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---
library_name: transformers
license: llama3.1
base_model: meta-llama/Llama-3.1-70B
tags:
- generated_from_trainer
- axolotl
datasets:
- bespokelabs/Bespoke-Stratos-17k
model-index:
- name: outputs/out/reasoning-70b-stratos
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: `0.8.0.dev0`
```yaml
base_model: meta-llama/Llama-3.1-70B
# Automatically upload checkpoint and final model to HF
# hub_model_id: username/custom_model_name
#
plugins:
- axolotl.integrations.liger.LigerPlugin
- axolotl.integrations.spectrum.SpectrumPlugin
spectrum_top_fraction: 0.5
spectrum_model_name: meta-llama/Meta-Llama-3.1-70B
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
strict: false
chat_template: llama3
datasets:
- path: bespokelabs/Bespoke-Stratos-17k
field_messages: conversations
message_property_mappings:
content: value
role: from
split: train
type: chat_template
dataset_prepared_path: last_run_prepared
val_set_size: 0.0
output_dir: ./outputs/out/reasoning-70b-stratos
save_safetensors: true
wandb_project: reasoning-70b-stratos
wandb_entity: axolotl-ai
wandb_watch:
wandb_name:
wandb_log_model:
sequence_len: 16384
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 3
optimizer: adamw_torch_fused
lr_scheduler: rex
learning_rate: 2.0e-6
max_grad_norm: 1.0
train_on_inputs: false
group_by_length: false
bf16: true
tf32: true
gradient_checkpointing: offload
gradient_checkpointing_kwargs:
use_reentrant: true
logging_steps: 1
flash_attention: true
warmup_steps: 20
evals_per_epoch: 4
saves_per_epoch: 2
weight_decay: 0.01
deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
special_tokens:
pad_token: <|finetune_right_pad_id|>
eos_token: <|eot_id|>
added_tokens_overrides:
128011: <think>
128012: </think>
128013: <|begin_of_thought|>
128014: <|end_of_thought|>
128015: <|begin_of_solution|>
128016: <|end_of_solution|>
fix_untrained_tokens:
- 128011
- 128012
- 128013
- 128014
- 128015
- 128016
```
</details><br>
# outputs/out/reasoning-70b-stratos
This model is a fine-tuned version of [meta-llama/Llama-3.1-70B](https://huggingface.co/meta-llama/Llama-3.1-70B) on the bespokelabs/Bespoke-Stratos-17k 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-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: rex
- lr_scheduler_warmup_steps: 20
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
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