metadata

license: apache-2.0
language:
  - en
  - de
  - es
  - fr
tags:
  - sft
inference: false
datasets:
  - OpenAssistant/oasst1

I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information

falcon-40b-sft-top1-560 - GGUF

Model creator: OpenAssistant
Original model: falcon-40b-sft-top1-560

Important Update for Falcon Models in llama.cpp Versions After October 18, 2023

As noted on the Llama.cpp GitHub repository, all new Llama.cpp releases after October 18, 2023, will require a re-quantization due to the new BPE tokenizer.

Good news! I am glad that my re-quantization process for Falcon Models is nearly complete. Download the latest quantized models to ensure compatibility with recent llama.cpp software.

Key Points:

Stay Informed: Keep an eye on software application release schedules using llama.cpp libraries.
Monitor Upload Times: Re-quantization is almost done. Watch for updates on my Hugging Face Model pages.

Important Compatibility Note: Old software will work with old Falcon models, but expect updated software to exclusively support the new models.

This change primarily affects Falcon and Starcoder models, with other models remaining unaffected.

Brief

I have a problem with the OpenAssistant falcon sft models

which currently prevents me from re-quantizing these models. It is not clear to me at the moment if this problem can be solved.

About GGUF format

gguf is the current file format used by the ggml library. A growing list of Software is using it and can therefore use this model. The core project making use of the ggml library is the llama.cpp project by Georgi Gerganov

Quantization variants

There is a bunch of quantized files available. How to choose the best for you:

Legacy quants

Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are legacy quantization types. Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants. Falcon 7B models cannot be quantized to K-quants.

K-quants

K-quants are based on the idea that the quantization of certain parts affects the quality in different ways. If you quantize certain parts more and others less, you get a more powerful model with the same file size, or a smaller file size and lower memory load with comparable performance. So, if possible, use K-quants. With a Q6_K you should find it really hard to find a quality difference to the original model - ask your model two times the same question and you may encounter bigger quality differences.

Original Model Card:

Open-Assistant Falcon 40B SFT OASST-TOP1 Model

This model is a fine-tuning of TII's Falcon 40B LLM. It was trained with top-1 (high-quality) demonstrations of the OASST data set (exported on May 6, 2023) with an effective batch size of 144 for ~7.5 epochs with LIMA style dropout (p=0.3) and a context-length of 2048 tokens.

Model Details

Finetuned from: tiiuae/falcon-40b
Model type: Causal decoder-only transformer language model
Language: English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
Demo: Continuations for 250 random prompts
Eval results: ilm-eval
Weights & Biases: Training log (Checkpoint: 560 steps)
License: Apache 2.0
Contact: Open-Assistant Discord

Prompting

Input prompt example:

<|prompter|>What is a meme, and what's the history behind this word?<|endoftext|><|assistant|>

The input ends with the <|assistant|> token to signal that the model should start generating the assistant reply.

Configuration Details

Model:

falcon-40b:
  dtype: bf16
  log_dir: "falcon_log_40b"
  learning_rate: 5e-6
  model_name: "tiiuae/falcon-40b"
  deepspeed_config: configs/zero3_config_falcon.json
  output_dir: falcon
  weight_decay: 0.0
  max_length: 2048
  warmup_steps: 20
  gradient_checkpointing: true
  gradient_accumulation_steps: 1
  per_device_train_batch_size: 18
  per_device_eval_batch_size: 10
  eval_steps: 80
  save_steps: 80
  num_train_epochs: 8
  save_total_limit: 4
  use_flash_attention: false
  residual_dropout: 0.3
  residual_dropout_lima: true
  sort_by_length: false
  save_strategy: steps

Dataset:

oasst-top1:
  datasets:
    - oasst_export:
        lang: "bg,ca,cs,da,de,en,es,fr,hr,hu,it,nl,pl,pt,ro,ru,sl,sr,sv,uk" # sft-8.0
        input_file_path: 2023-05-06_OASST_labels.jsonl.gz
        val_split: 0.05
        top_k: 1

End of original Model File

Please consider to support my work

Coming Soon: I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.