Is the FT script correct? about self-rag

[emrgnt-cmplxty]

I could not get the finetune script in the directory to work on Mistral.

However, I ran the steps above to update the tokenizer of the model (mistral ft'ed on textbooks) I wished to further fine-tune and then ran over the self-rag dataset with Axolotl. With my config I was able to complete two epochs in just several hours. The outputted model is here. I found an ARC challenge score of 75% using the same args as described in the repo.

Great work self-rag team, this looks really impressive. I will have the full pipeline online and easy to access shortly.

EDIT: Doing two more epochs now, to see how further tuning impacts the scores.

[emrgnt-cmplxty]

First pass is online now - https://www.reddit.com/r/LocalLLaMA/comments/17knjfz/update_from_sciphi_introducing/?rdt=55834.

The model is looking quite powerful for the size. I am hopeful that more people will continue to build on the self-rag work.

[AkariAsai]

Hi @emrgnt-cmplxty, thank you for tyring it out! I haven't tried Mistral by myself yet, so I am not sure how they process new special tokens... For Llama2-7b/13B or Llama1, we didn't have any issue adding special tokens.
Yes I'd recomment to doube check whether the special tokens are proprietly added and used during fine-tuning (e.g., print out the tokenized output and see if the special tokens are appearing in the processed output).
Also at one point, my co-author @yizhongw and I found that llama2 tokenizer was adding [UNK] tokens a lot and hurted the performance sigificantly when Yizhong was using slightly olrder version of huggingface transformers. Probably it might also help to double check whether the tokenizer output does not have any weird [UNK] tokens.

[emrgnt-cmplxty]

@AkariAsai Would it be a large hassle to outline how to extend the tokenizer with the process that you used? I think this would be very helpful for myself and for others. This would also allow us to use other training software, like Axolotl.

[AkariAsai]

If you are loading model checkpoints from huggingface transformers, it only requires two lines of code.

1. tokenizer.add_special_tokens to expand the special tokens - https://github.com/AkariAsai/self-rag/blob/main/retrieval_lm/finetune.py#L460
2. model.resize_token_embeddings to expand the embedding size - https://github.com/AkariAsai/self-rag/blob/main/retrieval_lm/finetune.py#L481

But I am not sure why the Mistra-7B fine-tuning got lower scores, though... I can take a look at fine-tuning of Mistra early next week.

[AkariAsai]

Cool, congrats!! Thank you so much for all of the help & contributions!

If you are implementing your own fine-tuning script , another key thing for Self-RAG is to add cntext markup to markup the retrived context surrounded by <paragraph> & </paragraph>.
I found even without this our model often performs fine in open-domain QA or classification tasks, but for long-form QA, this might be crucial (e.g., without this, the model starts generating paragraph by itself.
https://github.com/AkariAsai/self-rag/blob/main/retrieval_lm/finetune.py#L274

[emrgnt-cmplxty]

Oh, I see. I did not add this into my FT logic. I have set my completion rules to stop on <paragraph> tokens and everything seems to be working as expected. What is the impact of missing this logic? Is there any way to pre-compute this and then re-upload the data?

Either way, I will try to integrate the proper functionality for this into my workflow with Axolotl, though this is a framework I am still picking up.

Lastly, one thing I am noticing is that my FT'ed model attempts to retrieve after every completion when writing long-form content. Have you seen this before? Is it likely to be related to the failure to use the logic you outlined above?

EDIT - Disregard the first question. Reading through the code a second time, I now see that failing to mask the paragraph tokens will mean that the model is trained to predict them.

[AkariAsai]

This is a fantastic news! Thank you so much for all the work! I'll add mentions to this model in our README.

[AkariAsai]

I am closing this issue now, but feel free to reopen it!

[chanchimin]

wo epoc

Hi, I appreciate your attempt! I am wondering what machines you use to run that script, I tried to run the same scripts using 8*V100 32g, but it seems that it would take hundreds of hours to complete my training, and because V100 does not support bf16 so I changed the accelerate argument to use fp16, I am not sure if I am doing it right.

[1716649290]

First pass is online now - https://www.reddit.com/r/LocalLLaMA/comments/17knjfz/update_from_sciphi_introducing/?rdt=55834.

The model is looking quite powerful for the size. I am hopeful that more people will continue to build on the self-rag work.

Hello, I recently wanted to use the axolotl library for further fine-tuning based on your SciPhi-Self-RAG-Mistral-7B-32k model for the medical field, but my current training cannot achieve convergence. My parameters are as follows: ( Can I ask about your training parameter settings?)

base_model: SciPhi-Self-RAG-Mistral-7B-32k
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:

• path: /home/zhongjin/mnt_data/cl/data/bio_new.jsonl
  type: alpaca
  dataset_prepared_path: last_run_prepared
  val_set_size: 0
  output_dir: ./lora-out

adapter: lora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 64
lora_alpha: 128
lora_dropout: 0.1
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:

• gate_proj
• down_proj
• up_proj
• q_proj
• v_proj
• k_proj
• o_proj

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 16
micro_batch_size: 2
num_epochs: 5
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00002

train_on_inputs: false
group_by_length: false
bf16: false
fp16: true
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention:

loss_watchdog_threshold: 10.0
loss_watchdog_patience: 3
warmup_ratio: 0.03
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config: