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README.md

@@ -1,3 +1,90 @@
 ---
-license: cc
+license: llama2
+library_name: peft
+tags:
+- generated_from_trainer
+base_model: codellama/CodeLlama-13b-hf
+model-index:
+- name: lora-out
+  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)
+# lora-out
+
+This model is a fine-tuned version of [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) on the None dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.4263
+
+## 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: 8
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 2
+- total_train_batch_size: 16
+- total_eval_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 10
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 0.7628        | 0.01  | 1    | 0.7296          |
+| 0.7101        | 0.05  | 7    | 0.6906          |
+| 0.5395        | 0.1   | 14   | 0.5214          |
+| 0.5303        | 0.15  | 21   | 0.4871          |
+| 0.4821        | 0.2   | 28   | 0.4676          |
+| 0.5643        | 0.25  | 35   | 0.4563          |
+| 0.5307        | 0.3   | 42   | 0.4484          |
+| 0.5103        | 0.35  | 49   | 0.4445          |
+| 0.5515        | 0.4   | 56   | 0.4415          |
+| 0.4983        | 0.45  | 63   | 0.4386          |
+| 0.4919        | 0.5   | 70   | 0.4351          |
+| 0.4674        | 0.55  | 77   | 0.4316          |
+| 0.5193        | 0.6   | 84   | 0.4295          |
+| 0.4461        | 0.65  | 91   | 0.4295          |
+| 0.4541        | 0.71  | 98   | 0.4280          |
+| 0.486         | 0.76  | 105  | 0.4280          |
+| 0.4875        | 0.81  | 112  | 0.4269          |
+| 0.5553        | 0.86  | 119  | 0.4266          |
+| 0.4605        | 0.91  | 126  | 0.4260          |
+| 0.4767        | 0.96  | 133  | 0.4263          |
+
+
+### Framework versions
+
+- Transformers 4.36.0.dev0
+- Pytorch 2.0.1+cu118
+- Datasets 2.15.0
+- Tokenizers 0.15.0
+## Training procedure
+
+
+### Framework versions
+
+
+- PEFT 0.6.0

adapter_config.json

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+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "codellama/CodeLlama-13b-hf",
+  "bias": "none",
+  "fan_in_fan_out": null,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "o_proj",
+    "gate_proj",
+    "up_proj",
+    "down_proj",
+    "k_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.bin

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config.json

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+{
+  "_name_or_path": "codellama/CodeLlama-13b-hf",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 13824,
+  "max_position_embeddings": 16384,
+  "model_type": "llama",
+  "num_attention_heads": 40,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 40,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.0.dev0",
+  "use_cache": false,
+  "vocab_size": 32016
+}

config.yml

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+base_model: codellama/CodeLlama-13b-hf
+model_type: LlamaForCausalLM
+tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+bf16: true
+strict: false
+
+datasets:
+  - path: data_filtered.jsonl
+    ds_type: json
+    type:
+      # JSONL file contains question, context, answer fields per line.
+      # This gets mapped to instruction, input, output axolotl tags.
+      field_instruction: instruction
+      #field_input: context
+      field_output: output
+      # Format is used by axolotl to generate the prompt.
+      format: |-
+        Using the instruction context below, generate a typescript code that answers the question and explain it
+        {instruction} 
+
+dataset_prepared_path:
+val_set_size: 16 # must be at least micro_batch_size * N_GPUS, and more if eval packing.
+output_dir: ./lora-out
+
+sequence_len: 4096
+sample_packing: true
+eval_sample_packing: false
+pad_to_sequence_len: true
+
+adapter: lora
+lora_model_dir:
+lora_r: 16
+lora_alpha: 32 # alpha = 2 x rank is a good starting point.
+lora_dropout: 0.05
+lora_target_linear: true # target all linear layers
+lora_fan_in_fan_out:
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_run_id:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 8
+num_epochs: 1
+optimizer: adamw_bnb_8bit
+lr_scheduler: cosine
+learning_rate: 0.0002
+
+train_on_inputs: false
+group_by_length: false
+fp16: false
+tf32: false
+
+gradient_checkpointing: true
+early_stopping_patience:
+resume_from_checkpoint:
+auto_resume_from_checkpoints: true
+local_rank:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 10
+eval_steps: 0.05
+save_steps:
+debug: True
+deepspeed: /root/axolotl/deepspeed/zero3.json
+weight_decay: 0.0
+fsdp:
+fsdp_config:
+special_tokens:
+  bos_token: "<s>"
+  eos_token: "</s>"
+  unk_token: "<unk>"

generation_config.json

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+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "transformers_version": "4.36.0.dev0"
+}

logs.txt

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+    "▁<EOT>"
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+      "content": "▁<EOT>",
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+    "▁<EOT>"
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+  "eot_token": "▁<EOT>",
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