---
library_name: peft
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 24ade83e-027e-4383-8762-a809fb955437
  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: `0.4.1`
```yaml
adapter: lora
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 5ec7c0c178ba1b04_train_data.json
  ds_type: json
  field: prompt
  path: /workspace/input_data/
  split: train
  type: completion
ddp_find_unused_parameters: false
debug: null
deepspeed: null
early_stopping_patience: null
eps: 1.0e-06
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
gradient_normalization: true
greater_is_better: false
group_by_length: false
hub_model_id: CheapsetZero/24ade83e-027e-4383-8762-a809fb955437
learning_rate: 0.00024
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_nan_inf_filter: true
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1496
metric_for_best_model: eval_loss
micro_batch_size: 24
min_lr: 4.8e-05
mlflow_experiment_name: /tmp/5ec7c0c178ba1b04_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
reward_model_sampling_temperature: 0.7
s2_attention: null
sample_packing: false
save_total_limit: 3
saves_per_epoch: 4
sequence_len: 1024
skip_nan_gradients: true
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trl:
  adaptive_beta: true
  beta: 0.12
  entropy_coeff: 0.01
  gradient_normalization: true
  kl_monitoring: true
  max_completion_length: 1024
  num_generations: 7
  reward_funcs:
  - rewards_4ae4b631-baf3-48ba-908b-b393d1e81e48.reward_think_answer_format_normalized
  reward_weights:
  - 5.0
  target_kl: 0.01
  use_vllm: false
trust_remote_code: true
use_ema: false
use_peft: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: 4ae4b631-baf3-48ba-908b-b393d1e81e48
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 4ae4b631-baf3-48ba-908b-b393d1e81e48
warmup_steps: 214
weight_decay: 0.01
xformers_attention: null

```

</details><br>

# 24ade83e-027e-4383-8762-a809fb955437

This model is a fine-tuned version of [Vikhrmodels/Vikhr-7B-instruct_0.4](https://huggingface.co/Vikhrmodels/Vikhr-7B-instruct_0.4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan

## 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: 0.00024
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 214
- training_steps: 1496

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0           | 0.0016 | 1    | nan             |
| 0.0           | 0.2006 | 125  | nan             |
| 0.0           | 0.4013 | 250  | nan             |
| 0.0           | 0.6019 | 375  | nan             |
| 0.0           | 0.8026 | 500  | nan             |
| 0.0           | 1.0032 | 625  | nan             |
| 0.0           | 1.2039 | 750  | nan             |
| 0.0           | 1.4045 | 875  | nan             |
| 0.0           | 1.6051 | 1000 | nan             |
| 4.9718        | 1.8058 | 1125 | nan             |
| 0.0           | 2.0064 | 1250 | nan             |
| 0.0           | 2.2071 | 1375 | nan             |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1