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arcee-ai
/
raspberry-3B

Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
conversational
text-generation-inference
Inference Endpoints
Model card Files Community
4
raspberry-3B
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---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-3B
tags:
- generated_from_trainer
model-index:
- name: outputs/gelato-3b
  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
base_model: Qwen/Qwen2.5-3B
load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: arcee-ai/eval_tome
    type: sharegpt
    conversation: chatml
  - path: arcee-ai/math_code_5k_claude
    type: sharegpt
    conversation: chatml
    split: validation
  - path: Undi95/Capybara-ShareGPT
    type: sharegpt
    conversation: chatml
dataset_prepared_path:
val_set_size: 0.0

sequence_len: 8192
sample_packing: true

lora_fan_in_fan_out:
wandb_project: qwen2.5-3b-gelato
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
output_dir: ./outputs/gelato-3b
gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
torchdistx_path:
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16: 
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
gptq_groupsize:
s2_attention:
gptq_model_v1:
warmup_steps: 50
evals_per_epoch:
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  eos_token: "<|im_end|>"
  bos_token: "<|im_start|>"

```

</details><br>

# outputs/gelato-3b

This model is a fine-tuned version of [Qwen/Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) on the None 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 4

### Training results



### Framework versions

- Transformers 4.45.1
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0