初始化项目，由ModelHub XC社区提供模型

Model: brucewayne0459/OpenBioLLm-Derm
Source: Original Platform

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README.md

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+---
+datasets:
+- brucewayne0459/Skin_diseases_and_care
+language:
+- en
+license: mit
+tags:
+- medical
+- dermatology
+- skin_disease
+- skin_care
+- unsloth
+- trl
+- sft
+---
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** Bruce_Wayne(The Batman)
+- **Model type:** Text Generation
+- **Finetuned from model [optional]:** OpenBioLLM(llama-3)(aaditya/Llama3-OpenBioLLM-8B)
+
+## You can find the gguf versions here --> https://huggingface.co/brucewayne0459/OpenBioLLm-Derm-gguf
+### please let me know how the model works -->https://forms.gle/N14zZTkLpUr6Hf4BA
+### Thank you!
+## Uses
+
+### Direct Use
+
+This model is fine-tuned on skin diseases and dermatology data and is used for a dermatology chatbot to provide clear, accurate, and helpful information about various skin diseases, skin care routines, treatments, and related dermatological advice.
+
+
+## Bias, Risks, and Limitations
+
+This model is trained on dermatology data, which might contain inherent biases. It is important to note that the model's responses should not be considered a substitute for professional medical advice. There may be limitations in understanding rare skin conditions or those not well-represented in the training data.
+The model still need to be fine-tuned further to get accurate answers.
+
+### Recommendations
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "brucewayne0459/OpenBioLLm-Derm"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+```
+
+## Training Details
+
+### Training Data
+
+The model is fine-tuned on a dataset containing information about various skin diseases and dermatology care.
+brucewayne0459/Skin_diseases_and_care
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+You are a highly knowledgeable and empathetic dermatologist. Provide clear, accurate, and helpful information about various skin diseases, skin care routines, treatments, and related dermatological advice.
+
+### Input:
+{}
+
+### Response:
+{}
+"""
+EOS_TOKEN = tokenizer.eos_token  # Must add EOS_TOKEN
+
+def formatting_prompts_func(examples):
+    inputs = examples["Topic"]
+    outputs = examples["Information"]
+    texts = []
+
+Prompt passed while fine tuning the model
+#### Training Hyperparameters
+
+Training regime: The model was trained using the following hyperparameters:
+Per device train batch size: 2
+Gradient accumulation steps: 4
+Warmup steps: 5
+Max steps: 120
+Learning rate: 2e-4
+Optimizer: AdamW (8-bit)
+Weight decay: 0.01
+LR scheduler type: Linear
+
+
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** Tesls T4 gpu
+- **Hours used:** 1hr
+- **Cloud Provider:** Google Colab
+
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+This model is based on the LLaMA (Large Language Model Meta AI) architecture and fine-tuned to provide dermatological advice.
+
+
+#### Hardware
+
+The training was performed on Tesla T4 gpu with 4-bit quantization and gradient checkpointing to optimize memory usage.
+
+
+### Feel free to provide any missing details or correct the assumptions made, and I'll update the model card accordingly.
+

config.json

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+{
+  "_name_or_path": "aaditya/Llama3-OpenBioLLM-8B",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128001,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 128255,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.43.3",
+  "unsloth_version": "2024.8",
+  "use_cache": false,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128001,
+  "transformers_version": "4.43.3"
+}

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+}

special_tokens_map.json

@@ -0,0 +1,17 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|end_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|reserved_special_token_250|>"
+}