File size: 3,643 Bytes
0631ff4 eea609a 0631ff4 786ef4b 0631ff4 786ef4b 0631ff4 eea609a 0631ff4 eea609a 0631ff4 |
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 |
---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
model-index:
- name: mixtral-remove-negative-data
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
adam_beta2: 0.95
adam_epsilon: 1.0e-05
adapter: qlora
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
bf16: auto
chat_template: inst
dataset_prepared_path: last_run_prepared
datasets:
- conversation: mistral
path: dd7ba3a8030a4c7382d51a5d894f5cb4/./data/with_function_response/function_used_training.jsonl
type: sharegpt
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_steps: 0.2
eval_table_size: null
flash_attention: true
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: liuylhf/mixtral-remove-negative-data
hub_strategy: end
is_mistral_derived_model: true
learning_rate: 0.001
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
micro_batch_size: 2
model_config:
output_router_logits: true
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: paged_adamw_8bit
output_dir: dd7ba3a8030a4c7382d51a5d894f5cb4/model
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
save_steps: 0.1
sequence_len: 8192
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: false
val_set_size: 0.01
wandb_log_model: end
wandb_name: mixtral-instruct-raw-data-v3
wandb_project: function-call
warmup_steps: 10
weight_decay: 0
xformers_attention: null
```
</details><br>
# mixtral-remove-negative-data
This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0955
## 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: 0.001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.9579 | 0.01 | 1 | 4.0485 |
| 0.148 | 0.2 | 36 | 0.1565 |
| 0.1075 | 0.4 | 72 | 0.1138 |
| 0.099 | 0.6 | 108 | 0.1018 |
| 0.0954 | 0.8 | 144 | 0.0969 |
| 0.0945 | 1.0 | 180 | 0.0955 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.0 |