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Quest-AI
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rm-proto-7b-s104-v1

Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
rm-proto-7b-s104-v1
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---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- json
model-index:
- name: root/cproject_updated/conv_200k_14b
  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: `0.6.0`
```yaml
base_model: /root/cproject_updated/Qwen2.5-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
trust_remote_code: true

plugins:
  - axolotl.integrations.liger.LigerPlugin

liger_rope: true
liger_rms_norm: true
liger_swiglu: true
liger_fused_linear_cross_entropy: false

load_in_8bit: false
load_in_4bit: false
strict: false

auto_resume_from_checkpoints: true

datasets:
  - path: json
    data_files: /root/cproject_updated/judge_1k_axolotl.jsonl
    ds_type: json
    type: completion

shuffle_merged_datasets: true
dataset_prepared_path: /root/cproject_updated/prnew142
val_set_size: 0.05
output_dir: /root/cproject_updated/conv_200k_14b
sequence_len: 8192
sample_packing: true
eval_sample_packing: false

gradient_accumulation_steps: 1
micro_batch_size: 1
num_epochs: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine

learning_rate: 1e-5
adam_beta1: 0.99
adam_beta2: 0.99
max_grad_norm: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: true

logging_steps: 1
flash_attention: true
warmup_steps: 10
eval_steps: 26
saves_per_epoch: 1

deepspeed: /sky_workdir/axolotl/deepspeed_configs/zero3_bf16.json

auto_resume_from_checkpoints: false
wandb_project: corruption_model_rm
wandb_entity:
wandb_watch:
wandb_name: rm-test-v1-7b-adammax2
wandb_log_model:
```

</details><br>

# root/cproject_updated/conv_200k_14b

This model was trained from scratch on the json dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5430

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 8
- optimizer: Use paged_adamw_8bit with betas=(0.99,0.99) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7452        | 1.0   | 26   | 0.7888          |
| 0.6347        | 2.0   | 52   | 0.6729          |
| 0.6479        | 3.0   | 78   | 0.5560          |
| 0.3729        | 4.0   | 104  | 0.5430          |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.0