Model save
Browse files- README.md +159 -0
- adapter_model.safetensors +1 -1
README.md
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---
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license: apache-2.0
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library_name: peft
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tags:
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- generated_from_trainer
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base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
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model-index:
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- name: mixtral-fc-w-resp-new-format-4e-no-negative
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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/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)
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<details><summary>See axolotl config</summary>
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axolotl version: `0.4.0`
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```yaml
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base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
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model_type: AutoModelForCausalLM
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tokenizer_type: LlamaTokenizer
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trust_remote_code: true
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load_in_8bit: false
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load_in_4bit: true
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strict: false
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chat_template: inst
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datasets:
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- path: ./data/with_function_response/function_not_used_training.jsonl
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type: sharegpt
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conversation: mistral
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# - path: ./data/with_function_response/no_function_training.jsonl
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# type: sharegpt
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# conversation: mistral
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- path: ./data/with_function_response/function_used_training.jsonl
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type: sharegpt
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conversation: mistral
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dataset_prepared_path: last_run_prepared
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val_set_size: 0.0
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output_dir: ../mixtral-fc-w-resp-new-format-4e-no-negative
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model_config:
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output_router_logits: true
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adapter: qlora
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lora_model_dir:
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sequence_len: 16384
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sample_packing: true
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pad_to_sequence_len: true
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lora_r: 32
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lora_alpha: 64
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lora_dropout: 0.05
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lora_target_modules:
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- q_proj
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- k_proj
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- v_proj
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- o_proj
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wandb_project: function-call
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wandb_name: mixtral-instruct-lora-no-negative
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wandb_log_model: end
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hub_model_id: dyang415/mixtral-fc-w-resp-new-format-4e-no-negative
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gradient_accumulation_steps: 4
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micro_batch_size: 2
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num_epochs: 4
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optimizer: paged_adamw_8bit
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lr_scheduler: cosine
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learning_rate: 0.0002
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train_on_inputs: false
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group_by_length: false
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bf16: true
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fp16: false
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tf32: false
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gradient_checkpointing: true
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logging_steps: 1
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flash_attention: true
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loss_watchdog_threshold: 5.0
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loss_watchdog_patience: 3
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warmup_steps: 10
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evals_per_epoch: 4
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eval_table_size:
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eval_max_new_tokens: 128
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saves_per_epoch: 1
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debug:
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weight_decay: 0.0
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fsdp:
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fsdp_config:
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```
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</details><br>
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# mixtral-fc-w-resp-new-format-4e-no-negative
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This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on an unknown dataset.
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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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The following `bitsandbytes` quantization config was used during training:
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- quant_method: QuantizationMethod.BITS_AND_BYTES
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: None
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- llm_int8_enable_fp32_cpu_offload: False
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- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: True
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- bnb_4bit_compute_dtype: bfloat16
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0002
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- train_batch_size: 2
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- eval_batch_size: 2
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 2
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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- total_eval_batch_size: 4
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- num_epochs: 4
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### Framework versions
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- PEFT 0.7.0
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- Transformers 4.37.0
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- Pytorch 2.0.1+cu117
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- Datasets 2.17.1
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- Tokenizers 0.15.0
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adapter_model.safetensors
CHANGED
@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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-
oid sha256:
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size 109086416
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version https://git-lfs.github.com/spec/v1
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oid sha256:7104ab47daf575be181613fcd972c4d7aa25505633b9b6efed02460585be553e
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size 109086416
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