The creator of the source model has listed its license as ['llama2'], and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: Stability AI's Japanese StableLM Instruct Beta 7B.
Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit d0cee0d
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.
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 GGUF 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: TheBloke/japanese-stablelm-instruct-beta-7B-GGUF and below it, a specific filename to download, such as: japanese-stablelm-instruct-beta-7b.Q4_K_M.gguf.
Then click Download.
On the command line, including multiple files at once
I recommend using the huggingface-hub Python library:
pip3 install huggingface-hub
Then you can download any individual model file to the current directory, at high speed, with a command like this:
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 GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
How to load this model in Python code, using ctransformers
First install the package
Run one of the following commands, according to your system:
shell
1# Base ctransformers with no GPU acceleration2pip install ctransformers
3# Or with CUDA GPU acceleration4pip install ctransformers[cuda]5# Or with AMD ROCm GPU acceleration (Linux only)6CT_HIPBLAS=1 pip install ctransformers --no-binary ctransformers
7# Or with Metal GPU acceleration for macOS systems only8CT_METAL=1 pip install ctransformers --no-binary ctransformers
Simple ctransformers example code
python
1from ctransformers import AutoModelForCausalLM
23# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.4llm = AutoModelForCausalLM.from_pretrained("TheBloke/japanese-stablelm-instruct-beta-7B-GGUF", model_file="japanese-stablelm-instruct-beta-7b.Q4_K_M.gguf", model_type="llama", gpu_layers=50)56print(llm("AI is going to"))
How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
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Original model card: Stability AI's Japanese StableLM Instruct Beta 7B
Japanese-StableLM-Instruct-Beta-7B
A cute robot wearing a kimono writes calligraphy with one single brush
A cute robot wearing a kimono writes calligraphy with one single brush — Stable Diffusion XL
Model Description
japanese-stablelm-instruct-beta-7b is a 7B-parameter decoder-only language model based on japanese-stablelm-base-beta-7b and further fine tuned on Databricks Dolly-15k, Anthropic HH, and other public data.
First install additional dependencies in requirements.txt:
pip install -r requirements.txt
Then start generating text with japanese-stablelm-instruct-beta-7b by using the following code snippet:
python
1import torch
2from transformers import AutoTokenizer, AutoModelForCausalLM
34model_name ="stabilityai/japanese-stablelm-instruct-beta-7b"5tokenizer = AutoTokenizer.from_pretrained(model_name)67# The next line may need to be modified depending on the environment8model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")910defbuild_prompt(user_query, inputs):11 sys_msg ="<s>[INST] <<SYS>>\nあなたは役立つアシスタントです。\n<<SYS>>\n\n"12 p = sys_msg + user_query +"\n\n"+ inputs +" [/INST] "13return p
1415user_inputs ={16"user_query":"与えられたことわざの意味を小学生でも分かるように教えてください。",17"inputs":"情けは人のためならず"18}19prompt = build_prompt(**user_inputs)2021input_ids = tokenizer.encode(22 prompt,23 add_special_tokens=False,24 return_tensors="pt"25)2627# this is for reproducibility.28# feel free to change to get different result29seed =2330torch.manual_seed(seed)3132tokens = model.generate(33 input_ids.to(device=model.device),34 max_new_tokens=128,35 temperature=0.99,36 top_p=0.95,37 do_sample=True,38)3940out = tokenizer.decode(tokens[0], skip_special_tokens=True)41print(out)
We suggest playing with different generation config (top_p, repetition_penalty etc) to find the best setup for your tasks. For example, use higher temperature for roleplay task, lower temperature for reasoning.
Model Details
Model type: japanese-stablelm-instruct-beta-7b model is an auto-regressive language model based on the Llama2 transformer architecture.
Contact: For questions and comments about the model, please join Stable Community Japan. For future announcements / information about Stability AI models, research, and events, please follow https://twitter.com/StabilityAI_JP.
Training Dataset
The following datasets were used for the instruction training. Note these are Japanese translated versions of the original datasets, shared by kunishou.
The model is intended to be used by all individuals as a foundation for application-specific fine-tuning without strict limitations on commercial use.
Limitations and bias
The pre-training dataset may have contained offensive or inappropriate content even after applying data cleansing filters which can be reflected in the model generated text. We recommend users exercise reasonable caution when using these models in production systems. Do not use the model for any applications that may cause harm or distress to individuals or groups.
Authors
This model was developed by the Research & Development team at Stability AI Japan, and the development was co-led by Takuya Akiba and Meng Lee. The members of the team are as follows:
Meng Lee
Fujiki Nakamura
Makoto Shing
Paul McCann
Takuya Akiba
Naoki Orii
Acknowledgements
We thank Meta Research for releasing Llama 2 under an open license for others to build on.
We are grateful for the contributions of the EleutherAI Polyglot-JA team in helping us to collect a large amount of pre-training data in Japanese. Polyglot-JA members includes Hyunwoong Ko (Project Lead), Fujiki Nakamura (originally started this project when he commited to the Polyglot team), Yunho Mo, Minji Jung, KeunSeok Im, and Su-Kyeong Jang.
We are also appreciative of AI Novelist/Sta (Bit192, Inc.) and the numerous contributors from Stable Community Japan for assisting us in gathering a large amount of high-quality Japanese textual data for model training.
