license: apache-2.0
language:
- en
- de
- es
- fr
tags:
- sft
inference: false
datasets:
- OpenAssistant/oasst1
I am still building the structure of these descriptions.
These will contain increasingly more content to help find the best models for a purpose.
falcon-40b-sft-top1-560 - GGUF
- Model creator: OpenAssistant
- Original model: falcon-40b-sft-top1-560
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
Two special tokens are used to mark the beginning of user and assistant turns:
<|prompter|> and <|assistant|>. Each turn ends with a <|endoftext|> token.
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
```<center>
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[](https://github.com/maddes8cht)
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</center>