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Folio1.Q4_K_M.gguf

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README.md

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+---
+tags:
+- Coding
+- Developmental
+- Multimodal
+- GGUF
+- Q_4_KM
+- Foundation
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-Coder-1.5B
+pipeline_tag: image-text-to-text
+---
+
+
+![Folio-1](folio.png)
+
+
+![Status](https://img.shields.io/badge/status-multimodal-blue) ![Params](https://img.shields.io/badge/parameters-1.5b-blue) ![Status](https://img.shields.io/badge/company-Foundation-black) 
+
+
+# Introducing the first coding model (1.5B), in joint development between *Foundation* and *AeroAI*
+**Trained on Foundation's *Forge* on MLX**
+
+**Available in Q4_K_M GGUF**
+
+
+<video controls autoplay muted loop playsinline width="720">
+  <source src="https://huggingface.co/AeroAIAviation/Folio-1/resolve/main/video.MP4" type="video/mp4">
+</video>
+
+
+
+## Intended Use
+
+- Coding and developmental work.
+
+  
+## Limitations and Safety
+
+> **Not for commercial and/or operational use.** Foundation's ***Folio-1*** is experimental and may produce incorrect, outdated, or fabricated answers.
+
+- Do **not** use it for commerical or cyber work. 
+- Small models may hallucinate, particularly on numeric and regulatory detail.
+
+
+
+## Built in development between Foundation and AeroAI.
+![foundation](foundationfolio.JPG)
+
+
+## Quick Start - Ollama 
+
+```
+ollama run hf.co/AeroAIAviation/Folio-1:Q4_K_M
+```
+
+### Python (Transformers)
+
+
+```bash
+pip install -U transformers torch accelerate
+```
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "AeroAI/Folio-1"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+messages = [{"role": "user", "content": "Code me a single file website. You decide the context and domain."}]
+inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+out = model.generate(inputs, max_new_tokens=256, temperature=0.7, do_sample=True)
+print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+### Apple Silicon (MLX)
+
+```bash
+pip install -U mlx-lm
+mlx_lm.generate --model AeroAI/Folio-1 --prompt "Explain Terminal shell commands and what they are." --max-tokens 256
+```
+
+## Model Details
+
+![Foundation](foliodesign.png)
+
+| Field | Value |
+|---|---|
+| Developer | Foundation + AeroAI |
+| Family | Folio |
+| Parameters | 1.5B |
+| Type | Causal language model (chat) |
+| Domain | Coding and developmental |
+| Input / Output | Text and image/ Text |
+| Context length | 32,000 |
+| Training data | AeroAI x Foundation Proprietary + Distillation |
+| Fine-tuning method | LoRa + FFT |
+| License | Apache 2.0 |
+
+
+## Benchmarks and performance (coding)
+| Benchmark | Score (%) |
+|---|---:|
+| HumanEval | 43.9 |
+| HumanEval+ | 36.6 |
+| MBPP | 69.2 |
+| MBPP+ | 58.6 |
+| MBPP (3-shot) | 59.2 |
+| BigCodeBench-Complete (Full) | 34.6 |
+| BigCodeBench-Complete (Hard) | 9.5 |
+
+
+
+## Training
+Folio-1 was trained on Foundation's ***FORGE*** 
+The model was trained in four stages: 
+1. Base model testing.
+2. Model training.
+3. Model testing.
+4. Post training/fine tuning and alignment.
+
+Training materials included AeroAI X Foundation's proprietary datasets, along with distilled datasets using Perplexity, OpenAI's ChatGPT, Oscilla, and Anthropic's Claude. 
+
+
+
+
+
+
+## Citation
+
+```bibtex
+@misc{aeroai_Folio-1,
+  title  = {Folio-1: An Experimental Coding and Developmental work model},
+  author = {{AeroAI}},
+  year   = {2026}
+}
+```
+
+## Contact/Support
+Website: https://huggingface.co/AeroAIAviation/Folio-1 https://foundationai-iota.vercel.app/models
+Email: aeroaiaviation@icloud.com or aeroaisupport@icloud.com

video.MP4

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