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 - unsloth
 - trl
 - sft
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
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-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
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 ## 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. -->
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-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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 ### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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 ## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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 ### 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
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-### Training Procedure
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-## Evaluation
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-### Testing Data, Factors & Metrics
-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## 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).
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-## Technical Specifications [optional]
-### Model Architecture and Objective
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 - unsloth
 - trl
 - sft
+- Llama
+- Finetuning
+- Medical
+license: apache-2.0
+language:
+- en
+base_model:
+- meta-llama/Meta-Llama-3-8B
 ---
+# Model Card for Medical Chatbot LLaMA3 8b 4-bit Fine-tuned Model
+This is a fine-tuned version of the LLaMA3 model designed to assist with medical queries and preliminary health advice through a chatbot. It uses a 4-bit quantization to reduce memory usage while maintaining efficiency for chatbot interactions.
 ## Model Details
 ### Model Description
+This model is a LLaMA3-8b based chatbot fine-tuned specifically for medical symptom analysis and preliminary assessments. It uses 4-bit quantization, which allows for reduced memory usage, making it suitable for deployment on resource-constrained environments. The chatbot can respond to medical inquiries and provide initial health recommendations, though it should not be used as a substitute for professional medical advice.
+This model is trained on 1M datapoints dataset consisting of Question answering related to Medical field. It would be helpful for both general person who wants info related to medical issues and also for healthcare providers.
+- **Developed by:** Himanshu Kumar
+- **Finetuned from model:** LLaMA3 8b
+- **Language(s) (NLP):** English
+- **License:** apache-2.0
+- **Model type:** Causal Language Model (LLM), Fine-tuned with LoRA and 4-bit quantization
 ## Uses
 ### Direct Use
+The model is primarily intended for use in medical chatbots to handle preliminary health-related queries and symptom analysis. It is designed for interactive applications that aim to provide users with health-related information and advice.
+### Downstream Use
+This model can be further fine-tuned or adapted to other medical domains or integrated into larger healthcare-related systems. It can also be used for other conversational AI tasks in the medical domain, such as appointment scheduling, follow-up care reminders, or patient triage systems.
 ### Out-of-Scope Use
+- This model should not be used for critical medical decision-making or as a substitute for professional medical advice.
 ## Bias, Risks, and Limitations
+The model may inherit biases present in the training data, and the responses should be carefully monitored, particularly in sensitive contexts like healthcare. It is recommended that the model's responses be reviewed by healthcare professionals.
 ### Recommendations
+Users should exercise caution and verify any medical information provided by the model with qualified professionals. The model's limitations in handling complex medical cases must be understood before deployment in real-world scenarios.
 ## How to Get Started with the Model
+Use the following code to get started with the model:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model = AutoModelForCausalLM.from_pretrained("abhiyanta/chatDoctor")
+tokenizer = AutoTokenizer.from_pretrained("abhiyanta/chatDoctor")
+inputs = tokenizer(
+[
+    alpaca_prompt.format(
+        "I have fever, what should i do?", # instruction
+        "", # input
+        "", # output - leave this blank for generation!
+    )
+], return_tensors = "pt").to("cuda")
+from transformers import TextStreamer
+text_streamer = TextStreamer(tokenizer)
+_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128)