cgifbribcgfbi
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Llama-3.3-70B-chem-gpt-4-1-div

@@ -1,202 +1,148 @@
 ---
-base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
 library_name: peft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-## Uses
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-### Training Data
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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 ### Framework versions
-- PEFT 0.15.2

 ---
 library_name: peft
+license: llama3.3
+base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
+tags:
+- axolotl
+- generated_from_trainer
+datasets:
+- gpt-4-1-diverse_5000.jsonl
+model-index:
+- name: Llama-3.3-70B-chem-gpt-4-1-div
+  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: `0.9.2`
+```yaml
+base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
+load_in_8bit: false
+load_in_4bit: true
+adapter: qlora
+wandb_name: Llama-3.3-70B-chem-gpt-4-1-div
+output_dir: ./outputs/out/Llama-3.3-70B-chem-gpt-4-1-div
+hub_model_id: cgifbribcgfbi/Llama-3.3-70B-chem-gpt-4-1-div
+tokenizer_type: AutoTokenizer
+push_dataset_to_hub:
+strict: false
+datasets:
+  - path: gpt-4-1-diverse_5000.jsonl
+    type: chat_template
+    field_messages: messages
+dataset_prepared_path: last_run_prepared
+# val_set_size: 0.05
+# eval_sample_packing: False
+save_safetensors: true
+sequence_len: 6800
+sample_packing: true
+pad_to_sequence_len: true
+lora_r: 64
+lora_alpha: 32
+lora_dropout: 0.05
+lora_target_modules:
+lora_target_linear: true
+wandb_mode:
+wandb_project: finetune-sweep
+wandb_entity: gpoisjgqetpadsfke
+wandb_watch:
+wandb_run_id:
+wandb_log_model:
+gradient_accumulation_steps: 1
+micro_batch_size: 4  # This will be automatically adjusted based on available GPU memory
+num_epochs: 4
+optimizer: adamw_torch_fused
+lr_scheduler: cosine
+learning_rate: 0.00002
+train_on_inputs: false
+group_by_length: true
+bf16: true
+tf32: true
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: true
+logging_steps: 1
+flash_attention: true
+warmup_steps: 10
+evals_per_epoch: 3
+saves_per_epoch: 1
+weight_decay: 0.01
+fsdp:
+  - full_shard
+  - auto_wrap
+fsdp_config:
+  fsdp_limit_all_gathers: true
+  fsdp_sync_module_states: true
+  fsdp_offload_params: false
+  fsdp_use_orig_params: false
+  fsdp_cpu_ram_efficient_loading: true
+  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
+special_tokens:
+  pad_token: <|finetune_right_pad_id|>
+```
+</details><br>
+# Llama-3.3-70B-chem-gpt-4-1-div
+This model is a fine-tuned version of [huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned](https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned) on the gpt-4-1-diverse_5000.jsonl 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: 4
+- eval_batch_size: 4
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- total_train_batch_size: 16
+- total_eval_batch_size: 16
+- 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: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 4.0
+### Training results
 ### Framework versions
+- PEFT 0.15.2
+- Transformers 4.51.3
+- Pytorch 2.6.0+cu124
+- Datasets 3.5.1
+- Tokenizers 0.21.1