---
base_model: meta-llama/Meta-Llama-3-70B
library_name: peft
license: llama3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: llama3-70b-wh_cove_thght_062024_halluc_rem_refusal_runpod
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: meta-llama/Meta-Llama-3-70B
bf16: true
dataset_prepared_path: last_run_prepared
debug: null
deepspeed: null
early_stopping_patience: null
eval_table_size: null
evals_per_epoch: 0
flash_attention: true
fp16: null
deepspeed: /workspace/axolotl/deepspeed_configs/zero3_bf16.json
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
group_by_length: false
hub_model_id: minionai/llama3-70b-wh_cove_thght_062024_halluc_rem_refusal_runpod
hub_strategy: all_checkpoints
learning_rate: 1e-4
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
micro_batch_size: 1
model_type: LlamaForCausalLM
num_epochs: 3
optimizer: adamw_torch
output_dir: ./lora-out
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
wandb_entity: minionai
wandb_name: wh_cove_thght_062024_ift
wandb_project: llama3-70b
saves_per_epoch: 1
sequence_len: 8192
special_tokens:
  pad_token: <|end_of_text|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
val_set_size: 0
warmup_steps: 250
weight_decay: 0.0
datasets:
- path: minionai/wh_w_cv_thoughts_062024_halluc_filt_refusal_add_ift
  type: 
      system_prompt: ""
      system_format: "{system}"
      field_system: system
      field_instruction: instruction
      field_input: input
      field_output: output
      format: |-
        User: {instruction} {input}
        Assistant:
      # 'no_input_format' cannot include {input}
      no_input_format: "### System:\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\nverify(\""

```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/minionai/llama3-70b/runs/czcejakf)
# llama3-70b-wh_cove_thght_062024_halluc_rem_refusal_runpod

This model is a fine-tuned version of [meta-llama/Meta-Llama-3-70B](https://huggingface.co/meta-llama/Meta-Llama-3-70B) 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: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- 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: 250
- num_epochs: 3

### Training results



### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1