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README.md ADDED
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+ ---
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+ license: cc-by-sa-4.0
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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: stabilityai/stablelm-3b-4e1t
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+ model-index:
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+ - name: everything
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+ results: []
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+ ---
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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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+
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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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+
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+ axolotl version: `0.3.0`
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+ ```yaml
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+ base_model: stabilityai/stablelm-3b-4e1t
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+ base_model_config: stabilityai/stablelm-3b-4e1t
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+ trust_remote_code: true
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+ model_type: AutoModelForCausalLM
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+ tokenizer_type: GPTNeoXTokenizerFast
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+
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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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+
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+ datasets:
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+ - path: totally-not-an-llm/EverythingLM-data-V3
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+ type: alpaca
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+
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+ dataset_prepared_path:
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+ val_set_size: 0.005
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+ output_dir: ./everything
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+
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+ adapter: qlora
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+
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+ sequence_len: 1024
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+ sample_packing: false
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+ pad_to_sequence_len: true
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+ save_safetensors: false
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+
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+ lora_r: 128
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+ lora_alpha: 256
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+ lora_dropout: 0.05
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+ lora_target_linear: false
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+ lora_fan_in_fan_out:
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+ lora_modules_to_save:
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+ - embed_tokens
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+ - lm_head
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+ lora_target_modules:
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+ - q_proj
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+ - v_proj
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+
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+ gradient_accumulation_steps: 1
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+ micro_batch_size: 16
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+ num_epochs: 5
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+ optimizer: paged_adamw_32bit
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+ lr_scheduler: cosine
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+ learning_rate: 0.00005
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+
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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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+
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+ gradient_checkpointing: true
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+ early_stopping_patience:
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+ resume_from_checkpoint:
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+ local_rank:
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+ logging_steps: 1
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+ xformers_attention:
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+ flash_attention: true
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+
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+ warmup_steps: 10
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+ evals_per_epoch: 1
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+ eval_table_size:
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+ saves_per_epoch: 1
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+ debug:
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+ deepspeed:
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+ weight_decay: 0.0
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+ fsdp:
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+ fsdp_config:
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+ special_tokens:
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+ bos_token: "<|endoftext|>"
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+ eos_token: "<|im_end|>"
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+ unk_token: "<|endoftext|>"
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+ tokens:
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+ - "<|im_start|>"
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+ - "<|im_end|>"
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+
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+ ```
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+
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+ </details><br>
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+
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+ # everything
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+
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+ This model is a fine-tuned version of [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t) on the None dataset.
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+ It achieves the following results on the evaluation set:
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+ - Loss: 0.8096
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+
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+ ## Model description
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+
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+ More information needed
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+
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+ ## Intended uses & limitations
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+
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+ More information needed
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+
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+ ## Training and evaluation data
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+
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+ More information needed
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+
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+ ## Training procedure
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+
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+
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+ The following `bitsandbytes` quantization config was used during training:
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+ - quant_method: bitsandbytes
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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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+
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+ ### Training hyperparameters
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+
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-05
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+ - train_batch_size: 16
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+ - eval_batch_size: 16
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+ - seed: 42
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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: 5
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+
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+ ### Training results
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+
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+ | Training Loss | Epoch | Step | Validation Loss |
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+ |:-------------:|:-----:|:----:|:---------------:|
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+ | 1.3873 | 0.02 | 1 | 1.6256 |
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+ | 1.1399 | 1.0 | 60 | 0.8263 |
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+ | 1.5717 | 2.0 | 120 | 0.8054 |
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+ | 0.6861 | 3.0 | 180 | 0.8149 |
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+ | 0.9527 | 4.0 | 240 | 0.8036 |
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+ | 0.5738 | 5.0 | 300 | 0.8096 |
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+
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+
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+ ### Framework versions
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+
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+ - PEFT 0.7.0
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+ - Transformers 4.37.0.dev0
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+ - Pytorch 2.0.1+cu117
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+ - Datasets 2.16.1
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+ - Tokenizers 0.15.0
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+ {
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+ "alpha_pattern": {},
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+ "auto_mapping": null,
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+ "base_model_name_or_path": "stabilityai/stablelm-3b-4e1t",
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+ "bias": "none",
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+ "fan_in_fan_out": null,
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+ "inference_mode": true,
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+ "init_lora_weights": true,
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+ "layers_pattern": null,
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+ "loftq_config": {},
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+ "lora_alpha": 256,
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+ "lora_dropout": 0.05,
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+ "megatron_config": null,
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+ "megatron_core": "megatron.core",
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+ "modules_to_save": [
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+ "embed_tokens",
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+ "lm_head"
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+ ],
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+ "peft_type": "LORA",
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+ "r": 128,
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+ "rank_pattern": {},
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+ "revision": null,
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+ "target_modules": [
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+ "v_proj",
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+ "q_proj"
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+ ],
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+ "task_type": "CAUSAL_LM"
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+ }
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+ {
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+ "_name_or_path": "stabilityai/stablelm-3b-4e1t",
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+ "architectures": [
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+ "StableLMEpochForCausalLM"
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+ ],
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+ "auto_map": {
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+ "AutoConfig": "configuration_stablelm_epoch.StableLMEpochConfig",
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+ "AutoModelForCausalLM": "stabilityai/stablelm-3b-4e1t--modeling_stablelm_epoch.StableLMEpochForCausalLM"
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+ },
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+ "bos_token_id": 0,
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+ "eos_token_id": 0,
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+ "hidden_act": "silu",
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+ "hidden_size": 2560,
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+ "initializer_range": 0.02,
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+ "intermediate_size": 6912,
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+ "max_position_embeddings": 4096,
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+ "model_type": "stablelm_epoch",
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+ "norm_eps": 1e-05,
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+ "num_attention_heads": 32,
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+ "num_heads": 32,
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+ "num_hidden_layers": 32,
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+ "num_key_value_heads": 32,
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+ "quantization_config": {
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+ "bnb_4bit_compute_dtype": "bfloat16",
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+ "bnb_4bit_quant_type": "nf4",
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+ "bnb_4bit_use_double_quant": true,
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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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+ "llm_int8_skip_modules": null,
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+ "llm_int8_threshold": 6.0,
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+ "load_in_4bit": true,
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+ "load_in_8bit": false,
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+ "quant_method": "bitsandbytes"
34
+ },
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+ "rope_pct": 0.25,
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+ "rope_theta": 10000,
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+ "rotary_scaling_factor": 1.0,
38
+ "tie_word_embeddings": false,
39
+ "torch_dtype": "bfloat16",
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+ "transformers_version": "4.37.0.dev0",
41
+ "use_cache": false,
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+ "vocab_size": 50304
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+ }
configuration_stablelm_epoch.py ADDED
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+ # coding=utf-8
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+ # Copyright 2023 Stability and The HuggingFace Inc. team. All rights reserved.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
5
+ # you may not use this file except in compliance with the License.
