"Update README.md"

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	---
	license: apache-2.0
	language:
	- en
	- de
	- es
	- fr
	tags:
	- sft
	inference: false
	datasets:
	- OpenAssistant/oasst1
	- databricks/databricks-dolly-15k
	---
	[![banner](https://maddes8cht.github.io/assets/buttons/Huggingface-banner.jpg)]()

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

	# falcon-40b-sft-mix-1226 - GGUF
	- Model creator: [OpenAssistant](https://huggingface.co/OpenAssistant)
	- Original model: [falcon-40b-sft-mix-1226](https://huggingface.co/OpenAssistant/falcon-40b-sft-mix-1226)

	# K-Quants in Falcon 7b models

	New releases of Llama.cpp now support K-quantization for previously incompatible models, in particular all Falcon 7B models (While Falcon 40b is and always has been fully compatible with K-Quantisation). This is achieved by employing a fallback solution for model layers that cannot be quantized with real K-quants.

	For Falcon 7B models, although only a quarter of the layers can be quantized with true K-quants, this approach still benefits from utilizing different legacy quantization types Q4_0, Q4_1, Q5_0, and Q5_1. As a result, it offers better quality at the same file size or smaller file sizes with comparable performance.

	So this solution ensures improved performance and efficiency over legacy Q4_0, Q4_1, Q5_0 and Q5_1 Quantizations.




	---
	# Brief
	Finally got the OpenAssistant falcon sft models working again

	* [falcon-7b-sft-top1-696](https://huggingface.co/OpenAssistant/falcon-7b-sft-top1-696)
	* [falcon-40b-sft-top1-560](https://huggingface.co/OpenAssistant/falcon-40b-sft-top1-560)
	* [falcon-40b-sft-mix-1226](https://huggingface.co/OpenAssistant/falcon-40b-sft-mix-1226)



	---



	# About GGUF format

	`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/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](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov

	# Quantization variants

	There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option 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.
	## Note:
	Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not real K-quants. More details can be found in affected model descriptions.
	(This mainly refers to Falcon 7b and Starcoder models)

	# K-quants

	K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
	So, if possible, use K-quants.
	With a Q6_K, you'll likely find it challenging to discern a quality difference from 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 MIX Model

	This model is a fine-tuning of TII's [Falcon 40B](https://huggingface.co/tiiuae/falcon-40b) LLM.
	It was trained on a mixture of OASST top-2 threads (exported on June 2, 2023), Dolly-15k and synthetic instruction datasets (see dataset configuration below).

	## Model Details

	- Finetuned from: [tiiuae/falcon-40b]((https://huggingface.co/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](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Fchat-gpt%2F2023-04-11_gpt-3.5-turbo_lottery.json%0Ahttps%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-sft%2F2023-06-05_OpenAssistant_falcon-40b-sft-mix-1226_sampling_noprefix2.json), [multiligual-60](https://open-assistant.github.io/oasst-model-eval/?f=https%3A%2F%2Fraw.githubusercontent.com%2FOpen-Assistant%2Foasst-model-eval%2Fmain%2Fsampling_reports%2Foasst-sft%2F2023-06-05_OpenAssistant_falcon-40b-sft-mix-1226_multilingual_noprefix2.json)
	- Eval results: [ilm-eval](https://tju01.github.io/ilm-eval/)
	- Weights & Biases: [Training log](https://wandb.ai/open-assistant/public-sft/runs/feplc450) (checkpoint: 1226 steps)
	- License: Apache 2.0
	- Contact: [Open-Assistant Discord](https://ykilcher.com/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
	learning_rate: 1e-5
	model_name: "tiiuae/falcon-40b"
	deepspeed_config: configs/zero3_config_falcon.json
	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: 120
	save_strategy: steps
	save_steps: 613
	num_train_epochs: 8
	save_total_limit: 4
	use_flash_attention: false
	residual_dropout: 0.3
	residual_dropout_lima: true
	```


	Dataset:
	```
	sft9-stage2:
	# oasst_export: 100.00% (29899)
	# vicuna: 50.00% (16963)
	# code_alpaca: 50.00% (9510)
	# oa_wiki_qa_bart_10000row: 100.00% (9434)
	# grade_school_math_instructions: 100.00% (8351)
	# dolly15k: 100.00% (14250)

	use_custom_sampler: true
	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: 2023-06-02_oasst_all_labels.jsonl.gz
	val_split: 0.05
	top_k: 2
	- vicuna:
	fraction: 0.5
	val_split: 0.025
	max_val_set: 250
	- code_alpaca:
	fraction: 0.5
	val_split: 0.05
	max_val_set: 250
	- oa_wiki_qa_bart_10000row:
	val_split: 0.05
	max_val_set: 250
	- grade_school_math_instructions:
	val_split: 0.05
	- dolly15k:
	val_split: 0.05
	max_val_set: 300

	```

	*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.

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