---
license: mit
tags:
- generated_from_trainer
base_model: Josephgflowers/TinyLlama-Cinder-Tiny-Agent
model-index:
- name: TinyLlama-Cinder-Agent-v1
  results: []
---
The goal of this Model is to build a Tinyllama model that can be used for tool usage, RAG, take system instructions, and as a general assistant.

This model is a fine-tuned version of [Josephgflowers/TinyLlama-Cinder-Tiny-Agent](https://huggingface.co./Josephgflowers/TinyLlama-Cinder-Tiny-Agent).

Special Thanks to https://nationtech.io/ for their generous sponorship in training this model.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6328952f798f8d122ce62a44/MbN_SXChmMxuHO8GjdUSc.png)

This model is a fine-tuned version of [Josephgflowers/TinyLlama-3T-Cinder-v1.2](https://huggingface.co./Josephgflowers/TinyLlama-3T-Cinder-v1.2) on https://huggingface.co./datasets/Josephgflowers/agent_1.

## Model description

This models is trained for RAG, Summary, Function Calling and Tool usage. Trained off of Cinder. Cinder is a chatbot designed for chat about STEM topics and storytelling. More information coming.


See https://huggingface.co./Josephgflowers/TinyLlama-Cinder-Agent-Rag/blob/main/tinyllama_agent_cinder_txtai-rag.py 
For usage example with wiki rag.

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 12
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
- mixed_precision_training: Native AMP

### Training results



### Framework versions

- Transformers 4.41.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_Josephgflowers__TinyLlama-Cinder-Agent-v1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |39.17|
|AI2 Reasoning Challenge (25-Shot)|34.90|
|HellaSwag (10-Shot)              |53.87|
|MMLU (5-Shot)                    |26.89|
|TruthfulQA (0-shot)              |39.08|
|Winogrande (5-shot)              |59.12|
|GSM8k (5-shot)                   |21.15|