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falcon-7b-sft-top1-696 - GGUF

Model creator: OpenAssistant
Original model: falcon-7b-sft-top1-696

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

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 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 7B SFT OASST-TOP1 Model

This model is a fine-tuning of TII's Falcon 7B LLM. It was trained with 11,123 top-1 (high-quality) demonstrations of the OASST data set (exported on June 2, 2023) with a batch size of 128 for 8 epochs with LIMA style dropout (p=0.2) and a context-length of 2048 tokens.

Model Details

Finetuned from: tiiuae/falcon-7b
Model type: Causal decoder-only transformer language model
Language: English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
Weights & Biases: Training log (Checkpoint: 696 steps)
Code: Open-Assistant/model/model_training
Demo: Continuations for 250 random prompts
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.

Sample Code

from transformers import AutoTokenizer
import transformers
import torch

model = "OpenAssistant/falcon-7b-sft-top1-696"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
    device_map="auto",
)

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

sequences = pipeline(
    input_text,
    max_length=500,
    do_sample=True,
    return_full_text=False,
    top_k=10,
    num_return_sequences=1,
    eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
    print(f"Result: {seq['generated_text']}")

Configuration Details

Model:

falcon-7b:
  dtype: bf16
  log_dir: "falcon_log_7b"
  learning_rate: 1e-5
  model_name: "tiiuae/falcon-7b"
  deepspeed_config: configs/zero_config.json
  output_dir: falcon
  weight_decay: 0.0
  max_length: 2048
  save_strategy: steps
  eval_steps: 80
  save_steps: 80
  warmup_steps: 20
  gradient_checkpointing: true
  gradient_accumulation_steps: 4
  per_device_train_batch_size: 4
  per_device_eval_batch_size: 8
  num_train_epochs: 8
  save_total_limit: 4
  residual_dropout: 0.2
  residual_dropout_lima: true

Dataset:

oasst-top1:
  # oasst_export: 11123 (100.00%)
  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: 1

Train command:

deepspeed trainer_sft.py --configs defaults falcon-7b oasst-top1 --cache_dir <data_cache_dir> --output_dir <output_path> --deepspeed

Export command:

python export_model.py --dtype bf16 --hf_repo_name OpenAssistant/falcon-7b-sft-top1 --trust_remote_code --auth_token <auth_token> <output_path> --max_shard_size 2GB

End of original Model File

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