HuggingFaceUser/adapter-mini-medmobile-no_LoRA-UltraMedical-V3

README.md

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+---
+license: mit
+base_model: microsoft/Phi-3-mini-4k-instruct
+tags:
+- trl
+- sft
+- generated_from_trainer
+model-index:
+- name: phi-3-mini-UltraMedical-NoLoRA
+  results: []
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# phi-3-mini-UltraMedical-NoLoRA
+
+This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.7358
+
+## 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: 0.0001
+- train_batch_size: 8
+- eval_batch_size: 8
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 32
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_ratio: 0.1
+- num_epochs: 3
+
+### Training results
+
+| Training Loss | Epoch  | Step  | Validation Loss |
+|:-------------:|:------:|:-----:|:---------------:|
+| 0.8656        | 0.0225 | 200   | 0.7711          |
+| 0.7615        | 0.0451 | 400   | 0.7521          |
+| 0.748         | 0.0676 | 600   | 0.7457          |
+| 0.7465        | 0.0902 | 800   | 0.7428          |
+| 0.7468        | 0.1127 | 1000  | 0.7419          |
+| 0.7434        | 0.1352 | 1200  | 0.7429          |
+| 0.7467        | 0.1578 | 1400  | 0.7451          |
+| 0.7508        | 0.1803 | 1600  | 0.7469          |
+| 0.7505        | 0.2029 | 1800  | 0.7503          |
+| 0.7541        | 0.2254 | 2000  | 0.7531          |
+| 0.7559        | 0.2479 | 2200  | 0.7576          |
+| 0.7592        | 0.2705 | 2400  | 0.7599          |
+| 0.7729        | 0.2930 | 2600  | 0.7635          |
+| 0.772         | 0.3156 | 2800  | 0.7645          |
+| 0.7707        | 0.3381 | 3000  | 0.7628          |
+| 0.7616        | 0.3606 | 3200  | 0.7614          |
+| 0.7632        | 0.3832 | 3400  | 0.7590          |
+| 0.7613        | 0.4057 | 3600  | 0.7574          |
+| 0.7581        | 0.4283 | 3800  | 0.7558          |
+| 0.7583        | 0.4508 | 4000  | 0.7539          |
+| 0.7509        | 0.4733 | 4200  | 0.7518          |
+| 0.7559        | 0.4959 | 4400  | 0.7506          |
+| 0.7523        | 0.5184 | 4600  | 0.7491          |
+| 0.7461        | 0.5410 | 4800  | 0.7469          |
+| 0.7504        | 0.5635 | 5000  | 0.7464          |
+| 0.7486        | 0.5860 | 5200  | 0.7449          |
+| 0.7454        | 0.6086 | 5400  | 0.7436          |
+| 0.7451        | 0.6311 | 5600  | 0.7427          |
+| 0.7431        | 0.6537 | 5800  | 0.7412          |
+| 0.7438        | 0.6762 | 6000  | 0.7402          |
+| 0.7471        | 0.6987 | 6200  | 0.7390          |
+| 0.7416        | 0.7213 | 6400  | 0.7378          |
+| 0.7345        | 0.7438 | 6600  | 0.7364          |
+| 0.7437        | 0.7663 | 6800  | 0.7349          |
+| 0.7431        | 0.7889 | 7000  | 0.7349          |
+| 0.737         | 0.8114 | 7200  | 0.7339          |
+| 0.7358        | 0.8340 | 7400  | 0.7333          |
+| 0.7336        | 0.8565 | 7600  | 0.7320          |
+| 0.7327        | 0.8790 | 7800  | 0.7310          |
+| 0.7288        | 0.9016 | 8000  | 0.7303          |
+| 0.7326        | 0.9241 | 8200  | 0.7295          |
+| 0.7354        | 0.9467 | 8400  | 0.7287          |
