Model save

README.md

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+---
+library_name: transformers
+license: llama3.2
+base_model: meta-llama/Llama-3.2-3B-Instruct
+tags:
+- axolotl
+- generated_from_trainer
+datasets:
+- yahma/alpaca-cleaned
+model-index:
+- name: qat-llama-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: `0.13.0.dev0`
+```yaml
+base_model: meta-llama/Llama-3.2-3B-Instruct
+# Automatically upload checkpoint and final model to HF
+hub_model_id: smohammadi/qat-llama-3B # username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+
+    #liger_rope: true
+    #liger_rms_norm: true
+    #liger_glu_activation: true
+    #liger_layer_norm: true
+    #
+    #liger_fused_linear_cross_entropy: true
+
+datasets:
+  - path: yahma/alpaca-cleaned
+    type: alpaca
+    split: train[:95%]
+output_dir: ./outputs/qat-train_on_inputs/
+dataset_prepared_path: ./outputs/ds_prepared_new_token
+  #sample_packing: true
+
+sequence_len: 8192
+flash_attention: true
+  #flex_attention: true
+  #flex_attn_compile_kwargs:
+  # dynamic: false
+  # mode: max-autotune-no-cudagraphs
+
+qat:
+   activation_dtype: int8
+   weight_dtype: int4
+   group_size: 32
+
+wandb_project: qat_v2
+wandb_entity:
+wandb_watch:
+wandb_name: qat-train-on-inputs
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 16
+num_epochs: 1
+optimizer: adamw_torch_fused
+train_on_inputs: true
+
+  #cosine_constant_lr_ratio: 0
+  #cosine_min_lr_ratio: 1.0
+lr_scheduler: constant
+learning_rate: 2e-5
+save_only_model: true
+bf16: true
+
+resume_from_checkpoint:
+logging_steps: 1
+include_tkps: true
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+  #warmup_ratio: 0.1
+weight_decay: 0.0
+
+fsdp_config:
+  fsdp_version: 2
+  fsdp_offload_params: false
+  fsdp_cpu_ram_efficient_loading: False
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_state_dict_type: FULL_STATE_DICT
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_reshard_after_forward: true
+  fsdp_activation_checkpointing: true
+
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+
+```
+
+</details><br>
+
+# qat-llama-3B
+
+This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the yahma/alpaca-cleaned 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-05
+- train_batch_size: 16
+- eval_batch_size: 16
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- 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: constant
+- lr_scheduler_warmup_steps: 23
+- training_steps: 769
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.55.3
+- Pytorch 2.7.1+cu126
+- Datasets 4.0.0
+- Tokenizers 0.21.4

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128008,
+    128009
+  ],
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.55.3"
+}