From a399cd551f50aa1cafd1d1c2192b3c9e926e14b0 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Thu, 10 Sep 2026 05:49:17 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: cfierro/llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5 Source: Original Platform --- .gitattributes | 36 + README.md | 201 +++ chat_template.jinja | 5 + config.json | 35 + generation_config.json | 12 + model-00001-of-00004.safetensors | 3 + model-00002-of-00004.safetensors | 3 + model-00003-of-00004.safetensors | 3 + model-00004-of-00004.safetensors | 3 + model.safetensors.index.json | 299 +++++ special_tokens_map.json | 23 + tokenizer.json | 3 + tokenizer_config.json | 2063 ++++++++++++++++++++++++++++++ training_args.bin | 3 + 14 files changed, 2692 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00004.safetensors create mode 100644 model-00002-of-00004.safetensors create mode 100644 model-00003-of-00004.safetensors create mode 100644 model-00004-of-00004.safetensors create mode 100644 model.safetensors.index.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 training_args.bin diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..52373fe --- /dev/null +++ b/.gitattributes @@ -0,0 +1,36 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +tokenizer.json filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..570a83a --- /dev/null +++ b/README.md @@ -0,0 +1,201 @@ +--- +library_name: transformers +license: llama3.1 +base_model: meta-llama/Llama-3.1-8B-Instruct +tags: +- axolotl +- generated_from_trainer +datasets: +- cfierro/othello-snake-llama3-multi-format +- cfierro/othello-snake-llama3-multi-format +- cfierro/othello-snake-llama3-multi-format +- cfierro/othello-snake-llama3-multi-format +- cfierro/tulu3-sft-replay +model-index: +- name: llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5 + results: [] +--- + + + +[Built with Axolotl](https://github.com/axolotl-ai-cloud/axolotl) +
See axolotl config + +axolotl version: `0.10.0` +```yaml +base_model: meta-llama/Llama-3.1-8B-Instruct +model_type: LlamaForCausalLM +tokenizer_type: AutoTokenizer + +load_in_8bit: false +load_in_4bit: false + +# ============================================================================ +# Full FT of the Othello-snake task WITH a chat-data replay mix, to fight the +# catastrophic forgetting of instruction-following seen in the game-only run +# (the model collapsed to emitting only game moves). Two data sources are mixed: +# 1. The game, as a raw `input_output` continuation document (masked prefix), +# here in the MULTI-FORMAT variant: ~70% space-separated moves plus ~5% each +# of several alternative notations (Player 1/2, black/white, turn N, comma, +# "(black)/(white)"), so the model is robust to how a game is written down. +# 2. A general chat replay set (Tulu-3 subset) as `type: chat_template`. +# The Llama-3 role tokens on the chat data teach the model a context switch: +# game-like raw context -> moves; chat prompt with an instruction -> language. +# Replay built by data/push_replay_subset.py at a ~1:1 trained-token ratio vs the +# game (full-FT forgets hard, so a generous chat share). +# ============================================================================ +chat_template: llama3 +datasets: + # --- Game shards (template-free, loss-masked continuation, multi-format) --- + # The first shard is sliced to hold out its last 500 games for a game-ONLY + # validation set (see test_datasets below). The other three shards are full. + - path: cfierro/othello-snake-llama3-multi-format + type: input_output + split: games_0_5k[:-500] + - path: cfierro/othello-snake-llama3-multi-format + type: input_output + split: games_5k_10k + - path: cfierro/othello-snake-llama3-multi-format + type: input_output + split: games_10k_25k + - path: cfierro/othello-snake-llama3-multi-format + type: input_output + split: games_25k_50k + # --- Replay: general chat data (Llama-3 chat-template formatted) --- + - path: cfierro/tulu3-sft-replay + type: chat_template + field_messages: messages + split: train +train_on_inputs: false # game: segment labels apply; chat: mask user, train assistant +dataset_prepared_path: /scratch/project/eu-26-55/knowledge-ft/axolotl/datasets/llama-3.1-8b/othello-snake-multi-format-replay +output_dir: /scratch/project/eu-26-55/knowledge-ft/axolotl/models/llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5 + +sequence_len: 1024 # games are short; replay was filtered to <=1024 tokens +sample_packing: true # pack many short docs per sequence (block-diagonal attn) +eval_sample_packing: false + +# --- Validation loss on GAME DATA ONLY --- +# val_set_size: 0 disables the default carve (which would mix game + chat into the +# val set). test_datasets points eval exclusively at a held-out slice of game data, +# so eval_loss == game performance and drives load_best_model_at_end below. +# NOTE: the split-slicing syntax (`[:-500]` / `[-500:]`) requires your Axolotl to +# forward `split` to load_dataset (verify once with `axolotl preprocess ... --debug`). +# Fallback if slicing is unsupported in 0.10.0: hold out a WHOLE shard instead -- +# drop `games_25k_50k` from datasets above and use it (full) as the test_dataset. +val_set_size: 0 +test_datasets: + - path: cfierro/othello-snake-llama3-multi-format + type: input_output + split: games_0_5k[-500:] + +wandb_project: othello-snake-ft +wandb_entity: cfierro +wandb_watch: +wandb_name: llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5 +wandb_log_model: "false" + +# --- Multi-GPU: 4x A40 (48GB) per node --- +# Full FT of an 8B model needs ZeRO-3 to shard weights+grads+optimizer. +# Effective batch = 4 GPUs * micro 4 * grad_accum 1 = 16 packed sequences. +# Adding the ~1:1 replay roughly doubles the token count vs the game-only run, so +# expect ~2x the steps/epoch. Recompute exact steps with `axolotl preprocess`. +gradient_accumulation_steps: 1 +micro_batch_size: 4 +num_epochs: 2 # lowered from 3: replay + fewer epochs both reduce forgetting +save_strategy: epoch +eval_strategy: epoch # must match save_strategy for load_best_model_at_end + +# --- Auto-upload the BEST (lowest game eval_loss) checkpoint to the Hub --- +# eval_loss here is GAME-ONLY (see test_datasets), so "best" is selected on game +# performance, not on how chatty the model is. Needs a write-scoped HF token on the +# cluster (HF_TOKEN or `huggingface-cli login`). +load_best_model_at_end: true +metric_for_best_model: eval_loss +greater_is_better: false +hub_model_id: cfierro/llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5 +hub_strategy: end +hub_private_repo: false + +optimizer: adamw_bnb_8bit +lr_scheduler: cosine +learning_rate: 1e-5 # lowered from 2e-5 to reduce drift away from the base model + +bf16: auto +tf32: false + +gradient_checkpointing: true +resume_from_checkpoint: +logging_steps: 1 +flash_attention: true + +warmup_ratio: 0.03 +save_total_limit: 3 # keep all per-epoch checkpoints; best is always retained +weight_decay: 0.0 +special_tokens: + pad_token: <|end_of_text|> + +# DeepSpeed ZeRO Stage 3 — shards weights, gradients, and optimizer across GPUs +deepspeed: deepspeed_configs/zero3.json + +# Verify the data mix + loss mask before training: +# axolotl preprocess axolotl_configs/fullft-othello-snake-8b-multi-format-replay.yaml --debug +# Confirm: game rows show the masked prefix (-100) + trained moves/<|end_of_text|>; +# chat rows show masked user/system turns + trained assistant turns; and the +# heterogeneous input_output + chat_template mix concatenates without error. + +``` + +

+ +# llama-3.1-8b-fft-othello-snake-multi-format-replay-1e-5 + +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. +It achieves the following results on the evaluation set: +- Loss: 0.2137 + +## 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: 1e-05 +- train_batch_size: 4 +- eval_batch_size: 4 +- seed: 42 +- distributed_type: multi-GPU +- num_devices: 4 +- total_train_batch_size: 16 +- total_eval_batch_size: 16 +- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments +- lr_scheduler_type: cosine +- lr_scheduler_warmup_steps: 54 +- training_steps: 1824 + +### Training results + +| Training Loss | Epoch | Step | Validation Loss | +|:-------------:|:-----:|:----:|:---------------:| +| No log | 0 | 0 | 1.1501 | +| 0.5652 | 1.0 | 912 | 0.2181 | +| 0.3346 | 2.0 | 1824 | 0.2137 | + + +### Framework versions + +- Transformers 4.57.3 +- Pytorch 2.9.0+cu128 +- Datasets 3.5.0 +- Tokenizers 0.22.2 diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..386cbb0 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,5 @@ +{% 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|> + +'+ message['content'] | trim + '<|eot_id|>' %}{% if 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