license: llama2
library_name: transformers
tags:
- code
- llamafile
metrics:
- code_eval
base_model: WizardLM/WizardCoder-Python-34B-V1.0
inference: false
model_creator: WizardLM
model_type: llama
prompt_template: >
Below is an instruction that describes a task. Write a response that
appropriately completes the request.
### Instruction:
{prompt}
### Response:
quantized_by: TheBloke
model-index:
- name: WizardCoder-Python-34B-V1.0
results:
- task:
type: text-generation
dataset:
name: HumanEval
type: openai_humaneval
metrics:
- type: pass@1
value: 0.732
name: pass@1
verified: false
jartine's LLM work is generously supported by a grant from mozilla
WizardCoder Python 34B V1.0 - llamafile
- Model creator: WizardLM
- Original model: WizardCoder Python 34B V1.0
Description
This repo contains llamafile format model files for WizardLM's WizardCoder Python 34B V1.0.
WARNING: This README may contain inaccuracies. It was generated automatically by forking TheBloke/WizardCoder-Python-34B-V1.0-GGUF and piping the README through sed. Errors should be reported to jartine, and do not reflect TheBloke. You can also support his work on Patreon.
About llamafile
llamafile is a new format introduced by Mozilla Ocho on Nov 20th 2023. It uses Cosmopolitan Libc to turn LLM weights into runnable llama.cpp binaries that run on the stock installs of six OSes for both ARM64 and AMD64. llamafile offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support llamafile:
- llama.cpp. The source project for llamafile. Offers a CLI and a server option.
- text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
- KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
- LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
- LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
- Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
- 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.
- candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
Repositories available
- AWQ model(s) for GPU inference.
- GPTQ models for GPU inference, with multiple quantisation parameter options.
- 2, 3, 4, 5, 6 and 8-bit llamafile models for CPU+GPU inference
- WizardLM'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 llamafilev2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d36d5be95a0d9088b674dbb27354107221
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
Explanation of quantisation 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
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 |
|---|---|---|---|---|---|
| wizardcoder-python-34b-v1.0.Q2_K.llamafile | Q2_K | 2 | 14.21 GB | 16.71 GB | smallest, significant quality loss - not recommended for most purposes |
| wizardcoder-python-34b-v1.0.Q3_K_S.llamafile | Q3_K_S | 3 | 14.61 GB | 17.11 GB | very small, high quality loss |
| wizardcoder-python-34b-v1.0.Q3_K_M.llamafile | Q3_K_M | 3 | 16.28 GB | 18.78 GB | very small, high quality loss |
| wizardcoder-python-34b-v1.0.Q3_K_L.llamafile | Q3_K_L | 3 | 17.77 GB | 20.27 GB | small, substantial quality loss |
| wizardcoder-python-34b-v1.0.Q4_0.llamafile | Q4_0 | 4 | 19.05 GB | 21.55 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| wizardcoder-python-34b-v1.0.Q4_K_S.llamafile | Q4_K_S | 4 | 19.15 GB | 21.65 GB | small, greater quality loss |
| wizardcoder-python-34b-v1.0.Q4_K_M.llamafile | Q4_K_M | 4 | 20.22 GB | 22.72 GB | medium, balanced quality - recommended |
| wizardcoder-python-34b-v1.0.Q5_0.llamafile | Q5_0 | 5 | 23.24 GB | 25.74 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| wizardcoder-python-34b-v1.0.Q5_K_S.llamafile | Q5_K_S | 5 | 23.24 GB | 25.74 GB | large, low quality loss - recommended |
| wizardcoder-python-34b-v1.0.Q5_K_M.llamafile | Q5_K_M | 5 | 23.84 GB | 26.34 GB | large, very low quality loss - recommended |
| wizardcoder-python-34b-v1.0.Q6_K.llamafile | Q6_K | 6 | 27.68 GB | 30.18 GB | very large, extremely low quality loss |
| wizardcoder-python-34b-v1.0.Q8_0.llamafile | Q8_0 | 8 | 35.86 GB | 38.36 GB | very large, extremely low quality loss - not recommended |
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 download llamafile files
Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
In text-generation-webui
Under Download Model, you can enter the model repo: jartine/WizardCoder-Python-34B-V1.0-llamafile and below it, a specific filename to download, such as: wizardcoder-python-34b-v1.0.q4_K_M.llamafile.
Then click Download.
On the command line, including multiple files at once
I recommend using the huggingface-hub Python library:
pip3 install huggingface-hub>=0.17.1
Then you can download any individual model file to the current directory, at high speed, with a command like this:
huggingface-cli download jartine/WizardCoder-Python-34B-V1.0-llamafile wizardcoder-python-34b-v1.0.q4_K_M.llamafile --local-dir . --local-dir-use-symlinks False
More advanced huggingface-cli download usage
You can also download multiple files at once with a pattern:
huggingface-cli download jartine/WizardCoder-Python-34B-V1.0-llamafile --local-dir . --local-dir-use-symlinks False --include='*Q4_K*llamafile'
For more documentation on downloading with huggingface-cli, please see: HF -> Hub Python Library -> Download files -> Download from the CLI.
To accelerate downloads on fast connections (1Gbit/s or higher), install hf_transfer:
pip3 install hf_transfer
And set environment variable HF_HUB_ENABLE_HF_TRANSFER to 1:
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download jartine/WizardCoder-Python-34B-V1.0-llamafile wizardcoder-python-34b-v1.0.q4_K_M.llamafile --local-dir . --local-dir-use-symlinks False
Windows CLI users: Use set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 before running the download command.
Example llama.cpp command
Make sure you are using llama.cpp from commit d0cee0d36d5be95a0d9088b674dbb27354107221 or later.
./main -ngl 32 -m wizardcoder-python-34b-v1.0.q4_K_M.llamafile --color -c 4096 --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:\n{prompt}\n\n### Response:"
Change -ngl 32 to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change -c 4096 to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the llamafile file and set by llama.cpp automatically.
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.
How to run from Python code
You can use llamafile models from Python using the llama-cpp-python or ctransformers libraries.
How to load this model from Python using ctransformers
First install the package
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
Simple example code to load one of these llamafile models
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("jartine/WizardCoder-Python-34B-V1.0-llamafile", model_file="wizardcoder-python-34b-v1.0.q4_K_M.llamafile", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
Discord
For further support, and discussions on these models and AI in general, join us at:
Thanks, and how to contribute
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.
And thank you again to mozilla for their generous grant.
Original model card: WizardLM's WizardCoder Python 34B V1.0
🤗 HF Repo •🐱 Github Repo • 🐦 Twitter • 📃 [WizardLM] • 📃 [WizardCoder] • 📃 [WizardMath]
👋 Join our Discord
News
- 🔥🔥🔥[2023/08/26] We released WizardCoder-Python-34B-V1.0 , which achieves the 73.2 pass@1 and surpasses GPT4 (2023/03/15), ChatGPT-3.5, and Claude2 on the HumanEval Benchmarks.
- [2023/06/16] We released WizardCoder-15B-V1.0 , which achieves the 57.3 pass@1 and surpasses Claude-Plus (+6.8), Bard (+15.3) and InstructCodeT5+ (+22.3) on the HumanEval Benchmarks.
❗Note: There are two HumanEval results of GPT4 and ChatGPT-3.5. The 67.0 and 48.1 are reported by the official GPT4 Report (2023/03/15) of OpenAI. The 82.0 and 72.5 are tested by ourselves with the latest API (2023/08/26).
| Model | Checkpoint | Paper | HumanEval | MBPP | Demo | License |
|---|---|---|---|---|---|---|
| WizardCoder-Python-34B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 73.2 | 61.2 | Demo | Llama2 |
| WizardCoder-15B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 59.8 | 50.6 | -- | OpenRAIL-M |
| WizardCoder-Python-13B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 64.0 | 55.6 | -- | Llama2 |
| WizardCoder-3B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 34.8 | 37.4 | Demo | OpenRAIL-M |
| WizardCoder-1B-V1.0 | 🤗 HF Link | 📃 [WizardCoder] | 23.8 | 28.6 | -- | OpenRAIL-M |
- Our WizardMath-70B-V1.0 model slightly outperforms some closed-source LLMs on the GSM8K, including ChatGPT 3.5, Claude Instant 1 and PaLM 2 540B.
- Our WizardMath-70B-V1.0 model achieves 81.6 pass@1 on the GSM8k Benchmarks, which is 24.8 points higher than the SOTA open-source LLM, and achieves 22.7 pass@1 on the MATH Benchmarks, which is 9.2 points higher than the SOTA open-source LLM.
| Model | Checkpoint | Paper | GSM8k | MATH | Online Demo | License |
|---|---|---|---|---|---|---|
| WizardMath-70B-V1.0 | 🤗 HF Link | 📃 [WizardMath] | 81.6 | 22.7 | Demo | Llama 2 |
| WizardMath-13B-V1.0 | 🤗 HF Link | 📃 [WizardMath] | 63.9 | 14.0 | Demo | Llama 2 |
| WizardMath-7B-V1.0 | 🤗 HF Link | 📃 [WizardMath] | 54.9 | 10.7 | Demo | Llama 2 |
- [08/09/2023] We released WizardLM-70B-V1.0 model. Here is Full Model Weight.
| Model | Checkpoint | Paper | MT-Bench | AlpacaEval | GSM8k | HumanEval | License |
|---|---|---|---|---|---|---|---|
| WizardLM-70B-V1.0 | 🤗 HF Link | 📃Coming Soon | 7.78 | 92.91% | 77.6% | 50.6 | Llama 2 License |
| WizardLM-13B-V1.2 | 🤗 HF Link | 7.06 | 89.17% | 55.3% | 36.6 | Llama 2 License | |
| WizardLM-13B-V1.1 | 🤗 HF Link | 6.76 | 86.32% | 25.0 | Non-commercial | ||
| WizardLM-30B-V1.0 | 🤗 HF Link | 7.01 | 37.8 | Non-commercial | |||
| WizardLM-13B-V1.0 | 🤗 HF Link | 6.35 | 75.31% | 24.0 | Non-commercial | ||
| WizardLM-7B-V1.0 | 🤗 HF Link | 📃 [WizardLM] | 19.1 | Non-commercial | |||
Comparing WizardCoder-Python-34B-V1.0 with Other LLMs.
🔥 The following figure shows that our WizardCoder-Python-34B-V1.0 attains the second position in this benchmark, surpassing GPT4 (2023/03/15, 73.2 vs. 67.0), ChatGPT-3.5 (73.2 vs. 72.5) and Claude2 (73.2 vs. 71.2).
Prompt Format
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
Inference Demo Script
We provide the inference demo code here.
Citation
Please cite the repo if you use the data, method or code in this repo.
@article{luo2023wizardcoder,
title={WizardCoder: Empowering Code Large Language Models with Evol-Instruct},
author={Luo, Ziyang and Xu, Can and Zhao, Pu and Sun, Qingfeng and Geng, Xiubo and Hu, Wenxiang and Tao, Chongyang and Ma, Jing and Lin, Qingwei and Jiang, Daxin},
journal={arXiv preprint arXiv:2306.08568},
year={2023}
}