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nyu-dice-lab/
Mistral-7B-Base-SFT-Tulu2-2.0

Name: Mistral-7B-Base-SFT-Tulu2-2.0
Availability: InStock

generated_from_trainer

mistralai/Mistral-7B-v0.3

finetune

apache-2.0

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See axolotl config

axolotl version: 0.4.1

yaml

base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: allenai/tulu-v2-sft-mixture
    type: sharegpt.load_ultrachat
    conversation: llama3

chat_template: llama3

dataset_prepared_path: ./datasets/tulu-v2-sft-3.0
output_dir: ./outputs/m7b-tulu-v2-sft-3.0

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project: lm-evals
wandb_entity:
wandb_watch:
wandb_name: Mistral-7B-tulu-v2-sft-2.0
wandb_log_model:
hub_model_id: penfever/Mistral-7B-tulu-v2-sft-2.0

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 5e-6

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: <|begin_of_text|>
  eos_token: <|end_of_text|>
  pad_token: <|end_of_text|>
tokens:
  - "<|start_header_id|>"
  - "<|end_header_id|>"
  - "<|eot_id|>"

Visualize in Weights & Biases

Mistral-7B-tulu-v2-sft-2.0

This model is a fine-tuned version of mistralai/Mistral-7B-v0.3 on the None dataset.

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 5e-06
train_batch_size: 1
eval_batch_size: 1
seed: 42
distributed_type: multi-GPU
num_devices: 4
gradient_accumulation_steps: 8
total_train_batch_size: 32
total_eval_batch_size: 4
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_steps: 100
num_epochs: 3

Training results

Framework versions

Transformers 4.43.0.dev0
Pytorch 2.3.1+cu121
Datasets 2.19.1
Tokenizers 0.19.1

nyu-dice-lab/Mistral-7B-Base-SFT-Tulu2-2.0

Mistral-7B-tulu-v2-sft-2.0

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

nyu-dice-lab/
Mistral-7B-Base-SFT-Tulu2-2.0