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Model: shisa-ai/ablation-54-nemojp-shisa-v2-mistral-nemo-japanese-12b
Source: Original Platform

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README.md

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+---
+library_name: transformers
+license: apache-2.0
+base_model: cyberagent/Mistral-Nemo-Japanese-Instruct-2408
+tags:
+- generated_from_trainer
+datasets:
+- shisa-ai/shisa-v1-athenev2-reannotated-filtered
+- shisa-ai/shisa-v2-roleplaying
+model-index:
+- name: outputs/ablation-54-nemojp-shisa-v2-mistral-nemo-japanese-12b
+  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: `0.8.0.dev0`
+```yaml
+# train w/ shisa-ai/shisa-v1-athenev2-reannotated-filtered
+
+base_model: cyberagent/Mistral-Nemo-Japanese-Instruct-2408
+tokenizer_type: AutoTokenizer
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+# User Liger
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_fused_linear_cross_entropy: true
+
+chat_template: tokenizer_default
+datasets:
+  - path: shisa-ai/shisa-v1-athenev2-reannotated-filtered
+    # type: sharegpt deprecated
+    type: chat_template
+    field_messages: conversations
+    message_field_role: from
+    message_field_content: value
+  - path: shisa-ai/shisa-v2-roleplaying
+    type: chat_template
+    field_messages: conversations
+    message_property_mappings:
+      role: role
+      content: content
+    roles:
+      system:
+        - system
+      assistant:
+        - gpt
+        - model
+        - assistant
+      user:
+        - human
+        - user
+    roles_to_train: ["assistant"]
+    
+dataset_prepared_path: last_run_prepared
+val_set_size: 0.05
+output_dir: ./outputs/ablation-54-nemojp-shisa-v2-mistral-nemo-japanese-12b
+
+sequence_len: 8192
+sample_packing: true
+pad_to_sequence_len: true
+
+# marginal difference
+neftune_noise_alpha: 5
+
+use_wandb: true
+wandb_project: shisa-v2
+wandb_entity: augmxnt
+wandb_name: ablation-54-nemojp-shisa-v2-mistral-nemo-japanese-12b
+
+gradient_accumulation_steps: 4
+micro_batch_size: 2
+num_epochs: 3
+optimizer: paged_adamw_8bit
+lr_scheduler: linear
+learning_rate: 8e-6
+
+train_on_inputs: false
+group_by_length: false
+bf16: auto
+fp16:
+tf32: false
+
+gradient_checkpointing: true
+gradient_checkpointing_kwargs:
+  use_reentrant: false
+early_stopping_patience:
+resume_from_checkpoint:
+logging_steps: 1
+xformers_attention:
+flash_attention: true
+
+warmup_steps: 100
+evals_per_epoch: 2
+eval_table_size:
+saves_per_epoch: 0
+save_total_limit: 1 # Only store a single checkpoint
+debug:
+deepspeed: zero3_bf16.json
+weight_decay: 1e-4
+fsdp:
+fsdp_config:
+special_tokens:
+  pad_token: "<pad>"
+
+```
+
+</details><br>
+
+# outputs/ablation-54-nemojp-shisa-v2-mistral-nemo-japanese-12b
+
+This model is a fine-tuned version of [cyberagent/Mistral-Nemo-Japanese-Instruct-2408](https://huggingface.co/cyberagent/Mistral-Nemo-Japanese-Instruct-2408) on the shisa-ai/shisa-v1-athenev2-reannotated-filtered and the shisa-ai/shisa-v2-roleplaying datasets.
+It achieves the following results on the evaluation set:
+- Loss: 0.5718
+
+## 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: 8e-06
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- total_eval_batch_size: 16
+- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 100
+- num_epochs: 3.0
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 0.7482        | 0.0043 | 1    | 0.7689          |
+| 0.5451        | 0.5005 | 116  | 0.5419          |
+| 0.3819        | 1.0    | 232  | 0.5195          |
+| 0.3836        | 1.5005 | 348  | 0.5304          |
+| 0.2826        | 2.0    | 464  | 0.5222          |
+| 0.2685        | 2.5005 | 580  | 0.5718          |
+
+
+### Framework versions
+
+- Transformers 4.49.0
+- Pytorch 2.6.0+cu124
+- Datasets 3.2.0
+- Tokenizers 0.21.0

config.json

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+{
+  "_name_or_path": "cyberagent/Mistral-Nemo-Japanese-Instruct-2408",
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 131072,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 5120,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 128000,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 40,
+  "num_key_value_heads": 8,
+  "pad_token_id": 10,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 1000000.0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.49.0",
+  "use_cache": false,
+  "vocab_size": 131074
+}

configuration.json

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+{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 131072,
+  "pad_token_id": 10,
+  "transformers_version": "4.49.0"
+}

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