Hugging Face's logo

AlekseyKorshuk
/
WizardCoder-1B-V1.0-dpo-beta-0.01

Text Generation
Transformers
PyTorch
TensorBoard
Safetensors
gpt_bigcode
axolotl
dpo
trl
Generated from Trainer
text-generation-inference
Inference Endpoints
Model card Files Metrics Community
WizardCoder-1B-V1.0-dpo-beta-0.01
File size: 3,401 Bytes 
a880f89
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
 
---
license: bigcode-openrail-m
base_model: WizardLM/WizardCoder-1B-V1.0
tags:
- axolotl
- dpo
- trl
- dpo
- generated_from_trainer
model-index:
- name: WizardCoder-1B-V1.0-dpo-beta-0.01
  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: `0.4.0`
```yaml
base_model: WizardLM/WizardCoder-1B-V1.0
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

hub_model_id: AlekseyKorshuk/WizardCoder-1B-V1.0-dpo-beta-0.01
hub_strategy: every_save

load_in_8bit: false
load_in_4bit: false
strict: false

rl: dpo
datasets:
  - path: AlekseyKorshuk/evol-codealpaca-v1-dpo
    split: train
    type: wizardcoder.intel


dataset_prepared_path:
#val_set_size: 0.001
output_dir: ./output

sequence_len: 2048
#sample_packing: false  # currently unsupported
pad_to_sequence_len:

lora_r:
lora_alpha:
lora_dropout:
lora_target_modules:
lora_target_linear:
lora_fan_in_fan_out:

wandb_project: ui-thesis
wandb_entity:
wandb_watch:
wandb_name: ultrachat-stable-code-3b-dpo-chatml-beta-0.01
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 1
optimizer: paged_adamw_8bit
adam_beta1: 0.9
adam_beta2: 0.95
max_grad_norm: 1.0
adam_epsilon: 0.00001
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
learning_rate: 8.0e-7
warmup_steps: 32
#warmup_ratio: 0.1
weight_decay: 0.01
dpo_beta: 0.01

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true
#float16: true

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false


#evals_per_epoch: 5
#eval_table_size: 8 # Approximate number of predictions sent to wandb depending on batch size. Enabled above 0. Default is 0
#eval_table_max_new_tokens: 768 # Total number of tokens generated for predictions sent to wandb. Default is 128

#chat_template: chatml
#saves_per_epoch: 1
save_steps: 500
save_total_limit: 1
seed: 42
debug:
deepspeed:


fsdp:
fsdp_config:
resize_token_embeddings_to_32x: true

```

</details><br>

# WizardCoder-1B-V1.0-dpo-beta-0.01

This model is a fine-tuned version of [WizardLM/WizardCoder-1B-V1.0](https://huggingface.co/WizardLM/WizardCoder-1B-V1.0) 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: 8e-07
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 32
- training_steps: 312

### Training results



### Framework versions

- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0