README.md

---
license: llama2
datasets:
- rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored
model_name: LosslessMegaCoder Llama2 13B Mini
inference: false
model_creator: Rombo Dawg
model_link: https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-13b-mini
model_type: llama
quantized_by: TheBloke
base_model: rombodawg/LosslessMegaCoder-llama2-13b-mini
---

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# LosslessMegaCoder Llama2 13B Mini - GGML
- Model creator: [Rombo Dawg](https://huggingface.co/rombodawg)
- Original model: [LosslessMegaCoder Llama2 13B Mini](https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-13b-mini)

## Description

This repo contains GGML format model files for [Rombo Dawg's LosslessMegaCoder Llama2 13B Mini](https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-13b-mini).

### Important note regarding GGML files.

The GGML format has now been superseded by GGUF. As of August 21st 2023, [llama.cpp](https://github.com/ggerganov/llama.cpp) no longer supports GGML models. Third party clients and libraries are expected to still support it for a time, but many may also drop support.

Please use the GGUF models instead.
### About GGML

GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most popular web UI. Supports NVidia CUDA GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with CUDA GPU acceleration via the c_transformers backend.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.

## Repositories available

* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGUF)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML)
* [Rombo Dawg's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/rombodawg/LosslessMegaCoder-llama2-13b-mini)

## Prompt template: ChatML

```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant

```

<!-- compatibility_ggml start -->
## Compatibility

These quantised GGML files are compatible with llama.cpp between June 6th (commit `2d43387`) and August 21st 2023.

For support with latest llama.cpp, please use GGUF files instead.

The final llama.cpp commit with support for GGML was: [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa)

As of August 23rd 2023 they are still compatible with all UIs, libraries and utilities which use GGML. This may change in the future.

## Explanation of the new k-quant methods
<details>
  <summary>Click to see details</summary>

The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.

Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_ggml end -->

## Provided files

| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q2_K.bin) | q2_K | 2 | 5.74 GB| 8.24 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 5.87 GB| 8.37 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 6.53 GB| 9.03 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 7.14 GB| 9.64 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q4_0.bin) | q4_0 | 4 | 7.32 GB| 9.82 GB | Original quant method, 4-bit. |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 7.56 GB| 10.06 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 8.06 GB| 10.56 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q4_1.bin) | q4_1 | 4 | 8.14 GB| 10.64 GB | Original quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q5_0.bin) | q5_0 | 5 | 8.95 GB| 11.45 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 9.14 GB| 11.64 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 9.40 GB| 11.90 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q5_1.bin) | q5_1 | 5 | 9.76 GB| 12.26 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q6_K.bin) | q6_K | 6 | 10.83 GB| 13.33 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
| [losslessmegacoder-llama2-13b-min.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GGML/blob/main/losslessmegacoder-llama2-13b-min.ggmlv3.q8_0.bin) | q8_0 | 8 | 13.83 GB| 16.33 GB | Original quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |

**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

## How to run in `llama.cpp`

Make sure you are using `llama.cpp` from commit [dadbed99e65252d79f81101a392d0d6497b86caa](https://github.com/ggerganov/llama.cpp/commit/dadbed99e65252d79f81101a392d0d6497b86caa) or earlier.

For compatibility with latest llama.cpp, please use GGUF files instead.

```
./main -t 10 -ngl 32 -m losslessmegacoder-llama2-13b-min.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.

Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.

Change `-c 2048` to the desired sequence length for this model. For example, `-c 4096` for a Llama 2 model.  For models that use RoPE, add `--rope-freq-base 10000 --rope-freq-scale 0.5` for doubled context, or `--rope-freq-base 10000 --rope-freq-scale 0.25` for 4x context.

If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`

For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)

## How to run in `text-generation-webui`

Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).

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## Thanks, and how to contribute.

Thanks to the [chirper.ai](https://chirper.ai) team!

I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.

If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.

Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.

* Patreon: https://patreon.com/TheBlokeAI
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**Special thanks to**: Aemon Algiz.

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# Original model card: Rombo Dawg's LosslessMegaCoder Llama2 13B Mini

___________________________
- Please note this model was not trained on the rombodawg/LosslessMegaCodeTrainingV3_MINI dataset, despite the name similarity. You can find the training data at the bottom of the model card labeled (megacode2-min100)
___________________________

This is one of the first models trained on the LosslessMegaCodeTrainingV2_1m_Evol_Uncensored dataset. The version of the dataset used for this model was filtered by removed any data with less than 100 tokens but plans for much more refined filtering are in the works

- This model was made as a colaboration between me and andreaskoepf who is an affiliate of Open Assistant.

This Model score .29 on humaneval+ the same as LLaMA-2 70B Chat Link bellow (in this benchmark the model is called andreaskoepf/llama2-13b-megacode2_min100)

- https://tju01.github.io/FastEval-OpenAssistant/

Prompt template:

- chatml format is used: "<|im_start|>system\n{system message}<|im_end|>\n<|im_start|>user\n{user prompt}<|im_end|>\n<|im_start|>assistant\n{Assistant answer}<|im_end|>\n"

multi-line:
```
<|im_start|>system
{system message}<|im_end|>
<|im_start|>user
{user prompt}<|im_end|>
<|im_start|>assistant
{Assistant answer}<|im_end|>
```
Gpt4all template:

- System prompt
```
<|im_start|>system
"Below is an instruction that describes a task. Write a response that appropriately completes the request."
```
- Prompt template
```
<|im_end|>
<|im_start|>user
"%1"<|im_end|>
<|im_start|>assistant
```

Oobagooba Text-Generation-Webui Template
- user:
```
  <|im_start|>user
  {User string}<|im_end|>
```
- bot:
```
  <|im_start|>assistant
  {Bot string}<|im_end|>
```
- turn_template:
```
<|user|>\n<|user-message|>\n\n<|bot|>\n<|bot-message|>\n\n
```
- context:
```
  <|im_start|>system
  Below is an instruction that describes a task. Write a response that appropriately completes the request.<|im_end|>
```

Current quantizations available:

- https://huggingface.co/TheBloke/LosslessMegaCoder-Llama2-13B-Mini-GPTQ

Training data:

- https://wandb.ai/open-assistant/epfl-mt-sft/runs/run34_megacode2_min100_13b

The link for the full dataset is bellow:

- https://huggingface.co/datasets/rombodawg/LosslessMegaCodeTrainingV2_1m_Evol_Uncensored

Link for the filtered dataset used to make this model are bellow:

- https://huggingface.co/datasets/andreaskoepf/megacode2-min100

The original posting for this model was uploaded at the link bellow.

- https://huggingface.co/andreaskoepf/llama2-13b-megacode2_min100