Edit model card

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT
model_type: MixtralForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true

hub_model_id: MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca
hf_use_auth_token: true

load_in_4bit: true
strict: false

datasets:
  - path: tatsu-lab/alpaca
    type: alpaca
    
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-out

# save_safetensors: true

adapter: qlora
lora_model_dir: 

sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
#  - gate
  - q_proj
#  - k_proj
  - v_proj
#  - o_proj
#  - w1
#  - w2
#  - w3

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
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>"

Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca

This model is a fine-tuned version of NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.0276

How to use

PEFT

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

config = PeftConfig.from_pretrained("MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca")
model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT")
model = PeftModel.from_pretrained(model, "MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca")

Transformers

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca")

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: 2
  • eval_batch_size: 2
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 4
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • total_eval_batch_size: 8
  • 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
1.3912 0.01 1 1.3714
1.0321 0.25 45 1.0427
1.0312 0.51 90 1.0327
0.9917 0.76 135 1.0276

Framework versions

  • PEFT 0.8.2
  • Transformers 4.38.0.dev0
  • Pytorch 2.2.0+cu121
  • Datasets 2.17.0
  • Tokenizers 0.15.0
Downloads last month
3
Inference Examples
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social visibility and check back later, or deploy to Inference Endpoints (dedicated) instead.

Model tree for MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca

Adapter
(2)
this model
Quantizations
1 model

Collection including MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Alpaca