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Model: cfierro/llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5 Source: Original Platform
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---
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library_name: transformers
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license: llama3.1
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base_model: meta-llama/Llama-3.1-8B-Instruct
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tags:
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- axolotl
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- generated_from_trainer
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datasets:
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- cfierro/othello-snake-llama3-multi-format
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- cfierro/othello-snake-llama3-multi-format
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- cfierro/othello-snake-llama3-multi-format
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- cfierro/othello-snake-llama3-multi-format
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- cfierro/tulu3-sft-replay
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model-index:
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- name: llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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[<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)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.10.0`
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```yaml
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base_model: meta-llama/Llama-3.1-8B-Instruct
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model_type: LlamaForCausalLM
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tokenizer_type: AutoTokenizer
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load_in_8bit: false
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load_in_4bit: false
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# ============================================================================
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# Full FT of the Othello-snake task WITH a chat-data replay mix, to fight the
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# catastrophic forgetting of instruction-following seen in the game-only run
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# (the model collapsed to emitting only game moves). Two data sources are mixed:
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# 1. The game, as a raw `input_output` continuation document (masked prefix),
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# here in the MULTI-FORMAT variant: ~70% space-separated moves plus ~5% each
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# of several alternative notations (Player 1/2, black/white, turn N, comma,
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# "(black)/(white)"), so the model is robust to how a game is written down.
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# 2. A general chat replay set (Tulu-3 subset) as `type: chat_template`.
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# The Llama-3 role tokens on the chat data teach the model a context switch:
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# game-like raw context -> moves; chat prompt with an instruction -> language.
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# Replay built by data/push_replay_subset.py at a ~1:1 trained-token ratio vs the
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# game (full-FT forgets hard, so a generous chat share).
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# ============================================================================
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chat_template: llama3
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datasets:
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# --- Game shards (template-free, loss-masked continuation, multi-format) ---
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# The first shard is sliced to hold out its last 500 games for a game-ONLY
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# validation set (see test_datasets below). The other three shards are full.
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- path: cfierro/othello-snake-llama3-multi-format
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type: input_output
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split: games_0_5k[:-500]
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- path: cfierro/othello-snake-llama3-multi-format
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type: input_output
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split: games_5k_10k
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- path: cfierro/othello-snake-llama3-multi-format
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type: input_output
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split: games_10k_25k
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- path: cfierro/othello-snake-llama3-multi-format
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type: input_output
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split: games_25k_50k
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# --- Replay: general chat data (Llama-3 chat-template formatted) ---
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- path: cfierro/tulu3-sft-replay
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type: chat_template
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field_messages: messages
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split: train
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train_on_inputs: false # game: segment labels apply; chat: mask user, train assistant
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dataset_prepared_path: /scratch/project/eu-26-55/knowledge-ft/axolotl/datasets/llama-3.1-8b/othello-snake-multi-format-replay
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output_dir: /scratch/project/eu-26-55/knowledge-ft/axolotl/models/llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5
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sequence_len: 1024 # games are short; replay was filtered to <=1024 tokens
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sample_packing: true # pack many short docs per sequence (block-diagonal attn)
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eval_sample_packing: false
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# --- Validation loss on GAME DATA ONLY ---
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# val_set_size: 0 disables the default carve (which would mix game + chat into the
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# val set). test_datasets points eval exclusively at a held-out slice of game data,
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# so eval_loss == game performance and drives load_best_model_at_end below.
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# NOTE: the split-slicing syntax (`[:-500]` / `[-500:]`) requires your Axolotl to
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# forward `split` to load_dataset (verify once with `axolotl preprocess ... --debug`).
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# Fallback if slicing is unsupported in 0.10.0: hold out a WHOLE shard instead --
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# drop `games_25k_50k` from datasets above and use it (full) as the test_dataset.
