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README.md

@@ -0,0 +1,60 @@
+---
+library_name: transformers
+license: other
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+tags:
+- llama-factory
+- freeze
+- generated_from_trainer
+model-index:
+- name: qwen_nlx_8_1
+  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. -->
+
+# qwen_nlx_8_1
+
+This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the codes_nlx_over81 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: 5e-05
+- train_batch_size: 16
+- eval_batch_size: 8
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 4
+- gradient_accumulation_steps: 8
+- total_train_batch_size: 512
+- total_eval_batch_size: 32
+- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: cosine
+- num_epochs: 1.0
+
+### Training results
+
+
+
+### Framework versions
+
+- Transformers 4.48.2
+- Pytorch 2.5.1+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

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all_results.json

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+    "num_input_tokens_seen": 25165824,
+    "total_flos": 1.0676277147158446e+18,
+    "train_loss": 0.6214325428009033,
+    "train_runtime": 1255.1826,
+    "train_samples_per_second": 4.987,
+    "train_steps_per_second": 0.01
+}

config.json

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+{
+  "_name_or_path": "Qwen/Qwen2.5-Coder-7B-Instruct",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": {
+    "factor": 1.0,
+    "high_freq_factor": 4.0,
+    "low_freq_factor": 1.0,
+    "original_max_position_embeddings": 32768,
+    "rope_type": "llama3"
+  },
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.2",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
+    151645,
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+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.48.2"
+}

