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---
library_name: transformers
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- axolotl
- generated_from_trainer
datasets:
- custom
model-index:
- name: test
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: `0.12.0.dev0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: AutoTokenizer
datasets:
- path: /workspace/axolotl/train_model/data/finetune_dataset.jsonl
type:
field_instruction: prompt
field_output: response
format: "[INST] {instruction} [/INST]"
no_input_format: "[INST] {instruction} [/INST]"
system_prompt: "" # optionnel
val_set_size: 0.05
output_dir: /workspace/outputs-lovelace
hub_model_id: mikefol/test
sequence_len: 2048
sample_packing: true
eval_sample_packing: false
micro_batch_size: 4
gradient_accumulation_steps: 4
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-5
gradient_checkpointing: true
use_wandb: false
push_to_hub: false
hub_private_repo: false
trust_remote_code: true
save_strategy: "no"
save_optimizer: false
save_safetensors: false
```
</details><br>
# test
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the /workspace/axolotl/train_model/data/finetune_dataset.jsonl 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use paged_adamw_32bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- training_steps: 21
### Training results
### Framework versions
- Transformers 4.53.1
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.2
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