Hugging Face's logo

minionai
/
llama3-70b-wh_cove_thght_062024_halluc_rem_refusal_runpod_rename

PEFT
Safetensors
llama
axolotl
Generated from Trainer
Model card Files Community
llama3-70b-wh_cove_thght_062024_halluc_rem_refusal_runpod_rename
File size: 3,747 Bytes 
f110873
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
 
---
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