llamaboard_config.yaml

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+top.checkpoint_path: null
+top.finetuning_type: freeze
+top.model_name: Qwen2.5-Coder-7B-Instruct
+top.quantization_bit: none
+top.quantization_method: bitsandbytes
+top.rope_scaling: llama3
+top.template: qwen
+train.additional_target: ''
+train.apollo_rank: 256
+train.apollo_scale: 1
+train.apollo_target: all
+train.apollo_update_interval: 200
+train.badam_mode: layer
+train.badam_switch_interval: 50
+train.badam_switch_mode: ascending
+train.badam_update_ratio: 0.05
+train.batch_size: 16
+train.compute_type: bf16
+train.create_new_adapter: false
+train.cutoff_len: 4096
+train.dataset:
+- codes_nlx_over81
+train.dataset_dir: data
+train.ds_offload: false
+train.ds_stage: none
+train.extra_args: '{}'
+train.freeze_extra_modules: ''
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+train.freeze_trainable_modules: all
+train.galore_rank: 16
+train.galore_scale: 2
+train.galore_target: all
+train.galore_update_interval: 200
+train.gradient_accumulation_steps: 8
+train.learning_rate: 5e-5
+train.logging_steps: 1
+train.lora_alpha: 16
+train.lora_dropout: 0
+train.lora_rank: 8
+train.lora_target: ''
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+train.max_grad_norm: '1.0'
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+train.neat_packing: true
+train.neftune_alpha: 0
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+train.packing: true
+train.ppo_score_norm: false
+train.ppo_whiten_rewards: false
+train.pref_beta: 0.1
+train.pref_ftx: 0
+train.pref_loss: sigmoid
+train.report_to:
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+train.resize_vocab: false
+train.reward_model: null
+train.save_steps: 500
+train.swanlab_api_key: ''
+train.swanlab_mode: cloud
+train.swanlab_project: llamafactory
+train.swanlab_run_name: ''
+train.swanlab_workspace: ''
+train.train_on_prompt: false
+train.training_stage: Supervised Fine-Tuning
+train.use_apollo: true
+train.use_badam: false
+train.use_dora: false
+train.use_galore: false
+train.use_llama_pro: false
+train.use_pissa: false
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+[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file special_tokens_map.json from cache at None
+
+[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file tokenizer_config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/tokenizer_config.json
+
+[INFO|2025-05-29 19:59:18] tokenization_utils_base.py:2034 >> loading file chat_template.jinja from cache at None
+
+[INFO|2025-05-29 19:59:19] tokenization_utils_base.py:2304 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+
+[INFO|2025-05-29 19:59:20] configuration_utils.py:696 >> loading configuration file config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/config.json
+
+[INFO|2025-05-29 19:59:20] configuration_utils.py:768 >> Model config Qwen2Config {
+  "_name_or_path": "Qwen/Qwen2.5-Coder-7B-Instruct",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}
+
+
+[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file vocab.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/vocab.json
+
+[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file merges.txt from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/merges.txt
+
+[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file tokenizer.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/tokenizer.json
+
+[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file added_tokens.json from cache at None
+
+[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file special_tokens_map.json from cache at None
+
+[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file tokenizer_config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/tokenizer_config.json
+
+[INFO|2025-05-29 19:59:20] tokenization_utils_base.py:2034 >> loading file chat_template.jinja from cache at None
+
+[INFO|2025-05-29 19:59:21] tokenization_utils_base.py:2304 >> Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
+
+[INFO|2025-05-29 19:59:21] logging.py:157 >> Add <|im_end|> to stop words.
+
+[INFO|2025-05-29 19:59:21] logging.py:157 >> Loading dataset Codes_query_filtered_330k_ns_over8_1.json...
+
+[INFO|2025-05-29 19:59:33] configuration_utils.py:696 >> loading configuration file config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/config.json
+
+[INFO|2025-05-29 19:59:33] configuration_utils.py:768 >> Model config Qwen2Config {
+  "_name_or_path": "Qwen/Qwen2.5-Coder-7B-Instruct",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "max_position_embeddings": 32768,
+  "max_window_layers": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.2",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}
+
+
+[WARNING|2025-05-29 19:59:33] logging.py:162 >> Input length is smaller than max length. Consider increase input length.
+
+[INFO|2025-05-29 19:59:33] logging.py:157 >> Using llama3 scaling strategy and setting scaling factor to 1.0.
+
+[INFO|2025-05-29 19:59:33] logging.py:157 >> Using block diagonal attention for sequence packing without cross-attention.
+
+[INFO|2025-05-29 19:59:33] logging.py:157 >> Liger kernel has been applied to the model.
+
+[INFO|2025-05-29 19:59:33] modeling_utils.py:3904 >> loading weights file model.safetensors from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/model.safetensors.index.json
+
+[INFO|2025-05-29 19:59:33] modeling_utils.py:1582 >> Instantiating Qwen2ForCausalLM model under default dtype torch.bfloat16.
+
+[INFO|2025-05-29 19:59:33] configuration_utils.py:1140 >> Generate config GenerationConfig {
+  "bos_token_id": 151643,
+  "eos_token_id": 151645
+}
+
+
+[INFO|2025-05-29 19:59:37] modeling_utils.py:4888 >> All model checkpoint weights were used when initializing Qwen2ForCausalLM.
+
+
+[INFO|2025-05-29 19:59:37] modeling_utils.py:4896 >> All the weights of Qwen2ForCausalLM were initialized from the model checkpoint at Qwen/Qwen2.5-Coder-7B-Instruct.
+If your task is similar to the task the model of the checkpoint was trained on, you can already use Qwen2ForCausalLM for predictions without further training.
+
