---
library_name: peft
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3ab27b2c-001b-43ef-8445-1c972f2f32e0
  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
adam_epsilon: 1e-6
adapter: lora
base_model: Vikhrmodels/Vikhr-7B-instruct_0.4
bf16: true
chat_template: llama3
dataloader_num_workers: 0
dataloader_pin_memory: false
dataset_prepared_path: null
datasets:
- data_files:
  - bdadc8ad66cb9a94_train_data.json
  ds_type: json
  field: prompt
  path: /workspace/input_data/
  split: train
  type: completion
debug: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 2
flash_attention: false
fp16: false
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: CheapsetZero/3ab27b2c-001b-43ef-8445-1c972f2f32e0
hub_strategy: checkpoint
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 64
lora_dropout: 0.05
lora_r: 32
lora_target_linear: true
lr_scheduler: constant_with_warmup
max_steps: 4000
micro_batch_size: 16
mlflow_experiment_name: /tmp/bdadc8ad66cb9a94_train_data.json
model_type: AutoModelForCausalLM
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
sample_packing: false
save_safetensors: true
save_total_limit: 3
saves_per_epoch: 2
sequence_len: 512
strict: false
tf32: true
tokenizer_type: AutoTokenizer
torch_compile: false
train_on_inputs: false
trl:
  beta: 0.1
  clip_rewards: true
  max_completion_length: 256
  normalize_rewards: true
  num_generations: 8
  reward_clip_value: 10.0
  reward_funcs:
  - rewards_463aed43-c37f-4627-a1e3-4087047e7890.reward_long_completions
  - rewards_463aed43-c37f-4627-a1e3-4087047e7890.reward_short_sentences
  - rewards_463aed43-c37f-4627-a1e3-4087047e7890.reward_low_syllables_per_word
  - rewards_463aed43-c37f-4627-a1e3-4087047e7890.reward_low_difficult_words_percentage
  - rewards_463aed43-c37f-4627-a1e3-4087047e7890.reward_high_readability
  reward_weights:
  - 9.78318096270829
  - 7.187549400576799
  - 1.1055886683328564
  - 0.17903499586198302
  - 8.012345598633374
  temperature: 1.0
  use_vllm: false
trust_remote_code: true
val_set_size: 0.05
wandb_mode: online
wandb_name: 463aed43-c37f-4627-a1e3-4087047e7890
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 463aed43-c37f-4627-a1e3-4087047e7890
warmup_steps: 100
weight_decay: 0.01
xformers_attention: false

```

</details><br>

# 3ab27b2c-001b-43ef-8445-1c972f2f32e0

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: 0.0002
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-06 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 100
- training_steps: 1646

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0006 | 1    | nan             |
| 0.0           | 0.5    | 823  | nan             |
| 0.0           | 1.0    | 1646 | nan             |


### Framework versions

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