metadata
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
model-index:
- name: Mistral-7B-instruct-v0.2
results: []
See axolotl config
axolotl version: 0.4.0
base_model: mistralai/Mistral-7B-Instruct-v0.2
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true
hub_model_id: malmarjeh/Mistral-7B-instruct-v0.2
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: bitext/Bitext-customer-support-llm-chatbot-training-dataset
type:
system_prompt: "You are an expert in customer support."
field_instruction: instruction
field_output: response
format: "[INST] {instruction} [/INST]"
no_input_format: "[INST] {instruction} [/INST]"
#datasets:
# - path: json
# type: alpaca_w_system.load_open_orca
#data_files: file.zip
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: False
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
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: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 8
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
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:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
Mistral-7B-instruct-v0.2
This model is a fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.7667
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: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
Training results
Training Loss | Epoch | Step | Validation Loss |
---|---|---|---|
1.6865 | 0.01 | 1 | 2.0557 |
0.6351 | 0.25 | 32 | 0.8355 |
0.5724 | 0.5 | 64 | 0.7859 |
0.5249 | 0.75 | 96 | 0.7711 |
0.516 | 1.0 | 128 | 0.7667 |
Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0