Flexan/haphazardlyinc-Andy-Feather-V2-700m-GGUF

GGUF Files for Andy-Feather-V2-700m

These are the GGUF files for haphazardlyinc/Andy-Feather-V2-700m.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	IQ3_XS	Integer quant
Download	Q3_K_S
Download	IQ3_S	Integer quant, preferable over Q3_K_S
Download	IQ3_M	Integer quant
Download	Q3_K_M
Download	Q3_K_L
Download	IQ4_XS	Integer quant
Download	Q4_K_S	Fast with good performance
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q5_K_S
Download	Q5_K_M
Download	Q6_K	Very good quality
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

Note from Flexan

I provide GGUFs and quantizations of publicly available models that do not have a GGUF equivalent available yet. This process is not yet automated and I download, convert, quantize, and upload them by hand, usually for models I deem interesting and wish to try out.

If there are some quants missing that you'd like me to add, you may request one in the community tab. If you want to request a public model to be converted, you can also request that in the community tab. If you have questions regarding the model, please refer to the original model repo.

Model Card for Andy Feather 700M

⚠️⚠️⚠️IMPORTANT⚠️⚠️⚠️ In its current state, this model DOES NOT perform very well with Mindcraft and can only do very rudimentary tasks. It is a HUGE step up from V1, but still has absolutely ABYSMAL performance.

This model is a fine-tuned LoRA adapter built on top of LiquidAI/LFM2-700M.
It is designed for CPU inference or those who are GPU poor, requiring sub 1gb of memory to load the model at Q8 precision. The model is NOT compatible with Ollama, it is highly recommended to use LMStudio instead. Here is an example Mindcraft profile:

{
    "name": "andy",
    "model": {
        "api": "openai",
        "url": "http://localhost:1234/v1",
        "model": "Andy-Feather-V2-700m"
    },
    "embedding": {
        "api": "openai",
        "url": "http://localhost:1234/v1",
        "model": "text-embedding-nomic-embed-text-v1.5"
    }
}

And an example Mindcraft keys.json:

{
    "OPENAI_API_KEY": "http://localhost:1234/v1",
    "OPENAI_ORG_ID": "",
    "GEMINI_API_KEY": "",
    "ANTHROPIC_API_KEY": "",
    "REPLICATE_API_KEY": "",
    "GROQCLOUD_API_KEY": "",
    "HUGGINGFACE_API_KEY": "",
    "QWEN_API_KEY": "",
    "XAI_API_KEY": "",
    "MISTRAL_API_KEY": "",
    "DEEPSEEK_API_KEY": "",
    "GHLF_API_KEY": "",
    "HYPERBOLIC_API_KEY": "",
    "NOVITA_API_KEY": "",
    "OPENROUTER_API_KEY": "",
    "CEREBRAS_API_KEY": "",
    "MERCURY_API_KEY":""
}

Training Data

This model was trained on the following datasets:

• Sweaterdog/Andy-base-2
• Sweaterdog/Andy-4-base
• Sweaterdog/Andy-4-FT

Dataset License

The training data is subject to the Andy 1.0 License

This work uses data and models created by @Sweaterdog.

---

@misc{vonwerra2022trl,
  title        = {{TRL: Transformer Reinforcement Learning}},
  author       = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
  year         = {2020},
  journal      = {GitHub repository},
  publisher    = {GitHub},
  howpublished = {\url{https://github.com/huggingface/trl}}
}

@misc{liquidai_lfm2_700m,
  title        = {LFM2-700M},
  author       = {Liquid AI},
  year         = {2024},
  howpublished = {\url{https://huggingface.co/LiquidAI/LFM2-700M}}
}