+[INFO|2025-05-29 19:59:37] configuration_utils.py:1095 >> loading configuration file generation_config.json from cache at /home/kiho/.cache/huggingface/hub/models--Qwen--Qwen2.5-Coder-7B-Instruct/snapshots/c03e6d358207e414f1eca0bb1891e29f1db0e242/generation_config.json
+
+[INFO|2025-05-29 19:59:37] configuration_utils.py:1140 >> Generate config GenerationConfig {
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.1,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8
+}
+
+
+[INFO|2025-05-29 19:59:37] logging.py:157 >> Gradient checkpointing enabled.
+
+[INFO|2025-05-29 19:59:37] logging.py:157 >> Using torch SDPA for faster training and inference.
+
+[INFO|2025-05-29 19:59:37] logging.py:157 >> Upcasting trainable params to float32.
+
+[INFO|2025-05-29 19:59:37] logging.py:157 >> Fine-tuning method: Freeze
+
+[INFO|2025-05-29 19:59:37] logging.py:157 >> Set trainable layers: .26.,.27.
+
+[INFO|2025-05-29 19:59:37] logging.py:157 >> trainable params: 466,115,584 || all params: 7,615,616,512 || trainable%: 6.1205
+
+[INFO|2025-05-29 19:59:37] trainer.py:741 >> Using auto half precision backend
+
+[INFO|2025-05-29 19:59:37] logging.py:157 >> Found linear modules: q_proj,gate_proj,up_proj,o_proj,v_proj,k_proj,down_proj
+
+[INFO|2025-05-29 19:59:37] logging.py:157 >> Using APOLLO optimizer with args: {'rank': 256, 'proj': 'random', 'proj_type': 'std', 'update_proj_gap': 200, 'scale': 1, 'scale_type': 'channel', 'scale_front': False}.
+
+[INFO|2025-05-29 19:59:38] trainer.py:2369 >> ***** Running training *****
+
+[INFO|2025-05-29 19:59:38] trainer.py:2370 >>   Num examples = 6,260
+
+[INFO|2025-05-29 19:59:38] trainer.py:2371 >>   Num Epochs = 1
+
+[INFO|2025-05-29 19:59:38] trainer.py:2372 >>   Instantaneous batch size per device = 16
+
+[INFO|2025-05-29 19:59:38] trainer.py:2375 >>   Total train batch size (w. parallel, distributed & accumulation) = 512
+
+[INFO|2025-05-29 19:59:38] trainer.py:2376 >>   Gradient Accumulation steps = 8
+
+[INFO|2025-05-29 19:59:38] trainer.py:2377 >>   Total optimization steps = 12
+
+[INFO|2025-05-29 19:59:38] trainer.py:2378 >>   Number of trainable parameters = 466,115,584
+
+[INFO|2025-05-29 20:01:27] logging.py:157 >> {'loss': 0.6759, 'learning_rate': 4.9148e-05, 'epoch': 0.08, 'throughput': 19284.83}
+
+[INFO|2025-05-29 20:03:09] logging.py:157 >> {'loss': 0.6698, 'learning_rate': 4.6651e-05, 'epoch': 0.16, 'throughput': 19910.87}
+
+[INFO|2025-05-29 20:04:51] logging.py:157 >> {'loss': 0.6228, 'learning_rate': 4.2678e-05, 'epoch': 0.24, 'throughput': 20123.38}
+
+[INFO|2025-05-29 20:06:33] logging.py:157 >> {'loss': 0.6228, 'learning_rate': 3.7500e-05, 'epoch': 0.33, 'throughput': 20244.72}
+
+[INFO|2025-05-29 20:08:15] logging.py:157 >> {'loss': 0.6104, 'learning_rate': 3.1470e-05, 'epoch': 0.41, 'throughput': 20320.48}
+
+[INFO|2025-05-29 20:09:57] logging.py:157 >> {'loss': 0.6063, 'learning_rate': 2.5000e-05, 'epoch': 0.49, 'throughput': 20365.11}
+
+[INFO|2025-05-29 20:11:38] logging.py:157 >> {'loss': 0.6236, 'learning_rate': 1.8530e-05, 'epoch': 0.57, 'throughput': 20397.20}
+
+[INFO|2025-05-29 20:13:20] logging.py:157 >> {'loss': 0.6225, 'learning_rate': 1.2500e-05, 'epoch': 0.65, 'throughput': 20419.47}
+
+[INFO|2025-05-29 20:15:02] logging.py:157 >> {'loss': 0.5952, 'learning_rate': 7.3223e-06, 'epoch': 0.73, 'throughput': 20439.93}
+
+[INFO|2025-05-29 20:16:45] logging.py:157 >> {'loss': 0.6016, 'learning_rate': 3.3494e-06, 'epoch': 0.82, 'throughput': 20429.94}
+
+[INFO|2025-05-29 20:18:27] logging.py:157 >> {'loss': 0.5926, 'learning_rate': 8.5185e-07, 'epoch': 0.90, 'throughput': 20439.47}
+
+[INFO|2025-05-29 20:20:09] logging.py:157 >> {'loss': 0.6137, 'learning_rate': 0.0000e+00, 'epoch': 0.98, 'throughput': 20449.96}
+
+[INFO|2025-05-29 20:20:09] trainer.py:3910 >> Saving model checkpoint to saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12
+
+[INFO|2025-05-29 20:20:09] configuration_utils.py:420 >> Configuration saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/config.json
+
+[INFO|2025-05-29 20:20:09] configuration_utils.py:909 >> Configuration saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/generation_config.json
+
+[INFO|2025-05-29 20:20:32] modeling_utils.py:2996 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/model.safetensors.index.json.
+
+[INFO|2025-05-29 20:20:32] tokenization_utils_base.py:2491 >> tokenizer config file saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/tokenizer_config.json
+
+[INFO|2025-05-29 20:20:32] tokenization_utils_base.py:2500 >> Special tokens file saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/checkpoint-12/special_tokens_map.json
+
+[INFO|2025-05-29 20:20:33] trainer.py:2643 >> 
+
+Training completed. Do not forget to share your model on huggingface.co/models =)
+
+
+
+[INFO|2025-05-29 20:20:33] trainer.py:3910 >> Saving model checkpoint to saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1
+
+[INFO|2025-05-29 20:20:33] configuration_utils.py:420 >> Configuration saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/config.json
+
+[INFO|2025-05-29 20:20:33] configuration_utils.py:909 >> Configuration saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/generation_config.json
+
+[INFO|2025-05-29 20:20:56] modeling_utils.py:2996 >> The model is bigger than the maximum size per checkpoint (5GB) and is going to be split in 4 checkpoint shards. You can find where each parameters has been saved in the index located at saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/model.safetensors.index.json.
+
+[INFO|2025-05-29 20:20:56] tokenization_utils_base.py:2491 >> tokenizer config file saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/tokenizer_config.json
+
+[INFO|2025-05-29 20:20:56] tokenization_utils_base.py:2500 >> Special tokens file saved in saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1/special_tokens_map.json
+
+[WARNING|2025-05-29 20:20:56] logging.py:162 >> No metric eval_loss to plot.
+
+[WARNING|2025-05-29 20:20:56] logging.py:162 >> No metric eval_accuracy to plot.
+
+[INFO|2025-05-29 20:20:56] modelcard.py:449 >> Dropping the following result as it does not have all the necessary fields:
+{'task': {'name': 'Causal Language Modeling', 'type': 'text-generation'}}
+

