---
base_model: jtatman/pythia-160m-dolphin-extended
library_name: transformers
license: apache-2.0
tags:
- axolotl
- relora
- generated_from_trainer
- TensorBlock
- GGUF
datasets:
- cognitivecomputations/dolphin
- llamafactory/alpaca_gpt4_en
language:
- en
metrics:
- accuracy
- bleu
- rouge
model-index:
- name: pythia-160m-dolphin-extended
  results: []
---

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## jtatman/pythia-160m-dolphin-extended - GGUF

This repo contains GGUF format model files for [jtatman/pythia-160m-dolphin-extended](https://huggingface.co/jtatman/pythia-160m-dolphin-extended).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4011](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

<div style="text-align: left; margin: 20px 0;">
    <a href="https://tensorblock.co/waitlist/client" style="display: inline-block; padding: 10px 20px; background-color: #007bff; color: white; text-decoration: none; border-radius: 5px; font-weight: bold;">
        Run them on the TensorBlock client using your local machine ↗
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</div>

## Prompt template

```
<|endoftext|>[INST] {prompt} [/INST]
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [pythia-160m-dolphin-extended-Q2_K.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q2_K.gguf) | Q2_K | 0.078 GB | smallest, significant quality loss - not recommended for most purposes |
| [pythia-160m-dolphin-extended-Q3_K_S.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q3_K_S.gguf) | Q3_K_S | 0.087 GB | very small, high quality loss |
| [pythia-160m-dolphin-extended-Q3_K_M.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q3_K_M.gguf) | Q3_K_M | 0.095 GB | very small, high quality loss |
| [pythia-160m-dolphin-extended-Q3_K_L.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q3_K_L.gguf) | Q3_K_L | 0.099 GB | small, substantial quality loss |
| [pythia-160m-dolphin-extended-Q4_0.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q4_0.gguf) | Q4_0 | 0.103 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [pythia-160m-dolphin-extended-Q4_K_S.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q4_K_S.gguf) | Q4_K_S | 0.104 GB | small, greater quality loss |
| [pythia-160m-dolphin-extended-Q4_K_M.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q4_K_M.gguf) | Q4_K_M | 0.110 GB | medium, balanced quality - recommended |
| [pythia-160m-dolphin-extended-Q5_0.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q5_0.gguf) | Q5_0 | 0.119 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [pythia-160m-dolphin-extended-Q5_K_S.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q5_K_S.gguf) | Q5_K_S | 0.119 GB | large, low quality loss - recommended |
| [pythia-160m-dolphin-extended-Q5_K_M.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q5_K_M.gguf) | Q5_K_M | 0.124 GB | large, very low quality loss - recommended |
| [pythia-160m-dolphin-extended-Q6_K.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q6_K.gguf) | Q6_K | 0.135 GB | very large, extremely low quality loss |
| [pythia-160m-dolphin-extended-Q8_0.gguf](https://huggingface.co/tensorblock/pythia-160m-dolphin-extended-GGUF/blob/main/pythia-160m-dolphin-extended-Q8_0.gguf) | Q8_0 | 0.175 GB | very large, extremely low quality loss - not recommended |


## Downloading instruction

### Command line

Firstly, install Huggingface Client

```shell
pip install -U "huggingface_hub[cli]"
```

Then, downoad the individual model file the a local directory

```shell
huggingface-cli download tensorblock/pythia-160m-dolphin-extended-GGUF --include "pythia-160m-dolphin-extended-Q2_K.gguf" --local-dir MY_LOCAL_DIR
```

If you wanna download multiple model files with a pattern (e.g., `*Q4_K*gguf`), you can try:

```shell
huggingface-cli download tensorblock/pythia-160m-dolphin-extended-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```