transformers safetensors mistral text-generation generated_from_trainer conversational dataset:shisa-ai/shisa-v1-athenev2-reannotated-filtered dataset:shisa-ai/shisa-v2-roleplaying dataset:shisa-ai/translation-master-set dataset:shisa-ai/yahoo-q-and-a-filtered base_model:mistralai/Mistral-Nemo-Instruct-2407 base_model:finetune:mistralai/Mistral-Nemo-Instruct-2407 license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: mistralai/Mistral-Nemo-Instruct-2407
datasets:
- shisa-ai/shisa-v1-athenev2-reannotated-filtered
- shisa-ai/shisa-v2-roleplaying
- shisa-ai/translation-master-set
- shisa-ai/yahoo-q-and-a-filtered
library_name: transformers
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: outputs/ablation-67-rafathenev2.rp.tl.yahoo-mistral-nemo-12b
  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: `0.8.0.dev0`
```yaml
# train w/ shisa-ai/shisa-v1-athenev2-reannotated-filtered

base_model: mistralai/Mistral-Nemo-Instruct-2407
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

# User Liger
plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true

chat_template: llama3
datasets:
  - path: shisa-ai/shisa-v1-athenev2-reannotated-filtered
    # type: sharegpt deprecated
    type: chat_template
    field_messages: conversations
    message_field_role: from
    message_field_content: value
  - path: shisa-ai/shisa-v2-roleplaying
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles:
      system:
        - system
      assistant:
        - gpt
        - model
        - assistant
      user:
        - human
        - user
    roles_to_train: ["assistant"]
  - path: shisa-ai/translation-master-set
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles:
      system:
        - system
      assistant:
        - gpt
        - model
        - assistant
      user:
        - human
        - user
    roles_to_train: ["assistant"]
  - path: shisa-ai/yahoo-q-and-a-filtered
    type: chat_template
    field_messages: conversations
    message_property_mappings:
      role: role
      content: content
    roles:
      system:
        - system
      assistant:
        - gpt
        - model
        - assistant
      user:
        - human
        - user
    roles_to_train: ["assistant"]
    
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./outputs/ablation-67-rafathenev2.rp.tl.yahoo-mistral-nemo-12b

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

# marginal difference
neftune_noise_alpha: 5

use_wandb: true
wandb_project: shisa-v2
wandb_entity: augmxnt
wandb_name: ablation-67-rafathenev2.rp.tl.yahoo-mistral-nemo-12b

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: linear
learning_rate: 8e-6

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

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

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 0
save_total_limit: 1 # Only store a single checkpoint
debug:
deepspeed: zero3_bf16.json
weight_decay: 1e-4
fsdp:
fsdp_config:
special_tokens:
  pad_token: "<pad>"

```

</details><br>

# outputs/ablation-67-rafathenev2.rp.tl.yahoo-mistral-nemo-12b

This model is a fine-tuned version of [mistralai/Mistral-Nemo-Instruct-2407](https://huggingface.co/mistralai/Mistral-Nemo-Instruct-2407) on the shisa-ai/shisa-v1-athenev2-reannotated-filtered, the shisa-ai/shisa-v2-roleplaying, the shisa-ai/translation-master-set and the shisa-ai/yahoo-q-and-a-filtered datasets.
It achieves the following results on the evaluation set:
- Loss: 0.8622

## 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-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.0856        | 0.0029 | 1    | 1.0796          |
| 0.8804        | 0.5004 | 174  | 0.8520          |
| 0.6023        | 1.0    | 348  | 0.8255          |
| 0.6295        | 1.5004 | 522  | 0.8297          |
| 0.4974        | 2.0    | 696  | 0.8200          |
| 0.5554        | 2.5004 | 870  | 0.8622          |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
