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dolphin-2.8-mistral-7b-v02

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dolphin-2.8-mistral-7b-v02
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---
base_model: alpindale/Mistral-7B-v0.2-hf
tags:
- generated_from_trainer
model-index:
- name: workspace/dolphin-2.8-mistral-7b
  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: alpindale/Mistral-7B-v0.2-hf
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: /workspace/datasets/dolphin201-sharegpt2.jsonl
    type: sharegpt
  - path: /workspace/datasets/dolphin-coder-translate-sharegpt2.jsonl
    type: sharegpt
  - path: /workspace/datasets/dolphin-coder-codegen-sharegpt2.jsonl
    type: sharegpt
  - path: /workspace/datasets/m-a-p_Code-Feedback-sharegpt.jsonl
    type: sharegpt
  - path: /workspace/datasets/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt.jsonl
    type: sharegpt
  - path: /workspace/datasets/not_samantha_norefusals.jsonl
    type: sharegpt
  - path: /workspace/datasets/openhermes2_5-sharegpt.jsonl
    type: sharegpt

chat_template: chatml

dataset_prepared_path: last_run_prepared
val_set_size: 0.001
output_dir: /workspace/dolphin-2.8-mistral-7b

sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true

wandb_project: dolphin
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 3
num_epochs: 4
adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0
lr_scheduler: cosine
learning_rate: 0.000005
optimizer: adamw_bnb_8bit

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10

eval_steps: 73
eval_table_size:
eval_table_max_new_tokens:
eval_sample_packing: false
saves_per_epoch: 
save_steps: 73
save_total_limit: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|im_end|>"
tokens:
  - "<|im_start|>"

```

</details><br>

# workspace/dolphin-2.8-mistral-7b

This model is a fine-tuned version of [alpindale/Mistral-7B-v0.2-hf](https://huggingface.co/alpindale/Mistral-7B-v0.2-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4828

## 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: 5e-06
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 10
- gradient_accumulation_steps: 8
- total_train_batch_size: 240
- total_eval_batch_size: 30
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1736        | 0.0   | 1    | 1.0338          |
| 0.6106        | 0.36  | 73   | 0.5439          |
| 0.5766        | 0.72  | 146  | 0.5171          |
| 0.5395        | 1.06  | 219  | 0.5045          |
| 0.5218        | 1.42  | 292  | 0.4976          |
| 0.5336        | 1.78  | 365  | 0.4915          |
| 0.5018        | 2.13  | 438  | 0.4885          |
| 0.5113        | 2.48  | 511  | 0.4856          |
| 0.5066        | 2.84  | 584  | 0.4838          |
| 0.4967        | 3.19  | 657  | 0.4834          |
| 0.4956        | 3.55  | 730  | 0.4830          |
| 0.5026        | 3.9   | 803  | 0.4828          |


### Framework versions

- Transformers 4.40.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0