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

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CodeUp Llama 2 13B Chat HF - GGML

• Model creator: DeepSE
• Original model: CodeUp Llama 2 13B Chat HF

Description

This repo contains GGML format model files for DeepSE's CodeUp Llama 2 13B Chat HF.

Important note regarding GGML files.

The GGML format has now been superseded by GGUF. As of August 21st 2023, 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 and libraries and UIs which support this format, such as:

• text-generation-webui, the most popular web UI. Supports NVidia CUDA GPU acceleration.
• KoboldCpp, a powerful GGML web UI with GPU acceleration on all platforms (CUDA and OpenCL). Especially good for story telling.
• LM Studio, a fully featured local GUI with GPU acceleration on both Windows (NVidia and AMD), and macOS.
• LoLLMS Web UI, a great web UI with CUDA GPU acceleration via the c_transformers backend.
• ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
• 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.
• 2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference
• 2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference (deprecated)
• DeepSE's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions

Prompt template: Alpaca

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

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

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

Click to see details

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.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
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_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.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_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.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_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.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_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.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_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.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_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.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	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

Make sure you are using llama.cpp from commit dadbed99e65252d79f81101a392d0d6497b86caa or earlier.

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

./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 "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\nWrite a story about llamas\n\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.

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

How to run in text-generation-webui

Further instructions here: text-generation-webui/docs/llama.cpp.md.

Discord

For further support, and discussions on these models and AI in general, join us at:

TheBloke AI's Discord server

Thanks, and how to contribute.

Thanks to the 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
• Ko-Fi: https://ko-fi.com/TheBlokeAI

Special thanks to: Aemon Algiz.

Patreon special mentions: Russ Johnson, J, alfie_i, Alex, NimbleBox.ai, Chadd, Mandus, Nikolai Manek, Ken Nordquist, ya boyyy, Illia Dulskyi, Viktor Bowallius, vamX, Iucharbius, zynix, Magnesian, Clay Pascal, Pierre Kircher, Enrico Ros, Tony Hughes, Elle, Andrey, knownsqashed, Deep Realms, Jerry Meng, Lone Striker, Derek Yates, Pyrater, Mesiah Bishop, James Bentley, Femi Adebogun, Brandon Frisco, SuperWojo, Alps Aficionado, Michael Dempsey, Vitor Caleffi, Will Dee, Edmond Seymore, usrbinkat, LangChain4j, Kacper Wikieł, Luke Pendergrass, John Detwiler, theTransient, Nathan LeClaire, Tiffany J. Kim, biorpg, Eugene Pentland, Stanislav Ovsiannikov, Fred von Graf, terasurfer, Kalila, Dan Guido, Nitin Borwankar, 阿明, Ai Maven, John Villwock, Gabriel Puliatti, Stephen Murray, Asp the Wyvern, danny, Chris Smitley, ReadyPlayerEmma, S_X, Daniel P. Andersen, Olakabola, Jeffrey Morgan, Imad Khwaja, Caitlyn Gatomon, webtim, Alicia Loh, Trenton Dambrowitz, Swaroop Kallakuri, Erik Bjäreholt, Leonard Tan, Spiking Neurons AB, Luke @flexchar, Ajan Kanaga, Thomas Belote, Deo Leter, RoA, Willem Michiel, transmissions 11, subjectnull, Matthew Berman, Joseph William Delisle, David Ziegler, Michael Davis, Johann-Peter Hartmann, Talal Aujan, senxiiz, Artur Olbinski, Rainer Wilmers, Spencer Kim, Fen Risland, Cap'n Zoog, Rishabh Srivastava, Michael Levine, Geoffrey Montalvo, Sean Connelly, Alexandros Triantafyllidis, Pieter, Gabriel Tamborski, Sam, Subspace Studios, Junyu Yang, Pedro Madruga, Vadim, Cory Kujawski, K, Raven Klaugh, Randy H, Mano Prime, Sebastain Graf, Space Cruiser

Thank you to all my generous patrons and donaters!

And thank you again to a16z for their generous grant.

Original model card: DeepSE's CodeUp Llama 2 13B Chat HF

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) 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.

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 follows the previous Self-Instruct paper [3] and Stanford Alpaca repo 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 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.

Training & Inference

Detailed instructions can be found at https://github.com/juyongjiang/CodeUp.