeko.jpeg" alt="Meeko the cat" width="520">
+</p>
+
+**Meeko 1 Preview** is a 1.2B-parameter transcript-cleanup model. It turns raw
+speech-to-text output into concise written text with **no system prompt**.
+
+The model is named after Meeko, the cat pictured above.
+
+## What it does
+
+Given a raw transcript as the user message, Meeko returns only the cleaned text.
+Its target editing register is medium-touch written English:
+
+- fix punctuation, capitalization, spacing, and grammar;
+- remove speech fillers and stutters;
+- expand casual forms such as “gonna” to “going to”;
+- split run-on sentences when needed;
+- format numbers, dates, times, money, percentages, symbols, and lists;
+- resolve explicit self-corrections so the final value wins;
+- preserve intent, facts, hedges, contrasts, identifiers, and all
+  non-retracted content.
+
+It is an editor, not a general-purpose chat assistant. Do not add a system
+prompt for the intended cleanup task.
+
+## Quick start
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "tigerisaac/meeko-1-preview"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+
+messages = [{
+    "role": "user",
+    "content": "um so i was gonna send it friday no wait monday morning",
+}]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+
+with torch.inference_mode():
+    generated = model.generate(
+        inputs,
+        max_new_tokens=128,
+        do_sample=False,
+    )
+
+new_tokens = generated[0, inputs.shape[-1]:]
+print(tokenizer.decode(new_tokens, skip_special_tokens=True))
+# I was going to send it Monday morning.
+```
+
+## GGUF (llama.cpp)
+
+A pre-quantized **Q4_K_M** build is included for local inference:
+
+- File: `meeko-1-preview-Q4_K_M.gguf`
+- No system prompt; pass the raw transcript as the user message.
+
+```bash
+llama-server -m meeko-1-preview-Q4_K_M.gguf \
+  --host 127.0.0.1 --port 8080 \
+  --ctx-size 16384 --parallel 1 --reasoning off
+```
+
+## Training
+
+- **Base model:** `LiquidAI/LFM2.5-1.2B-Instruct`
+- **Method:** supervised fine-tuning with Unsloth
+- **Epochs:** 2
+- **Training format:** one raw transcript user message followed by one cleaned
+  assistant response; no system message
+- **Corpus:** 11,438 curated cleanup pairs, assembled into 10,833 training and
+  570 validation examples after deduplication
+- **Label target:** medium-touch written-English cleanup
+
+The corpus includes ordinary dictation, fillers, stutters, number and symbol
+formatting, listification, explicit retractions, and matched contrast/hedge
+examples that must not be over-edited. Constructed number and retraction rows
+use mechanically verified labels.
+
+## License
+
+This fine-tune is derived from `LiquidAI/LFM2.5-1.2B-Instruct` and is released
+under the applicable upstream model terms. Review the
+[base model repository](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct)
+before use or redistribution.
+
+## Acknowledgments
+
+Built with Liquid AI's LFM2.5 base model and Unsloth. Named for Meeko, who
+provided supervision of the non-gradient variety.