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README.md

@@ -1,73 +1,204 @@
 ---
 library_name: peft
-base_model: daryl149/llama-2-7b-chat-hf
-license: apache-2.0
-datasets:
-- sid6i7/patient-doctor
-pipeline_tag: conversational
-tags:
-- medical
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 ---
 
- Model Card for "medllama"
----------------------------
+# Model Card for Model ID
 
-**Model Name:** medllama
+<!-- Provide a quick summary of what the model is/does. -->
 
-**Library Name:** peft (Python library for Efficient Tuning)
 
-**Base Model:** daryl149/llama-2-7b-chat-hf (A pretrained language model with 7 billion parameters, fine-tuned for chat applications.)
 
-**License:** Apache-2.0 License
+## Model Details
 
-### Usage 
-For usage, use this code block, with GPU recommended
-```
-from transformers import AutoModelForCausalLM, AutoTokenizer
+### Model Description
 
-config = PeftConfig.from_pretrained("tmberooney/medllama")
-model = AutoModelForCausalLM.from_pretrained("daryl149/llama-2-7b-chat-hf", device_map="auto")
-model = PeftModel.from_pretrained(model, "tmberooney/medllama")
-tokenizer=AutoTokenizer.from_pretrained(config.base_model_name_or_path)
-```
+<!-- Provide a longer summary of what this model is. -->
 
-### Intended Use
----------------
 
-The `medllama` model is a fine-tuned version of the base model specifically adapted for medical conversations between patients and doctors. This model can be used in various healthcare settings to assist professionals during their interactions with patients, providing relevant suggestions or answering questions related to health conditions, treatments, medications, and other medical topics. The goal is to improve communication efficiency and ensure accurate information exchange while maintaining privacy and confidentiality standards.
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+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.
+
+[More Information Needed]
+
+## Training Details
 
 ### Training Data
---------------
 
-This model was trained using data from the `sid6i7/patient-doctor` dataset, which contains deidentified medical dialogues between patients and physicians covering diverse medical domains like internal medicine, pediatrics, neurology, psychiatry, and more. These conversations are designed to simulate real-life clinical scenarios, allowing the model to understand context, generate responses that reflect appropriate levels of empathy, and provide reliable medical information based on user queries.
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### 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]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## 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:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
 
-### Evaluation Results
---------------------
+## More Information [optional]
 
-Evaluations were conducted on several benchmark datasets tailored towards measuring performance in medical dialogue systems. Metrics such as perplexity, BLEU score, ROUGE score, and F1 score have been reported to assess the quality and relevance of generated responses compared to reference answers. Detailed evaluation results will be provided separately upon request.
+[More Information Needed]
 
-### Ethical Considerations
--------------------------
+## Model Card Authors [optional]
 
-To maintain ethical guidelines when deploying this model, it's crucial to consider the following aspects:
+[More Information Needed]
 
-* **Data Privacy**: Ensure patient data remains anonymous and protected throughout all stages of development and deployment. Obtain informed consent before utilizing any identifiable personal health information.
-* **Medical Accuracy**: Regularly review and update the model based on new research findings and evidence-based practices. Always encourage users to consult licensed healthcare providers regarding specific concerns or diagnoses.
-* **Bias Mitigation**: Continuously monitor and address potential biases within training data and model outputs to avoid discrimination against certain demographics. Strive for inclusivity by incorporating diverse sources of information during development.
-* **User Awareness**: Inform end-users about limitations, intended uses, and possible risks associated with interacting with an AI system rather than a human expert. Clearly outline expectations for accuracy, response times, and available features.
+## Model Card Contact
 
-For further details on these guidelines, please refer to our project documentation.
+[More Information Needed]
 
-### Citation Information
-----------------------
 
-When citing this work, kindly include the original base model along with your customization:
+### Framework versions
 
-```
-@misc{daryl149_2023_llama_2,
-    author       = {Daryl Lu},
-    title        = {{LLaMA-2}: Scaling Vision-Language Models},
-    howpublished = {\url{https://github.com/facebookresearch/llama}},
-    
-}
-```
+- PEFT 0.7.1

adapter_config.json

@@ -1,7 +1,7 @@
 {
   "alpha_pattern": {},
   "auto_mapping": null,
-  "base_model_name_or_path": "daryl149/llama-2-7b-chat-hf",
+  "base_model_name_or_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
   "bias": "none",
   "fan_in_fan_out": false,
   "inference_mode": true,
@@ -19,8 +19,8 @@
   "rank_pattern": {},
   "revision": null,
   "target_modules": [
-    "q_proj",
-    "v_proj"
+    "v_proj",
+    "q_proj"
   ],
   "task_type": "CAUSAL_LM"
 }

adapter_model.safetensors

@@ -1,3 +1,3 @@
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-size 33571624
+oid sha256:4142640feb2f57f1e1c6f6eadffb520049edeaa6107f3741d0d1bef716253252
+size 9022864