File size: 6,498 Bytes

---
license: cc-by-nc-4.0
datasets:
- argilla/dpo-mix-7k
- nvidia/HelpSteer
- jondurbin/airoboros-3.2
- hkust-nlp/deita-10k-v0
- LDJnr/Capybara
- HPAI-BSC/CareQA
- GBaker/MedQA-USMLE-4-options
- lukaemon/mmlu
- bigbio/pubmed_qa
- openlifescienceai/medmcqa
- bigbio/med_qa
- HPAI-BSC/better-safe-than-sorry
- HPAI-BSC/pubmedqa-cot
- HPAI-BSC/medmcqa-cot
- HPAI-BSC/medqa-cot
language:
- en
library_name: transformers
tags:
- biology
- medical
- TensorBlock
- GGUF
pipeline_tag: question-answering
base_model: HPAI-BSC/Llama3-Aloe-8B-Alpha
---

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## HPAI-BSC/Llama3-Aloe-8B-Alpha - GGUF

This repo contains GGUF format model files for [HPAI-BSC/Llama3-Aloe-8B-Alpha](https://huggingface.co/HPAI-BSC/Llama3-Aloe-8B-Alpha).

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).


## Our projects
<table border="1" cellspacing="0" cellpadding="10">
<tr>
  <th style="font-size: 25px;">Awesome MCP Servers</th>
  <th style="font-size: 25px;">TensorBlock Studio</th>
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  <tr>
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    <th><img src="https://imgur.com/pJcmF5u.jpeg" alt="Project B" width="450"/></th>
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    <th>A comprehensive collection of Model Context Protocol (MCP) servers.</th>
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    ">👀 See what we built 👀</a>
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</table>
## Prompt template


```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>

{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>

{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama3-Aloe-8B-Alpha-Q2_K.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q2_K.gguf) | Q2_K | 2.961 GB | smallest, significant quality loss - not recommended for most purposes |
| [Llama3-Aloe-8B-Alpha-Q3_K_S.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q3_K_S.gguf) | Q3_K_S | 3.413 GB | very small, high quality loss |
| [Llama3-Aloe-8B-Alpha-Q3_K_M.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q3_K_M.gguf) | Q3_K_M | 3.743 GB | very small, high quality loss |
| [Llama3-Aloe-8B-Alpha-Q3_K_L.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q3_K_L.gguf) | Q3_K_L | 4.025 GB | small, substantial quality loss |
| [Llama3-Aloe-8B-Alpha-Q4_0.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q4_0.gguf) | Q4_0 | 4.341 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [Llama3-Aloe-8B-Alpha-Q4_K_S.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q4_K_S.gguf) | Q4_K_S | 4.370 GB | small, greater quality loss |
| [Llama3-Aloe-8B-Alpha-Q4_K_M.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q4_K_M.gguf) | Q4_K_M | 4.583 GB | medium, balanced quality - recommended |
| [Llama3-Aloe-8B-Alpha-Q5_0.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q5_0.gguf) | Q5_0 | 5.215 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [Llama3-Aloe-8B-Alpha-Q5_K_S.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q5_K_S.gguf) | Q5_K_S | 5.215 GB | large, low quality loss - recommended |
| [Llama3-Aloe-8B-Alpha-Q5_K_M.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q5_K_M.gguf) | Q5_K_M | 5.339 GB | large, very low quality loss - recommended |
| [Llama3-Aloe-8B-Alpha-Q6_K.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q6_K.gguf) | Q6_K | 6.143 GB | very large, extremely low quality loss |
| [Llama3-Aloe-8B-Alpha-Q8_0.gguf](https://huggingface.co/tensorblock/Llama3-Aloe-8B-Alpha-GGUF/blob/main/Llama3-Aloe-8B-Alpha-Q8_0.gguf) | Q8_0 | 7.954 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/Llama3-Aloe-8B-Alpha-GGUF --include "Llama3-Aloe-8B-Alpha-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/Llama3-Aloe-8B-Alpha-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```