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/Inkbot-13B-8k-0.2-GGUF and below it, a specific filename to download, such as: inkbot-13b-8k-0.2.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/Inkbot-13B-8k-0.2-GGUF", model_file="inkbot-13b-8k-0.2.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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Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
Original model card: Adam Brusselback's Inkbot 13B 8K 0.2
Model Card for Inkbot
Model Details
Inkbot is a conversational AI model designed to interpret and respond to structured prompts with or without contextual information. Its unique feature lies in the structured prompt system, allowing users to engage in dynamic dialogues that can evolve based on context, metadata, and user input.
Performance
The model excels in RAG type queries, answering from context, and overriding memory when necessary.
It can handle very large contexts, but may sometimes enter a repeating text loop, especially during complex tasks.
The model is intended to be more functional and less chatty, avoiding the waste of tokens on superfluous language.
<#meta#>
- Date: 2023-07-20
- Task: general
<#system#>
A dialogue focused on determining dates based on context clues.
<#chat#>
<#user#>
What is today's date?
<#bot#>
The date is: 2023-07-20
<#user#>
When did the event in the provided context take place?
<#user_context#>
It's the day after new years eve, and I'm starting 2018 with a headache.
<#bot#>
The event in the context took place on: 2018-01-01
Without Context:
<#meta#>
- Date: 2023-07-20
- Task: general
<#system#>
A simple dialogue about daily queries.
<#chat#>
<#user#>
Will it rain today?
<#bot#>
I am not sure, without knowing your location I am unable to determine the weather.
<#user#>
Can you compose a haiku for me?
<#bot#>
Whispers of the breeze,
Nature's melodies unfold,
Dawn dispels the night.
Task Options
The model has been trained on a variety of tasks. Below is a breakdown of these tasks, along with example prompts to help guide your usage.
2. Content Generation
Tasks in this category involve creating or expanding content.
kg_writer (800 examples)
Example Prompts:
"Using the provided knowledge graph, write an article about the topics and entities in the graph, incorporating the linked ideas. Use idea tags while writing to help focus."
"Construct a story based on the information in the knowledge graph."
summary (1,600 examples)
Example Prompts:
"Generate an extensive summary of the given document."
"Please read the provided document to understand the context and content. Use this understanding to generate a summary. Separate the article into chunks, and sequentially create a summary for each chunk. Give me a final summary in the end."
paraphrase (1,100 examples)
Example Prompts:
"Rephrase the following sentence while retaining its original meaning."
"Can you provide an alternative wording for the paragraph below?"
3. Content Analysis
Tasks in this category evaluate, grade, or filter content.
grading (400 examples)
Example Prompts:
"Based on the provided document, please rate the usefulness as training data on a scale from 0-5."
sponsorblock (5,200 examples)
Example Prompts:
"Read the document and extract any sentences or phrases that contain explicit mentions of sponsorship, promotional partnerships, or any form of paid content."
4. Information Structuring
Tasks in this category involve the structured representation or extraction of information.
kg (3,600 examples)
Example Prompts:
"Create a Knowledge Graph from the document provided."
"Extract key concepts and relationships from the conversation to form a knowledge graph."
5. General Interaction
Tasks in this category are designed for general questions and interactions.
general (1,600 examples)
Example Prompts:
"What is the capital of France?"
"Explain particle physics to a 5 years old."
Limitations
Adhere to the prompt structure for best results.
When providing contextual details, clarity is essential for Inkbot to derive accurate and meaningful responses.
The overriding memory from user_context property generally only works for the next prompt or two, after which the model reverts to its original behavior.
On complex tasks, like creating a coherent story based on a set of facts from context, there's a potential for a repeating text loop as context fills.
Sometimes the model doesn't know when to end a knowledge graph, which can result in adding nodes and edges until it runs out of context.
Additional Notes
Use rope-freq-scale=0.5 or compress_pos_emb=2 for 8k ctx
The 'date', 'task', and 'system' are crucial metadata components that need to be provided outside the core dialogue.
Use the 'user_context' when you want to offer supplementary context that guides Inkbot's response. You can interleave it in the chat log as necessary. It comes after the users instruction.
The specific tag format, such as <#word#>, is used to because there are filters in a lot of APIs for <|word|> and this makes interactions easier.
