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---
base_model: lpetreadg/trained-tinyllama-ultrachat
inference: false
license: apache-2.0
model-index:
- name: trained-tinyllama-ultrachat
  results: []
model_creator: lpetreadg
model_name: trained-tinyllama-ultrachat
pipeline_tag: text-generation
quantized_by: afrideva
tags:
- generated_from_trainer
- gguf
- ggml
- quantized
- q2_k
- q3_k_m
- q4_k_m
- q5_k_m
- q6_k
- q8_0
---
# lpetreadg/trained-tinyllama-ultrachat-GGUF

Quantized GGUF model files for [trained-tinyllama-ultrachat](https://huggingface.co/lpetreadg/trained-tinyllama-ultrachat) from [lpetreadg](https://huggingface.co/lpetreadg)


| Name | Quant method | Size |
| ---- | ---- | ---- |
| [trained-tinyllama-ultrachat.q2_k.gguf](https://huggingface.co/afrideva/trained-tinyllama-ultrachat-GGUF/resolve/main/trained-tinyllama-ultrachat.q2_k.gguf) | q2_k | None  |
| [trained-tinyllama-ultrachat.q3_k_m.gguf](https://huggingface.co/afrideva/trained-tinyllama-ultrachat-GGUF/resolve/main/trained-tinyllama-ultrachat.q3_k_m.gguf) | q3_k_m | None  |
| [trained-tinyllama-ultrachat.q4_k_m.gguf](https://huggingface.co/afrideva/trained-tinyllama-ultrachat-GGUF/resolve/main/trained-tinyllama-ultrachat.q4_k_m.gguf) | q4_k_m | None  |
| [trained-tinyllama-ultrachat.q5_k_m.gguf](https://huggingface.co/afrideva/trained-tinyllama-ultrachat-GGUF/resolve/main/trained-tinyllama-ultrachat.q5_k_m.gguf) | q5_k_m | None  |
| [trained-tinyllama-ultrachat.q6_k.gguf](https://huggingface.co/afrideva/trained-tinyllama-ultrachat-GGUF/resolve/main/trained-tinyllama-ultrachat.q6_k.gguf) | q6_k | None  |
| [trained-tinyllama-ultrachat.q8_0.gguf](https://huggingface.co/afrideva/trained-tinyllama-ultrachat-GGUF/resolve/main/trained-tinyllama-ultrachat.q8_0.gguf) | q8_0 | None  |



## Original Model Card:
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# trained-tinyllama-ultrachat

This model is a fine-tuned version of [PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T](https://huggingface.co/PY007/TinyLlama-1.1B-intermediate-step-715k-1.5T) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3258

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3767        | 0.08  | 100  | 1.3685          |
| 1.3494        | 0.17  | 200  | 1.3490          |
| 1.3436        | 0.25  | 300  | 1.3389          |
| 1.3231        | 0.33  | 400  | 1.3331          |
| 1.3278        | 0.42  | 500  | 1.3296          |
| 1.3214        | 0.5   | 600  | 1.3276          |
| 1.3376        | 0.58  | 700  | 1.3266          |
| 1.3227        | 0.67  | 800  | 1.3261          |
| 1.3329        | 0.75  | 900  | 1.3259          |
| 1.3185        | 0.83  | 1000 | 1.3258          |
| 1.332         | 0.92  | 1100 | 1.3258          |


### Framework versions

- Transformers 4.34.1
- Pytorch 2.0.1+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1