+| 0.731         | 0.9692 | 8600  | 0.7278          |
+| 0.7317        | 0.9917 | 8800  | 0.7272          |
+| 0.6809        | 1.0143 | 9000  | 0.7359          |
+| 0.6548        | 1.0368 | 9200  | 0.7341          |
+| 0.6463        | 1.0594 | 9400  | 0.7353          |
+| 0.6516        | 1.0819 | 9600  | 0.7357          |
+| 0.6544        | 1.1044 | 9800  | 0.7345          |
+| 0.6558        | 1.1270 | 10000 | 0.7342          |
+| 0.6532        | 1.1495 | 10200 | 0.7331          |
+| 0.653         | 1.1721 | 10400 | 0.7328          |
+| 0.6583        | 1.1946 | 10600 | 0.7323          |
+| 0.6537        | 1.2171 | 10800 | 0.7326          |
+| 0.6622        | 1.2397 | 11000 | 0.7318          |
+| 0.6596        | 1.2622 | 11200 | 0.7315          |
+| 0.6522        | 1.2848 | 11400 | 0.7304          |
+| 0.6517        | 1.3073 | 11600 | 0.7300          |
+| 0.657         | 1.3298 | 11800 | 0.7296          |
+| 0.6554        | 1.3524 | 12000 | 0.7286          |
+| 0.6545        | 1.3749 | 12200 | 0.7287          |
+| 0.6556        | 1.3975 | 12400 | 0.7283          |
+| 0.655         | 1.4200 | 12600 | 0.7294          |
+| 0.6489        | 1.4425 | 12800 | 0.7285          |
+| 0.6539        | 1.4651 | 13000 | 0.7269          |
+| 0.654         | 1.4876 | 13200 | 0.7273          |
+| 0.6556        | 1.5102 | 13400 | 0.7273          |
+| 0.6529        | 1.5327 | 13600 | 0.7271          |
+| 0.6504        | 1.5552 | 13800 | 0.7264          |
+| 0.6498        | 1.5778 | 14000 | 0.7256          |
+| 0.6517        | 1.6003 | 14200 | 0.7255          |
+| 0.656         | 1.6229 | 14400 | 0.7252          |
+| 0.6471        | 1.6454 | 14600 | 0.7242          |
+| 0.6485        | 1.6679 | 14800 | 0.7243          |
+| 0.6545        | 1.6905 | 15000 | 0.7242          |
+| 0.6527        | 1.7130 | 15200 | 0.7238          |
+| 0.6504        | 1.7356 | 15400 | 0.7236          |
+| 0.6492        | 1.7581 | 15600 | 0.7229          |
+| 0.6529        | 1.7806 | 15800 | 0.7232          |
+| 0.6507        | 1.8032 | 16000 | 0.7226          |
+| 0.653         | 1.8257 | 16200 | 0.7229          |
+| 0.6461        | 1.8483 | 16400 | 0.7223          |
+| 0.6453        | 1.8708 | 16600 | 0.7221          |
+| 0.6534        | 1.8933 | 16800 | 0.7219          |
+| 0.6455        | 1.9159 | 17000 | 0.7220          |
+| 0.6485        | 1.9384 | 17200 | 0.7212          |
+| 0.6536        | 1.9610 | 17400 | 0.7214          |
+| 0.6444        | 1.9835 | 17600 | 0.7211          |
+| 0.6346        | 2.0060 | 17800 | 0.7356          |
+| 0.5929        | 2.0286 | 18000 | 0.7368          |
+| 0.5951        | 2.0511 | 18200 | 0.7371          |
+| 0.6013        | 2.0736 | 18400 | 0.7374          |
+| 0.6004        | 2.0962 | 18600 | 0.7375          |
+| 0.5991        | 2.1187 | 18800 | 0.7375          |
+| 0.5971        | 2.1413 | 19000 | 0.7369          |
+| 0.597         | 2.1638 | 19200 | 0.7380          |
+| 0.5951        | 2.1863 | 19400 | 0.7370          |
+| 0.5916        | 2.2089 | 19600 | 0.7370          |
+| 0.5992        | 2.2314 | 19800 | 0.7372          |
+| 0.6011        | 2.2540 | 20000 | 0.7364          |
+| 0.6003        | 2.2765 | 20200 | 0.7370          |
