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cosmosage-v3

Text Generation
Transformers
PyTorch
Safetensors
English
llama
physics
cosmology
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
2
cosmosage-v3
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---
tags:
- generated_from_trainer
model-index:
- name: home/005/th5351/output
  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/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
base_model: /home/005/th5351/models/cosmosage-llama3-8b-base/
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: llama3
datasets:
  - path: /home/005/th5351/datasets/combined_sft.jsonl
    type: chat_template
    chat_template: llama3
    field_messages: conversations
    message_field_role: from
    message_field_content: value
    roles:
      system:
        - system
      user:
        - human
      assistant:
        - gpt
    

dataset_prepared_path: /home/005/th5351/output/last_run_prepared
val_set_size: 0.001
eval_sample_packing: false
output_dir: /home/005/th5351/output

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 5e-5
cosine_min_lr_ratio: 0.2
cosine_constant_lr_ratio: 0.8
max_grad_norm: 3.0

seed: 42

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

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
evals_per_epoch: 5
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed: /home/005/th5351/packages/axolotl/deepspeed_configs/zero2.json
ddp_timeout: 3600000
weight_decay: 0.0
fsdp:
fsdp_config:

```

</details><br>

# home/005/th5351/output

This model was trained from scratch 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3757        | 0.0005 | 1    | nan             |
| 0.8083        | 0.1999 | 388  | nan             |
| 0.8005        | 0.3998 | 776  | nan             |
| 0.7389        | 0.5998 | 1164 | nan             |
| 0.7269        | 0.7997 | 1552 | nan             |
| 0.7069        | 0.9996 | 1940 | nan             |
| 0.5786        | 1.1613 | 2328 | nan             |
| 0.5385        | 1.3613 | 2716 | nan             |
| 0.5381        | 1.5612 | 3104 | nan             |
| 0.5273        | 1.7611 | 3492 | nan             |
| 0.527         | 1.9610 | 3880 | nan             |


### Framework versions

- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1