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val_set_size: 0
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test_datasets:
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- path: cfierro/othello-snake-llama3-multi-format
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type: input_output
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split: games_0_5k[-500:]
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wandb_project: othello-snake-ft
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wandb_entity: cfierro
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wandb_watch:
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wandb_name: llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5
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wandb_log_model: "false"
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# --- Multi-GPU: 4x A40 (48GB) per node ---
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# Full FT of an 8B model needs ZeRO-3 to shard weights+grads+optimizer.
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# Effective batch = 4 GPUs * micro 4 * grad_accum 1 = 16 packed sequences.
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# Adding the ~1:1 replay roughly doubles the token count vs the game-only run, so
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# expect ~2x the steps/epoch. Recompute exact steps with `axolotl preprocess`.
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gradient_accumulation_steps: 1
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micro_batch_size: 4
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num_epochs: 2 # lowered from 3: replay + fewer epochs both reduce forgetting
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save_strategy: epoch
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eval_strategy: epoch # must match save_strategy for load_best_model_at_end
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# --- Auto-upload the BEST (lowest game eval_loss) checkpoint to the Hub ---
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# eval_loss here is GAME-ONLY (see test_datasets), so "best" is selected on game
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# performance, not on how chatty the model is. Needs a write-scoped HF token on the
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# cluster (HF_TOKEN or `huggingface-cli login`).
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load_best_model_at_end: true
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metric_for_best_model: eval_loss
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greater_is_better: false
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hub_model_id: cfierro/llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5
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hub_strategy: end
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hub_private_repo: false
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optimizer: adamw_bnb_8bit
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lr_scheduler: cosine
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learning_rate: 1e-5 # lowered from 2e-5 to reduce drift away from the base model
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bf16: auto
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tf32: false
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gradient_checkpointing: true
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resume_from_checkpoint:
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logging_steps: 1
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flash_attention: true
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warmup_ratio: 0.03
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save_total_limit: 3 # keep all per-epoch checkpoints; best is always retained
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weight_decay: 0.0
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special_tokens:
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pad_token: <|end_of_text|>
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# DeepSpeed ZeRO Stage 3 — shards weights, gradients, and optimizer across GPUs
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deepspeed: deepspeed_configs/zero3.json
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# Verify the data mix + loss mask before training:
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# axolotl preprocess axolotl_configs/fullft-othello-snake-8b-multi-format-replay.yaml --debug
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# Confirm: game rows show the masked prefix (-100) + trained moves/<|end_of_text|>;
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# chat rows show masked user/system turns + trained assistant turns; and the
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# heterogeneous input_output + chat_template mix concatenates without error.
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```
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</details><br>
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# llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5
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This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the cfierro/othello-snake-llama3-multi-format, the cfierro/othello-snake-llama3-multi-format, the cfierro/othello-snake-llama3-multi-format, the cfierro/othello-snake-llama3-multi-format and the cfierro/tulu3-sft-replay datasets.
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It achieves the following results on the evaluation set:
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- Loss: 0.2137
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- total_train_batch_size: 16
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- total_eval_batch_size: 16
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- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 54
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- training_steps: 1824
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:-----:|:----:|:---------------:|
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| No log | 0 | 0 | 1.1501 |
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| 0.5652 | 1.0 | 912 | 0.2181 |
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| 0.3346 | 2.0 | 1824 | 0.2137 |
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### Framework versions
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- Transformers 4.57.3
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- Pytorch 2.9.0+cu128
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- Datasets 3.5.0
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- Tokenizers 0.22.2
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chat_template.jinja
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{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>
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'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>
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' }}{% endif %}
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"dtype": "bfloat16",
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"eos_token_id": 128009,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"transformers_version": "4.57.3",
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"use_cache": false,
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"vocab_size": 128256
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}
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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.57.3"
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}
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||||
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||||
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
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||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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|
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||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4b0c66e39f26cbf7432cd42a19fda1764c43fb14a52cb1508e3ef1dc9d4d5fe6
|
||||
size 9745
|
||||
Reference in New Issue
Block a user