+| 0.6003        | 2.2990 | 20400 | 0.7370          |
+| 0.5985        | 2.3216 | 20600 | 0.7370          |
+| 0.5988        | 2.3441 | 20800 | 0.7367          |
+| 0.5959        | 2.3667 | 21000 | 0.7370          |
+| 0.6019        | 2.3892 | 21200 | 0.7370          |
+| 0.5977        | 2.4117 | 21400 | 0.7367          |
+| 0.602         | 2.4343 | 21600 | 0.7368          |
+| 0.5958        | 2.4568 | 21800 | 0.7368          |
+| 0.5969        | 2.4794 | 22000 | 0.7360          |
+| 0.6025        | 2.5019 | 22200 | 0.7362          |
+| 0.5942        | 2.5244 | 22400 | 0.7361          |
+| 0.6006        | 2.5470 | 22600 | 0.7361          |
+| 0.5952        | 2.5695 | 22800 | 0.7366          |
+| 0.6007        | 2.5921 | 23000 | 0.7363          |
+| 0.6003        | 2.6146 | 23200 | 0.7363          |
+| 0.6006        | 2.6371 | 23400 | 0.7359          |
+| 0.6014        | 2.6597 | 23600 | 0.7360          |
+| 0.6008        | 2.6822 | 23800 | 0.7356          |
+| 0.6005        | 2.7048 | 24000 | 0.7357          |
+| 0.5958        | 2.7273 | 24200 | 0.7356          |
+| 0.5977        | 2.7498 | 24400 | 0.7358          |
+| 0.6           | 2.7724 | 24600 | 0.7358          |
+| 0.5978        | 2.7949 | 24800 | 0.7362          |
+| 0.6018        | 2.8175 | 25000 | 0.7359          |
+| 0.6079        | 2.8400 | 25200 | 0.7359          |
+| 0.6036        | 2.8625 | 25400 | 0.7359          |
+| 0.5985        | 2.8851 | 25600 | 0.7359          |
+| 0.6019        | 2.9076 | 25800 | 0.7359          |
+| 0.5994        | 2.9302 | 26000 | 0.7358          |
+| 0.6027        | 2.9527 | 26200 | 0.7358          |
+| 0.6014        | 2.9752 | 26400 | 0.7358          |
+| 0.5957        | 2.9978 | 26600 | 0.7358          |
+
+
+### Framework versions
+
+- Transformers 4.43.3
+- Pytorch 2.3.1+cu121
+- Datasets 2.20.0
+- Tokenizers 0.19.1

added_tokens.json

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config.json

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+{
+  "_name_or_path": "microsoft/Phi-3-mini-4k-instruct",
+  "architectures": [
+    "Phi3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "auto_map": {
+    "AutoConfig": "microsoft/Phi-3-mini-4k-instruct--configuration_phi3.Phi3Config",
+    "AutoModelForCausalLM": "microsoft/Phi-3-mini-4k-instruct--modeling_phi3.Phi3ForCausalLM"
+  },
+  "bos_token_id": 1,
+  "embd_pdrop": 0.0,
+  "eos_token_id": 32000,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "initializer_range": 0.02,
+  "intermediate_size": 8192,
+  "max_position_embeddings": 4096,
+  "model_type": "phi3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 32,
+  "original_max_position_embeddings": 4096,
+  "pad_token_id": 32000,
+  "resid_pdrop": 0.0,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "sliding_window": 2047,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.43.3",
+  "use_cache": true,
+  "vocab_size": 32064
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": [
+    32000,
+    32001,
+    32007
+  ],
+  "pad_token_id": 32000,
+  "transformers_version": "4.43.3"
+}

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