File size: 1,998 Bytes
abdf333 81dcf54 550cc0e abdf333 5ca3198 a91b842 672c426 a91b842 672c426 6def784 a91b842 6def784 a91b842 6def784 22b9a0d 04b8b68 a91b842 6def784 a91b842 5c1cb7f a91b842 5c1cb7f a91b842 5c1cb7f b426132 caac39b 5c1cb7f b426132 5c1cb7f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 |
---
license: mit
language:
- en
pipeline_tag: text-generation
library_name: transformers
tags:
- text-generation-inference
new_version: GoofyLM/N2-Nemo
---
*Banner by [Croissant](https://huggingface.co/CroissantWhyNot)*
# N1 - A Chain-of-Thought Language Model
N1 is a small, experimental Chain-of-Thought (COT) model based on the LLaMA architecture, developed by GoofyLM.
## Model Details
- **Architecture**: LLaMA-based
- **Parameter Count**: 135M
- **Training Data**: Closed-source dataset
- **Special Features**: Chain-of-Thought reasoning capabilities
- **Note**: The model often shows "schizophrenia"
- **Note**: You may need to add this Jinja to the model:
```jinja
{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system
You are a helpful AI assistant named N1, trained by GoofyLM<|im_end|>
' }}{% endif %}{{'<|im_start|>' + message['role'] + '
' + message['content'] + '<|im_end|>' + '
'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
' }}{% endif %}
```
- ONNX available at [onnx-community/N1-ONNX](https://huggingface.co/onnx-community/N1-ONNX).
## Intended Use
This model is designed for text generation tasks with a focus on reasoning through problems step-by-step (using its Chain-of-Thought).
## Limitations
- Small parameter size may limit reasoning capabilities
- May produce unstable or inconsistent outputs
- Not suitable for production use without further testing
---
## Usage
The model can be loaded using the following:
### Transformers:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("GoofyLM/N1")
tokenizer = AutoTokenizer.from_pretrained("GoofyLM/N1")
```
### llama-cpp-python:
```python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="GoofyLM/N1-quant",
filename="N1_Q8_0.gguf",
)
```
### Ollama:
```python
ollama run hf.co/GoofyLM/N1-quant:Q8_0
``` |