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
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+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
+size 11421896

tokenizer_config.json

@@ -0,0 +1,209 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+      "rstrip": false,
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+      "special": true
+    },
+    "151645": {
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+      "lstrip": false,
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+}

train_results.json

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training_args.yaml

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+apollo_rank: 256
+apollo_scale: 1
+apollo_target: all
+apollo_update_interval: 200
+bf16: true
+cutoff_len: 4096
+dataset: codes_nlx_over81
+dataset_dir: data
+ddp_timeout: 180000000
+do_train: true
+enable_liger_kernel: true
+finetuning_type: freeze
+flash_attn: auto
+freeze_trainable_layers: 2
+freeze_trainable_modules: all
+gradient_accumulation_steps: 8
+include_num_input_tokens_seen: true
+learning_rate: 5.0e-05
+logging_steps: 1
+lr_scheduler_type: cosine
+max_grad_norm: 1.0
+max_samples: 50000000
+model_name_or_path: Qwen/Qwen2.5-Coder-7B-Instruct
+neat_packing: true
+num_train_epochs: 1.0
+output_dir: saves/Qwen2.5-Coder-7B-Instruct/freeze/qwen_nlx_8_1
+packing: true
+per_device_train_batch_size: 16
+plot_loss: true
+preprocessing_num_workers: 16
+report_to: none
+rope_scaling: llama3
+save_steps: 500
+stage: sft
+template: qwen
+trust_remote_code: true
+use_apollo: true
+warmup_steps: 0