6
+ # You may obtain a copy of the License at
7
+ #
8
+ # http://www.apache.org/licenses/LICENSE-2.0
9
+ #
10
+ # Unless required by applicable law or agreed to in writing, software
11
+ # distributed under the License is distributed on an "AS IS" BASIS,
12
+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
13
+ # See the License for the specific language governing permissions and
14
+ # limitations under the License.
15
+ """ StableLM Epoch model configuration"""
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+ from transformers import PretrainedConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+
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+ class StableLMEpochConfig(PretrainedConfig):
24
+ r"""
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+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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+ documentation from [`PretrainedConfig`] for more information.
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+
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+ Args:
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+ vocab_size (`int`, *optional*, defaults to 50_304):
30
+ Vocabulary size of the StableLM model. Defines the number of different tokens that
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+ can be represented by the `inputs_ids` passed when calling [`StableLMEpochModel`].
32
+ intermediate_size (`int`, *optional*, defaults to 6912):
33
+ Dimension of the MLP representations.
34
+ hidden_size (`int`, *optional*, defaults to 2560):
35
+ Dimension of the decoder layers and the pooler layer.
36
+ num_hidden_layers (`int`, *optional*, defaults to 32):
37
+ Number of hidden layers in the Transformer decoder.
38
+ num_attention_heads (`int`, *optional*, defaults to 32):
39
+ Number of attention heads for each attention layer in the Transformer encoder.
40
+ num_key_value_heads (`int`, *optional*):
41
+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
45
+ by meanpooling all the original heads within that group. For more details checkout [this
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+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
47
+ `num_attention_heads`.
48
+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
49
+ The non-linear activation function (function or string).
50
+ rope_pct (`float`, *optional*, defaults to 1.0):
51
+ Percentage of hidden dimensions to allocate to rotary embeddings.
52
+ rope_theta (`float`, *optional*, defaults to 10000.0):
53
+ The base period of the RoPE embeddings.
54
+ max_position_embeddings (`int`, *optional*, defaults to 2048):
55
+ The maximum sequence length that this model might ever be used with.
56
+ Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
57
+ initializer_range (`float`, *optional*, defaults to 1e-5):
58
+ The standard deviation of the truncated_normal_initializer for initializing
59
+ all weight matrices.
60
+ norm_eps (`float`, *optional*, defaults to 1e-8):
61
+ The epsilon used by the normalization layers.
62
+ use_cache (`bool`, *optional*, defaults to `True`):
63
+ Whether or not the model should return the last key/values attentions
64
+ (not used by all models). Only relevant if `config.is_decoder=True`.
65
+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
66
+ Whether to tie weight embeddings
67
+ """
68
+ model_type = "stablelm_epoch"
69
+ keys_to_ignore_at_inference = ["past_key_values"]
70
+
71
+ def __init__(
72
+ self,
73
+ vocab_size=50_304,
74
+ intermediate_size=6912,
75
+ hidden_size=2560,
76
+ num_hidden_layers=32,
77
+ num_attention_heads=32,
78
+ num_key_value_heads=32,
79
+ hidden_act="silu",
80
+ rope_pct=0.25,
81
+ rope_theta=10_000,
82
+ max_position_embeddings=4096,
83
+ initializer_range=0.02,
84
+ norm_eps=1.0e-5,
85
+ use_cache=True,
86
+ bos_token_id=0,
87
+ eos_token_id=2,
88
+ tie_word_embeddings=False,
89
+ **kwargs,
90
+ ):
91
+ self.vocab_size = vocab_size
92
+ self.max_position_embeddings = max_position_embeddings
93
+ self.intermediate_size = intermediate_size
94
+ self.hidden_size = hidden_size
95
+ self.num_hidden_layers = num_hidden_layers
96
+ self.num_attention_heads = num_attention_heads
97
+ self.num_key_value_heads = num_key_value_heads
98
+ self.hidden_act = hidden_act
99
+ self.rope_pct = rope_pct
100
+ self.rope_theta = rope_theta
101
+ self.initializer_range = initializer_range
102
+ self.norm_eps = norm_eps
103
+ self.use_cache = use_cache
104
+ self.tie_word_embeddings = tie_word_embeddings
105
+ super().__init__(
106
+ bos_token_id=bos_token_id,
107
+ eos_token_id=eos_token_id,
108
+ tie_word_embeddings=tie_word_embeddings,
109
+ **kwargs,
110
+ )
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