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Open Assistant's Falcon 40B SFT MIX GGML

These files are GGCC format model files for Open Assistant's Falcon 40B SFT MIX.

These files will not work in llama.cpp, text-generation-webui or KoboldCpp.

GGCC is a new format created in a new fork of llama.cpp that introduced this new Falcon GGML-based support: cmp-nc/ggllm.cpp.

Currently these files will also not work with code that previously supported Falcon, such as LoLLMs Web UI and ctransformers. But support should be added soon.

Repositories available

Prompt template

<|prompter|>prompt<|endoftext|><|assistant|>

Compatibility

To build cmp-nct's fork of llama.cpp with Falcon support plus CUDA acceleration, please try the following steps:

git clone https://github.com/cmp-nct/ggllm.cpp
cd ggllm.cpp
rm -rf build && mkdir build && cd build && cmake -DGGML_CUBLAS=1 .. && cmake --build . --config Release

Compiling on Windows: developer cmp-nct notes: 'I personally compile it using VScode. When compiling with CUDA support using the Microsoft compiler it's essential to select the "Community edition build tools". Otherwise CUDA won't compile.'

Once compiled you can then use bin/falcon_main just like you would use llama.cpp. For example:

bin/falcon_main -t 8 -ngl 100 -b 1 -m falcon-40b-sft-mix-1226.ggccv1.q4_K.bin -p "<|prompter|>write a story about llamas<|endoftext|><|assistant|>"

You can specify -ngl 100 regardles of your VRAM, as it will automatically detect how much VRAM is available to be used.

Adjust -t 8 (the number of CPU cores to use) according to what performs best on your system. Do not exceed the number of physical CPU cores you have.

-b 1 reduces batch size to 1. This slightly lowers prompt evaluation time, but frees up VRAM to load more of the model on to your GPU. If you find prompt evaluation too slow and have enough spare VRAM, you can remove this parameter.

Please see https://github.com/cmp-nct/ggllm.cpp for further details and instructions.

Provided files

Name	Quant method	Bits	Size	Max RAM required	Use case
falcon-40b-sft-mix-1226.ggccv1.q2_K.bin	q2_K	2	13.74 GB	16.24 GB	New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors.
falcon-40b-sft-mix-1226.ggccv1.q3_K.bin	q3_K_S	3	17.98 GB	20.48 GB	New k-quant method. Uses GGML_TYPE_Q3_K for all tensors
falcon-40b-sft-mix-1226.ggccv1.q4_K.bin	q4_K_S	4	23.54 GB	26.04 GB	New k-quant method. Uses GGML_TYPE_Q4_K for all tensors
falcon-40b-sft-mix-1226.ggccv1.q5_K.bin	q5_K_S	5	28.77 GB	31.27 GB	New k-quant method. Uses GGML_TYPE_Q5_K for all tensors
falcon-40b-sft-mix-1226.ggccv1.q6_K.bin	q6_K	6	34.33 GB	36.83 GB	New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors
falcon-40b-sft-mix-1226.ggccv1.q8_0.bin	q8_0	8	44.46 GB	46.96 GB	Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users.

Note: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.

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Original model card: Open Assistant's Falcon 40B SFT MIX

Open-Assistant Falcon 40B SFT MIX Model

This model is a fine-tuning of TII's 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
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, multiligual-60
Eval results: ilm-eval
Weights & Biases: Training log (checkpoint: 1226 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
  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

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falcon-40b-sft-mix-1226-GGML