Add README

README.md

@@ -1,71 +1,23 @@
 ---
-license: apache-2.0
-base_model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit
-language:
-- en
-- de
 tags:
-- creative
-- flash
-- roleplay
+- gguf
+- llama.cpp
 - unsloth
-- lazyloopstudio
-- nomi
-- creative_writing
-- nomi flash
-- nomi 1
-model_name: Nomi-1-Flash
-datasets:
-- databricks/databricks-dolly-15k
-metrics:
-- character
-pipeline_tag: text-generation
-library_name: transformers
----
-
-<div align="center">
-  
-![Znomi_nbg](https://cdn-uploads.huggingface.co/production/uploads/6921fa6332f7fb129563d495/9oOMYWNoxBDmF854MciTn.png)
-
-</div>
-
-# Nomi-1-Flash ⚡
-
-**Nomi-1-Flash** is a high-speed, creative-focused AI companion based on the Qwen 2.5 3B architecture. Developed by **LazyLoopStudio**, this model is fine-tuned to prioritize vibrant personality, creative vocabulary, and rapid responses over rigid technical instruction following.
-
-
 
-## 📊 Evaluation & Benchmarks
-
-We believe in transparency. Nomi-1-Flash was tested using a custom evaluation suite to measure its "Creative First" approach.
-
-| Metric | Score | Interpretation |
-| :--- | :---: | :--- |
-| **Creativity Index** | **100.0%** | Exceptional vocabulary diversity and imaginative flair. |
-| **General Knowledge (MMLU)** | **48.0%** | Solid factual foundation, comparable to mid-sized models. |
-| **Instruction Following (IFEval)** | **33.3%** | Low. Nomi tends to prioritize style over strict formatting rules. |
-
-### Summary
-Nomi-1-Flash is **not** a coding or logic expert. She is a storyteller and a conversationalist. While her instruction following is lower than the base model, her creative output is significantly more engaging and human-like.
-
-## 🚀 Quick Start (Inference)
-
-To use Nomi-1-Flash in Python (requires `unsloth` or `transformers`):
+---
 
-```python
-from unsloth import FastLanguageModel
-import torch
+# Nomi-1-Flash : GGUF
 
-model, tokenizer = FastLanguageModel.from_pretrained(
-    model_name = "LazyLoopStudio/Nomi-1-Flash",
-    max_seq_length = 2048,
-    load_in_4bit = True,
-)
-FastLanguageModel.for_inference(model)
+This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
 
-prompt = "Write a creative opening for a story about a neon-lit cloud city."
-inputs = tokenizer([f"<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"], return_tensors="pt").to("cuda")
+**Example usage**:
+- For text only LLMs:    `./llama.cpp/llama-cli -hf LazyLoopStudio/Nomi-1-Flash --jinja`
+- For multimodal models: `./llama.cpp/llama-mtmd-cli -hf LazyLoopStudio/Nomi-1-Flash --jinja`
 
-outputs = model.generate(**inputs, max_new_tokens=256)
-print(tokenizer.batch_decode(outputs)[0])
+## Available Model files:
+- `Qwen2.5-3B-Instruct.Q8_0.gguf`
 
+## Ollama
+An Ollama Modelfile is included for easy deployment.
+This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)