TheBloke
/

CodeUp-Llama-2-13B-Chat-HF-GGML

+---
+inference: false
+language:
+- en
+license: other
+model_creator: DeepSE
+model_link: https://huggingface.co/deepse/CodeUp-Llama-2-13b-chat-hf
+model_name: CodeUp Llama 2 13B Chat HF
+model_type: llama
+quantized_by: TheBloke
+tags:
+- text-to-code
+- multilingual-code-generation
+---
+<!-- header start -->
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+<!-- header end -->
+# CodeUp Llama 2 13B Chat HF - GGML
+- Model creator: [DeepSE](https://huggingface.co/deepse)
+- Original model: [CodeUp Llama 2 13B Chat HF](https://huggingface.co/deepse/CodeUp-Llama-2-13b-chat-hf)
+## Description
+This repo contains GGML format model files for [DeepSE's CodeUp Llama 2 13B Chat HF](https://huggingface.co/deepse/CodeUp-Llama-2-13b-chat-hf).
+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/CodeUp-Llama-2-13B-Chat-HF-GPTQ)
+* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML)
+* [DeepSE's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepse/CodeUp-Llama-2-13b-chat-hf)
+## Prompt template: Alpaca
+```
+Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+{prompt}
+### Response:
+```
+<!-- compatibility_ggml start -->
+## Compatibility
+### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
+These are guaranteed to be compatible with any UIs, tools and libraries released since late May. They may be phased out soon, as they are largely superseded by the new k-quant methods.
+### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
+These new quantisation methods are compatible with llama.cpp as of June 6th, commit `2d43387`.
+They are now also compatible with recent releases of text-generation-webui, KoboldCpp, llama-cpp-python, ctransformers, rustformers and most others. For compatibility with other tools and libraries, please check their documentation.
+## 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 |
+| ---- | ---- | ---- | ---- | ---- | ----- |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q2_K.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q2_K.bin) | q2_K | 2 | 5.51 GB| 8.01 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. |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q3_K_L.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q3_K_L.bin) | q3_K_L | 3 | 6.93 GB| 9.43 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 |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q3_K_M.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q3_K_M.bin) | q3_K_M | 3 | 6.31 GB| 8.81 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 |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q3_K_S.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q3_K_S.bin) | q3_K_S | 3 | 5.66 GB| 8.16 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q4_0.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q4_0.bin) | q4_0 | 4 | 7.37 GB| 9.87 GB | Original quant method, 4-bit. |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q4_1.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q4_1.bin) | q4_1 | 4 | 8.17 GB| 10.67 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. |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q4_K_M.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q4_K_M.bin) | q4_K_M | 4 | 7.87 GB| 10.37 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 |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q4_K_S.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q4_K_S.bin) | q4_K_S | 4 | 7.37 GB| 9.87 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q5_0.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q5_0.bin) | q5_0 | 5 | 8.97 GB| 11.47 GB | Original quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q5_1.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q5_1.bin) | q5_1 | 5 | 9.78 GB| 12.28 GB | Original quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q5_K_M.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q5_K_M.bin) | q5_K_M | 5 | 9.23 GB| 11.73 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 |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q5_K_S.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q5_K_S.bin) | q5_K_S | 5 | 8.97 GB| 11.47 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q6_K.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q6_K.bin) | q6_K | 6 | 10.68 GB| 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K for all tensors - 6-bit quantization |
+| [codeup-llama-2-13b-chat-hf.ggmlv3.q8_0.bin](https://huggingface.co/TheBloke/CodeUp-Llama-2-13B-Chat-HF-GGML/blob/main/codeup-llama-2-13b-chat-hf.ggmlv3.q8_0.bin) | q8_0 | 8 | 13.79 GB| 16.29 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`
+I use the following command line; adjust for your tastes and needs:
+```
+./main -t 10 -ngl 32 -m codeup-llama-2-13b-chat-hf.ggmlv3.q4_K_M.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
+```
+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.
+If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
+## How to run in `text-generation-webui`
+Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
+<!-- footer start -->
+## Discord
+For further support, and discussions on these models and AI in general, join us at:
+[TheBloke AI's Discord server](https://discord.gg/theblokeai)
+## 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.
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+Thank you to all my generous patrons and donaters!
