jellyconsumer
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jellyconsumer/new_model_falcon_large
Browse files- README.md +55 -0
- config.json +35 -0
- generation_config.json +6 -0
- pytorch_model-00001-of-00003.bin +3 -0
- pytorch_model-00002-of-00003.bin +3 -0
- pytorch_model-00003-of-00003.bin +3 -0
- pytorch_model.bin.index.json +203 -0
- special_tokens_map.json +17 -0
- tokenizer.json +0 -0
- tokenizer_config.json +11 -0
- training_args.bin +3 -0
README.md
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---
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license: apache-2.0
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base_model: vilsonrodrigues/falcon-7b-instruct-sharded
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tags:
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- generated_from_trainer
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model-index:
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- name: new_model_falcon_large
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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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# new_model_falcon_large
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This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) 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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### 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: 4
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- eval_batch_size: 8
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- seed: 42
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 16
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: constant
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- lr_scheduler_warmup_ratio: 0.03
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- training_steps: 1000
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### Training results
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### Framework versions
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- Transformers 4.32.1
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- Pytorch 2.0.1+cu117
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- Datasets 2.12.0
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- Tokenizers 0.13.2
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config.json
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{
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"_name_or_path": "vilsonrodrigues/falcon-7b-instruct-sharded",
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"alibi": false,
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"apply_residual_connection_post_layernorm": false,
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"architectures": [
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"FalconForCausalLM"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "vilsonrodrigues/falcon-7b-instruct-sharded--configuration_falcon.FalconConfig",
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"AutoModel": "vilsonrodrigues/falcon-7b-instruct-sharded--modeling_falcon.FalconModel",
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"AutoModelForCausalLM": "vilsonrodrigues/falcon-7b-instruct-sharded--modeling_falcon.FalconForCausalLM",
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"AutoModelForQuestionAnswering": "vilsonrodrigues/falcon-7b-instruct-sharded--modeling_falcon.FalconForQuestionAnswering",
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"AutoModelForSequenceClassification": "vilsonrodrigues/falcon-7b-instruct-sharded--modeling_falcon.FalconForSequenceClassification",
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"AutoModelForTokenClassification": "vilsonrodrigues/falcon-7b-instruct-sharded--modeling_falcon.FalconForTokenClassification"
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},
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"bias": false,
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"bos_token_id": 11,
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"eos_token_id": 11,
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"hidden_dropout": 0.0,
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"hidden_size": 4544,
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"initializer_range": 0.02,
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"layer_norm_epsilon": 1e-05,
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"model_type": "falcon",
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"multi_query": true,
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"new_decoder_architecture": false,
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"num_attention_heads": 71,
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"num_hidden_layers": 32,
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"num_kv_heads": 71,
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"parallel_attn": true,
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"torch_dtype": "float32",
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"transformers_version": "4.32.1",
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"use_cache": false,
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"vocab_size": 65024
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}
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generation_config.json
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"_from_model_config": true,
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"transformers_version": "4.32.1"
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}
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pytorch_model-00001-of-00003.bin
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tokenizer.json
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tokenizer_config.json
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