README.md

---
license: apache-2.0
language:
- en
pipeline_tag: text-generation
library_name: transformers
base_model: SakanaAI/TAID-LLM-1.5B
datasets:
- HuggingFaceTB/smollm-corpus
tags:
- llama-cpp
- gguf-my-repo
---

# Triangle104/TAID-LLM-1.5B-Q8_0-GGUF
This model was converted to GGUF format from [`SakanaAI/TAID-LLM-1.5B`](https://huggingface.co/SakanaAI/TAID-LLM-1.5B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/SakanaAI/TAID-LLM-1.5B) for more details on the model.

---
TAID-LLM-1.5B is an English language model created 
through TAID (Temporally Adaptive Interpolated Distillation), our new 
knowledge distillation method. 
We used Qwen2-72B-Instruct as the teacher model and Qwen2-1.5B-Instruct as the student model. 



	
		
	

		Model Details
	



Developed by: Sakana AI
Model type: Autoregressive Language Model
Language(s): English
License: Apache License, Version 2.0
Paper: https://arxiv.org/abs/2501.16937
Blog: https://sakana.ai/taid/



	
		
	

		Uses
	



This model is provided for research and development purposes only and
 should be considered as an experimental prototype. 
It is not intended for commercial use or deployment in mission-critical 
environments. 
Use of this model is at the user's own risk, and its performance and 
outcomes are not guaranteed. 
Sakana AI shall not be liable for any direct, indirect, special, 
incidental, or consequential damages, or any loss arising from the use 
of this model, regardless of the results obtained. 
Users must fully understand the risks associated with the use of this 
model and use it at their own discretion.



	
		
	

		Acknowledgement
	



We would like to thank the developers of the source models for their contributions and for making their work available. 


This model is based on results obtained from a project, JPNP20017, 
subsidized by the New Energy and Industrial Technology Development 
Organization (NEDO).



	
		
	

		Citation
	



@misc{sakana2025taid,
      title         = {TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models}, 
      author.       = {Makoto Shing and Kou Misaki and Han Bao and Sho Yokoi and Takuya Akiba},
      year          = {2025},
      eprint        = {2501.16937},
      archivePrefix = {arXiv},
      primaryClass  = {cs.LG},
      url           = {https://arxiv.org/abs/2501.16937}

---
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo Triangle104/TAID-LLM-1.5B-Q8_0-GGUF --hf-file taid-llm-1.5b-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo Triangle104/TAID-LLM-1.5B-Q8_0-GGUF --hf-file taid-llm-1.5b-q8_0.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/TAID-LLM-1.5B-Q8_0-GGUF --hf-file taid-llm-1.5b-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo Triangle104/TAID-LLM-1.5B-Q8_0-GGUF --hf-file taid-llm-1.5b-q8_0.gguf -c 2048
```