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README.md

@@ -2,33 +2,36 @@
 license: llama2
 library_name: peft
 tags:
-- typescript
-- instruction-tuning
-- code-generation
-- lora
-- peft
-base_model: codellama/CodeLlama-13b-hf
+- generated_from_trainer
+base_model: codellama/CodeLlama-13b-Instruct-hf
 model-index:
 - name: lora-out
   results: []
-datasets:
-- mhhmm/typescript-instruct-20k
-language:
-- en
-metrics:
-- code_eval
-pipeline_tag: text-generation
 ---
 
-## Architecture
+<!-- 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. -->
 
-![The Architecture](https://github.com/LeVuMinhHuy/brocode/blob/master/.pics/about-the-model.png?raw=true)
+[<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
 
-## About
-
-This model is a fine-tuned version of [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf).
+This model is a fine-tuned version of [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) on the None dataset.
 It achieves the following results on the evaluation set:
-- Loss: 0.4268
+- Loss: 0.4198
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
 
 ### Training hyperparameters
 
@@ -50,27 +53,27 @@ The following hyperparameters were used during training:
 
 | Training Loss | Epoch | Step | Validation Loss |
 |:-------------:|:-----:|:----:|:---------------:|
-| 0.7555        | 0.01  | 1    | 0.7062          |
-| 0.7036        | 0.05  | 7    | 0.6673          |
-| 0.5422        | 0.1   | 14   | 0.5152          |
-| 0.5351        | 0.15  | 21   | 0.4866          |
-| 0.495         | 0.2   | 28   | 0.4688          |
-| 0.5651        | 0.25  | 35   | 0.4587          |
-| 0.5146        | 0.3   | 42   | 0.4486          |
-| 0.4955        | 0.35  | 49   | 0.4469          |
-| 0.5117        | 0.4   | 56   | 0.4432          |
-| 0.5245        | 0.45  | 63   | 0.4410          |
-| 0.5003        | 0.5   | 70   | 0.4371          |
-| 0.4502        | 0.55  | 77   | 0.4340          |
-| 0.527         | 0.6   | 84   | 0.4315          |
-| 0.48          | 0.65  | 91   | 0.4305          |
-| 0.448         | 0.7   | 98   | 0.4289          |
-| 0.5427        | 0.75  | 105  | 0.4289          |
-| 0.4715        | 0.8   | 112  | 0.4279          |
-| 0.5584        | 0.85  | 119  | 0.4276          |
-| 0.4936        | 0.9   | 126  | 0.4267          |
-| 0.4788        | 0.95  | 133  | 0.4268          |
-| 0.476         | 1.0   | 140  | 0.4268          |
+| 0.6555        | 0.01  | 1    | 0.6550          |
+| 0.6532        | 0.05  | 7    | 0.6035          |
+| 0.5278        | 0.1   | 14   | 0.4977          |
+| 0.5466        | 0.15  | 21   | 0.4736          |
+| 0.4832        | 0.2   | 28   | 0.4637          |
+| 0.5069        | 0.25  | 35   | 0.4492          |
+| 0.4864        | 0.3   | 42   | 0.4436          |
+| 0.4625        | 0.35  | 49   | 0.4379          |
+| 0.4792        | 0.4   | 56   | 0.4336          |
+| 0.4608        | 0.45  | 63   | 0.4302          |
+| 0.4738        | 0.5   | 70   | 0.4266          |
+| 0.4839        | 0.55  | 77   | 0.4245          |
+| 0.4791        | 0.6   | 84   | 0.4227          |
+| 0.4701        | 0.65  | 91   | 0.4233          |
+| 0.4612        | 0.7   | 98   | 0.4225          |
+| 0.4419        | 0.75  | 105  | 0.4212          |
+| 0.4705        | 0.8   | 112  | 0.4199          |
+| 0.4422        | 0.85  | 119  | 0.4198          |
+| 0.4889        | 0.9   | 126  | 0.4198          |
+| 0.4914        | 0.95  | 133  | 0.4195          |
+| 0.4799        | 1.0   | 140  | 0.4198          |
 
 
 ### Framework versions
@@ -79,33 +82,10 @@ The following hyperparameters were used during training:
 - Pytorch 2.0.1+cu118
 - Datasets 2.15.0
 - Tokenizers 0.15.0
-- PEFT 0.6.0
-  
-### Evaluation
-
-I'm using MultiPL-E benchmark, the same as Code Llmama using in their paper
-
-
-| Modal                                   | Pass@k | Estimate | Num problems  |
-|-----------------------------------------|--------|----------|---------------|
-| Code LLama - Instruct 13B               | 1      | 39.0%    | 159           |
-| Our  13B                                | 1      | 42.4%    | 159           |
+## Training procedure
 
-How to reproduce my evaluation? Just run like the offical document of MultiPL-E: https://nuprl.github.io/MultiPL-E/tutorial.html, change the modal name by my model here: `mhhmm/typescript-instruct-20k-v2` 
 
-This is the code that I ran with Google Colab (using A100 40GB, yes, it requires that much GPU RAM)
-
-If you even have a stronger GPU, increase the --batch-size, or --completion-limit
+### Framework versions
 
-```
-!pip install --upgrade pip
-!pip install aiohttp numpy tqdm pytest datasets torch transformers sentencepiece
-!git clone https://github.com/nuprl/MultiPL-E
-%cd MultiPL-E
-!mkdir typescript
-!python3 automodel.py --name mhhmm/typescript-instruct-20k-v2 --root-dataset humaneval --lang ts --temperature 0.2 --batch-size 10 --completion-limit 20 --output-dir-prefix typescript
-%cd evaluation/src
-!python3 main.py --dir ../../typescript --output-dir ../../typescript --recursive
-!python3 pass_k.py ./typescript/*
-```
 
+- PEFT 0.6.0

adapter_config.json

@@ -0,0 +1,28 @@
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+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "codellama/CodeLlama-13b-Instruct-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"
+}

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config.json

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+{
+  "_name_or_path": "codellama/CodeLlama-13b-Instruct-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,
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+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.0.dev0",
+  "use_cache": false,
+  "vocab_size": 32016
+}

config.yml

@@ -0,0 +1,77 @@
+base_model: codellama/CodeLlama-13b-Instruct-hf
+model_type: LlamaForCausalLM
+tokenizer_type: CodeLlamaTokenizer
+is_llama_derived_model: true
+
+load_in_8bit: false
+bf16: true
+strict: false
+
+datasets:
+  - path: data_removed_logs.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"
+}

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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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