README.md

---
language:
- en
library_name: transformers
pipeline_tag: text-generation
datasets:
- jondurbin/airoboros-2.2
- Open-Orca/OpenOrca
- garage-bAInd/Open-Platypus
- WizardLM/WizardLM_evol_instruct_V2_196k
- TokenBender/python_eval_instruct_51k
tags:
- llama-2
- code
- TensorBlock
- GGUF
license: llama2
base_model: uukuguy/speechless-coding-7b-16k-tora
model-index:
- name: SpeechlessCoder
  results:
  - task:
      type: text-generation
    dataset:
      name: HumanEval
      type: openai_humaneval
    metrics:
    - type: pass@1
      value: 52.439
      name: pass@1
      verified: false
---

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## uukuguy/speechless-coding-7b-16k-tora - GGUF

This repo contains GGUF format model files for [uukuguy/speechless-coding-7b-16k-tora](https://huggingface.co/uukuguy/speechless-coding-7b-16k-tora).

The files were quantized using machines provided by [TensorBlock](https://tensorblock.co/), and they are compatible with llama.cpp as of [commit b4242](https://github.com/ggerganov/llama.cpp/commit/a6744e43e80f4be6398fc7733a01642c846dce1d).

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## Prompt template

```

```

## Model file specification

| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [speechless-coding-7b-16k-tora-Q2_K.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q2_K.gguf) | Q2_K | 2.533 GB | smallest, significant quality loss - not recommended for most purposes |
| [speechless-coding-7b-16k-tora-Q3_K_S.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q3_K_S.gguf) | Q3_K_S | 2.948 GB | very small, high quality loss |
| [speechless-coding-7b-16k-tora-Q3_K_M.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q3_K_M.gguf) | Q3_K_M | 3.298 GB | very small, high quality loss |
| [speechless-coding-7b-16k-tora-Q3_K_L.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q3_K_L.gguf) | Q3_K_L | 3.597 GB | small, substantial quality loss |
| [speechless-coding-7b-16k-tora-Q4_0.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q4_0.gguf) | Q4_0 | 3.826 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [speechless-coding-7b-16k-tora-Q4_K_S.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q4_K_S.gguf) | Q4_K_S | 3.857 GB | small, greater quality loss |
| [speechless-coding-7b-16k-tora-Q4_K_M.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q4_K_M.gguf) | Q4_K_M | 4.081 GB | medium, balanced quality - recommended |
| [speechless-coding-7b-16k-tora-Q5_0.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q5_0.gguf) | Q5_0 | 4.652 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [speechless-coding-7b-16k-tora-Q5_K_S.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q5_K_S.gguf) | Q5_K_S | 4.652 GB | large, low quality loss - recommended |
| [speechless-coding-7b-16k-tora-Q5_K_M.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q5_K_M.gguf) | Q5_K_M | 4.783 GB | large, very low quality loss - recommended |
| [speechless-coding-7b-16k-tora-Q6_K.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q6_K.gguf) | Q6_K | 5.529 GB | very large, extremely low quality loss |
| [speechless-coding-7b-16k-tora-Q8_0.gguf](https://huggingface.co/tensorblock/speechless-coding-7b-16k-tora-GGUF/blob/main/speechless-coding-7b-16k-tora-Q8_0.gguf) | Q8_0 | 7.161 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/speechless-coding-7b-16k-tora-GGUF --include "speechless-coding-7b-16k-tora-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/speechless-coding-7b-16k-tora-GGUF --local-dir MY_LOCAL_DIR --local-dir-use-symlinks False --include='*Q4_K*gguf'
```