---
library_name: transformers
license: apache-2.0
base_model: Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat
tags:
- axolotl
- generated_from_trainer
datasets:
- Dans-DiscountModels/dpe-130l-m-24b-32k
model-index:
- name: 24b-ms-dans-personality-engine-v1.3.0L-TestArticle-1
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.10.0.dev0`
```yaml
base_model: Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

trust_remote_code:

# wandb configuration
wandb_project: 24b-ms-dans-personality-engine
wandb_watch:

wandb_run_id: V1.3.0L-1-3 # V{Version}-{Run Number}-{Attempt Number}
wandb_log_model:

# push checkpoints to hub
hub_model_id: Dans-DiscountModels/24b-ms-dans-personality-engine-v1.3.0L-TestArticle-1
# how to push checkpoints to hub
# https://huggingface.co/docs/transformers/v4.31.0/en/main_classes/trainer#transformers.TrainingArguments.hub_strategy
hub_strategy: "every_save"
# Whether to use hf `use_auth_token` for loading datasets. Useful for fetching private datasets
# Required to be true when used in combination with `push_dataset_to_hub`
hf_use_auth_token: true

# where to save the finished model to
output_dir: ./24b-ms-dans-personality-engine

save_safetensors: true

datasets:
  - path: Dans-DiscountModels/dpe-130l-m-24b-32k
    split: train
    ds_type: parquet
    type:

test_datasets:
  - path: Dans-DiscountModels/dpe-130l-m-24b-32k
    split: validation
    ds_type: parquet
    type:


plugins:
  - axolotl.integrations.liger.LigerPlugin
  - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: false
cut_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

adapter:
lora_model_dir:

dataset_prepared_path: ./24b-ms-dans-personality-engine-data

sequence_len: 32768

sample_packing: true
eval_sample_packing: true

pad_to_sequence_len: true

gradient_checkpointing: true

gradient_accumulation_steps: 4
micro_batch_size: 1

num_epochs: 1

optimizer: ademamix_8bit
optim_args: "beta1=0.9,beta2=0.999,beta3=0.999,alpha=5"

lr_scheduler: rex
learning_rate: 0.0000012
cosine_min_lr_ratio: 0.1

max_grad_norm: 0.001

train_on_inputs: false
group_by_length: false

bf16: true
fp16: false
tf32: false

early_stopping_patience:

resume_from_checkpoint:
auto_resume_from_checkpoints: false

local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_ratio: 0.1

evals_per_epoch: 10
eval_table_size:
eval_max_new_tokens:

saves_per_epoch: 4
save_total_limit: 1

debug: false

deepspeed: deepspeed_configs/zero3_bf16.json

fsdp:
fsdp_config:

special_tokens:

```

</details><br>

# 24b-ms-dans-personality-engine-v1.3.0L-TestArticle-1

This model is a fine-tuned version of [Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat](https://huggingface.co/Dans-DiscountModels/Mistral-Small-3.1-24B-Base-2503-hf-DanChat) on the Dans-DiscountModels/dpe-130l-m-24b-32k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3214

## 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: 1.2e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 8
- optimizer: Use ademamix_8bit and the args are:
beta1=0.9,beta2=0.999,beta3=0.999,alpha=5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 48
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4826        | 0.0021 | 1    | 1.4263          |
| 1.4024        | 0.1012 | 49   | 1.3709          |
| 1.4655        | 0.2024 | 98   | 1.3545          |
| 1.576         | 0.3036 | 147  | 1.3459          |
| 1.3687        | 0.4047 | 196  | 1.3396          |
| 1.4367        | 0.5059 | 245  | 1.3346          |
| 1.3409        | 0.6071 | 294  | 1.3304          |
| 1.4442        | 0.7083 | 343  | 1.3270          |
| 1.4049        | 0.8095 | 392  | 1.3242          |
| 1.5044        | 0.9107 | 441  | 1.3214          |


### Framework versions

- Transformers 4.51.3
- Pytorch 2.4.1+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1