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---
base_model: mlabonne/NeuralBeagle14-7B
inference: false
license: apache-2.0
model_creator: Maxime Labonne
model_name: NeuralBeagle14 7B
model_type: mistral
pipeline_tag: text-generation
quantized_by: Second State Inc.
tags:
- merge
- mergekit
- lazymergekit
- fblgit/UNA-TheBeagle-7b-v1
- argilla/distilabeled-Marcoro14-7B-slerp
- dpo
- rlhf
---
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<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://github.com/second-state/LlamaEdge/raw/dev/assets/logo.svg" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
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# NeuralBeagle14-7B-GGUF
## Original Model
[mlabonne/NeuralBeagle14-7B](https://huggingface.co./mlabonne/NeuralBeagle14-7B)
## Run with LlamaEdge
Note that the original model has an potential issue (see the discussion: [Model follows ChatML format, but does not have the special tokens for ChatML](https://huggingface.co./mlabonne/NeuralBeagle14-7B/discussions/3)).
<!-- - LlamaEdge version: [v0.2.8](https://github.com/second-state/LlamaEdge/releases/tag/0.2.8) and above
- Prompt template
- Prompt type: `chatml`
- Prompt string
```text
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- Context size: `4096`
- Run as LlamaEdge service
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:NeuralBeagle14-7B-Q5_K_M.gguf llama-api-server.wasm -p chatml
```
- Run as LlamaEdge command app
```bash
wasmedge --dir .:. --nn-preload default:GGML:AUTO:NeuralBeagle14-7B-Q5_K_M.gguf llama-chat.wasm -p chatml
``` -->
## Quantized GGUF Models
| Name | Quant method | Bits | Size | Use case |
| ---- | ---- | ---- | ---- | ----- |
| [NeuralBeagle14-7B-Q2_K.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q2_K.gguf) | Q2_K | 2 | 2.72 GB| smallest, significant quality loss - not recommended for most purposes |
| [NeuralBeagle14-7B-Q3_K_L.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| small, substantial quality loss |
| [NeuralBeagle14-7B-Q3_K_M.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| very small, high quality loss |
| [NeuralBeagle14-7B-Q3_K_S.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| very small, high quality loss |
| [NeuralBeagle14-7B-Q4_0.ggufs](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| legacy; small, very high quality loss - prefer using Q3_K_M |
| [NeuralBeagle14-7B-Q4_K_M.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| medium, balanced quality - recommended |
| [NeuralBeagle14-7B-Q4_K_S.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| small, greater quality loss |
| [NeuralBeagle14-7B-Q5_0.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| legacy; medium, balanced quality - prefer using Q4_K_M |
| [NeuralBeagle14-7B-Q5_K_M.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| large, very low quality loss - recommended |
| [NeuralBeagle14-7B-Q5_K_S.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| large, low quality loss - recommended |
| [NeuralBeagle14-7B-Q6_K.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q6_K.gguf) | Q6_K | 6 | 5.94 GB| very large, extremely low quality loss |
| [NeuralBeagle14-7B-Q8_0.gguf](https://huggingface.co./second-state/NeuralBeagle14-7B-GGUF/blob/main/NeuralBeagle14-7B-Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| very large, extremely low quality loss - not recommended |
|