+<!-- footer end -->
+# Original model card: DeepSE's CodeUp Llama 2 13B Chat HF
+<!-- <p align="center" width="70%">
+<img src="assets/Logo.jpg" alt="HKUST CodeUp" style="width: 50%; min-width: 250px; display: block; margin: auto;">
+</p> -->
+![HKUST CodeUp](assets/Logo.jpg)
+# CodeUp: A Multilingual Code Generation Llama2 Model with Parameter-Efficient Instruction-Tuning on a Single RTX 3090
+## Description
+In recent years, large language models (LLMs) have shown exceptional capabilities in a wide range of applications due to their fantastic emergence ability. To align with human preference, instruction-tuning and reinforcement learning from human feedback (RLHF) are proposed for Chat-based LLMs (e.g., ChatGPT, GPT-4). However, these LLMs (except for Codex) primarily focus on the general domain and are not specifically designed for the code domain. Although Codex provides an alternative choice, it is a closed-source model developed by OpenAI. Hence, it is imperative to develop open-source instruction-following LLMs for the code domain.
+However, the large-scale number of LLMs' parameters ($\ge$7B) and training datasets require a vast amount of computational resources, which significantly impedes the development of training and inference on consumer hardware.
+To handle these challenges, in this project, we adopt the latest powerful foundation model `Llama 2` and construct high-quality instruction-following data for code generation tasks, and propose an instruction-following multilingual code generation Llama2 model. Meanwhile, to make it fit an academic budget and consumer hardware (e.g., a single RTX 3090) based on `Alpaca-LoRA`, we equip `CodeUp` with the advanced parameter-efficient fine-tuning (PEFT) methods (e.g., [LoRA](https://arxiv.org/abs/2106.09685)) which enable efficient adaptation of pre-trained language models (PLMs, also known as foundation model) to various downstream applications without fine-tuning the entire model's parameters. The overall training recipe is as follows.
+![Training Framework](assets/Framework.jpg)
+## NL2Code Data Release
+Recently, it has attracted significant attention to exploiting much larger and more powerful LLMs (e.g., ChatGPT, GPT-4) to self-generate instruction-following data by delicate prompt design. However, many approaches primarily focus on the general domain and lack code-specific domain considerations. To this end, [Code Alpaca](https://github.com/sahil280114/codealpaca) follows the previous Self-Instruct paper [3] and [Stanford Alpaca repo](https://github.com/tatsu-lab/stanford_alpaca) with some code-related modifications to conduct 20K instruction-following data `data/code_alpaca_20k.json` for code generation tasks. This `JSON` file following `alpaca_data.json` format is a list of dictionaries; each dictionary contains the following fields:
+- `instruction`: `str`, describes the task the model should perform. Each of the 20K instructions is unique.
+- `input`: `str`, optional context or input for the task. For example, when the instruction is "Amend the following SQL query to select distinct elements", the input is the SQL query. Around 40% of the examples have an input.
+- `output`: `str`, the answer to the instruction as generated by `text-davinci-003`.
+### High-quality Data Filter
+However, after carefully checking the LLMs-self-generated data, we observe three critical problems that may hinder LLMs' instruction learning due to ambiguous and irrelevant noise. That is
+1. When `instruction` doesn't specify the programming language (PL) of implementation, the `output` appears with diverse options, e.g., Python, C++, and JavaScript.
+2. It is ambiguous to identify which programming language `output` is implemented by.
+3. Both `instruction` and `output` are irrelevant to the code-specific domain.
+Hence, we filter the ambiguous and irrelevant data by rigorous design to obtain high-quality instruction data. Specifically, to solve 1) we set Python as the default PL of implementation and use [Guesslang](https://guesslang.readthedocs.io/en/latest/) package to detect the PL of a given source code in `output`. If the Python is detected, this prompt is retained. Otherwise, it will be filtered. 2) and 3) In these cases, we delete these prompts. After that, about 5K low-quality instruction data is filtered. To supplement the high-quality instruction data, we further integrate the `data/new_codealpaca.json` data (about 4.5K) under the above filter rules.
+This way, we gain the 19K high-quality instruction data of code generation. The following is the instruction number distribution of each PL with Radar visualization before and after filtering.
+<!-- | Raw Data (20K + 4K)| Filtered Data (19K)  |
+| -- | -- |
+| <center><img src="assets/PL_Raw.png" width="100%"></center>  | <center><img src="assets/PL_Clean.png" width="92%"></center> |  -->
+![PL Data Filtering)](assets/PL_Filter.jpg)
+## Training & Inference
+Detailed instructions can be found at [https://github.com/juyongjiang/CodeUp](https://github.com/juyongjiang/CodeUp).