UltronTactIQ-Mistral-2-7b

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<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

adapter: null
base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
batch_size: 2
bf16: auto
dataset_prepared_path: null
datasets:
- ds_type: json
  path: /workspace/data.jsonl
  type: context_qa.load_v2
debug: null
deepspeed: null
early_stopping_patience: null
evals_per_epoch: 4
flash_attention: null
fp16: null
fsdp: null
fsdp_config: null
gptq_groupsize: null
gptq_model_v1: null
gradient_checkpointing: true
group_by_length: false
learning_rate: 1.0e-05
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.2
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
max_packed_sequence_len: null
micro_batch_size: 1
model_config:
  output_router_logits: true
model_type: MistralForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: /workspace/out-mistral-2B
resume_from_checkpoint: null
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
  bos_token: <s>
  eos_token: <|im_end|>
  pad_token: </s>
tf32: true
tokenizer_type: LlamaTokenizer
torchdistx_path: null
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_log_model: Nous-Hermes-2-Mistral-7B-DPO
wandb_name: mistral
wandb_project: Ultron-llama
wandb_watch: null
warmup_steps: 40
weight_decay: 0.0
xformers_attention: true

</details><br>

workspace/out-mistral-2B

This model is a fine-tuned version of NousResearch/Nous-Hermes-2-Mistral-7B-DPO on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.5036

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: 1e-05
• train_batch_size: 1
• eval_batch_size: 1
• seed: 42
• distributed_type: multi-GPU
• num_devices: 2
• gradient_accumulation_steps: 2
• total_train_batch_size: 4
• total_eval_batch_size: 2
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 40
• num_epochs: 4

Training results

Framework versions

• Transformers 4.39.0.dev0
• Pytorch 2.1.2+cu121
• Datasets 2.18.0
• Tokenizers 0.15.0