This repo contains GGUF format model files for falcon-11B.
About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
llama.cpp. This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option.
text-generation-webui, Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration.
Ollama Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications
KoboldCpp, A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling.
GPT4All, This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration.
LM Studio An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration.
LoLLMS Web UI. A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection.
Faraday.dev, An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration.
llama-cpp-python, A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server.
candle, A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use.
ctransformers, A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server.
localGPT An open-source initiative enabling private conversations with documents.
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.
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 folder.
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: LiteLLMs/falcon-11B-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.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 8192 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. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the -p <PROMPT> argument with -i -ins
You can use GGUF models from Python using the llama-cpp-python or ctransformers libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
How to load this model in Python code, using llama-cpp-python
Run one of the following commands, according to your system:
shell
1# Base ctransformers with no GPU acceleration2pip install llama-cpp-python
3# With NVidia CUDA acceleration4CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
5# Or with OpenBLAS acceleration6CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
7# Or with CLBLast acceleration8CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
9# Or with AMD ROCm GPU acceleration (Linux only)10CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
11# Or with Metal GPU acceleration for macOS systems only12CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
13# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:14$env:CMAKE_ARGS ="-DLLAMA_OPENBLAS=on"15pip install llama-cpp-python
Simple llama-cpp-python example code
python
1from llama_cpp import Llama
2# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.3llm = Llama(4 model_path="./Q4_0/Q4_0-00001-of-00009.gguf",# Download the model file first5 n_ctx=32768,# The max sequence length to use - note that longer sequence lengths require much more resources6 n_threads=8,# The number of CPU threads to use, tailor to your system and the resulting performance7 n_gpu_layers=35# The number of layers to offload to GPU, if you have GPU acceleration available8)9# Simple inference example10output = llm(11"<PROMPT>",# Prompt12 max_tokens=512,# Generate up to 512 tokens13 stop=["</s>"],# Example stop token - not necessarily correct for this specific model! Please check before using.14 echo=True# Whether to echo the prompt15)16# Chat Completion API17llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2")# Set chat_format according to the model you are using18llm.create_chat_completion(19 messages =[20{"role":"system","content":"You are a story writing assistant."},21{22"role":"user",23"content":"Write a story about llamas."24}25]26)
How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
Falcon2-11B is an 11B parameters causal decoder-only model built by TII and trained on over 5,000B tokens of RefinedWeb enhanced with curated corpora. The model is made available under the TII Falcon License 2.0, the permissive Apache 2.0-based software license which includes an acceptable use policy that promotes the responsible use of AI.
Paper coming soon 😊.
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading this great blogpost from HF!
⚠️ This is a raw, pretrained model, which should be further finetuned for most usecases.
python
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import transformers
3import torch
45model ="tiiuae/falcon-11B"67tokenizer = AutoTokenizer.from_pretrained(model)8pipeline = transformers.pipeline(9"text-generation",10 model=model,11 tokenizer=tokenizer,12 torch_dtype=torch.bfloat16,13)14sequences = pipeline(15"Can you explain the concepts of Quantum Computing?",16 max_length=200,17 do_sample=True,18 top_k=10,19 num_return_sequences=1,20 eos_token_id=tokenizer.eos_token_id,21)22for seq in sequences:23print(f"Result: {seq['generated_text']}")24
💥 Falcon LLMs require PyTorch 2.0 for use with transformers!
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
Bias, Risks, and Limitations
Falcon2-11B is trained mostly on English, but also German, Spanish, French, Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish. It will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
Recommendations
We recommend users of Falcon2-11B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
How to Get Started with the Model
python
1from transformers import AutoTokenizer, AutoModelForCausalLM
2import transformers
3import torch
45model ="tiiuae/falcon-11B"67tokenizer = AutoTokenizer.from_pretrained(model)8pipeline = transformers.pipeline(9"text-generation",10 model=model,11 tokenizer=tokenizer,12 torch_dtype=torch.bfloat16,13 device_map="auto",14)15sequences = pipeline(16"Can you explain the concepts of Quantum Computing?",17 max_length=200,18 do_sample=True,19 top_k=10,20 num_return_sequences=1,21 eos_token_id=tokenizer.eos_token_id,22)23for seq in sequences:24print(f"Result: {seq['generated_text']}")25
Training Details
Training Data
Falcon2-11B was trained over 5,000B tokens of RefinedWeb, a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. It followed a four stage training strategy. The first three stages were focused on increasing the context length, from to 2048 to 4096 and finally to 8192 tokens. The last stage aimed to further enhance performance using only high quality data.
Overall, the data sources included RefinedWeb-English, Refined Web-Europe (cs, de, es, fr, it, nl, pl, pt, ro, sv), high quality technical data, code data, and conversational data extracted from public sources.
The data was tokenized with the Falcon-7B/11B tokenizer.
Training Procedure
Falcon2-11B was trained on 1024 A100 40GB GPUs for the majority of the training, using a 3D parallelism strategy (TP=8, PP=1, DP=128) combined with ZeRO and Flash-Attention 2.
Falcon2-11B was trained on AWS SageMaker, using on average 1024 A100 40GB GPUs in 128 p4d instances.
Software
Falcon2-11B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO, high-performance Triton kernels and FlashAttention-2. More details about the distributed training strategy can be found in Almazrouei et.al.
Citation
Paper coming soon 😊.
License
Falcon2-11B is licenced under TII Falcon License 2.0, the permissive Apache 2.0-based software license which includes an acceptable use policy that promotes the responsible use of AI.
