Update README.md

README.md

@@ -8,8 +8,9 @@ base_model:
 
 # Model Card for AnthroBot (Llama-3.2-1B-Instruct Fine-tuned)
 
-<!-- This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct, adapted for reasoning and generating contextual insights from anthropometric data (e.g., age, sex, weight, height, waist circumference). It can summarize or comment on health-related metrics conversationally. -->
-
+<!-- Provide a longer summary of what this model is. -->
+This model is a fine-tuned version of meta-llama/Llama-3.2-1B-Instruct, adapted for reasoning and generating contextual insights from anthropometric data (e.g., age, sex, weight, height, waist circumference). 
+It can summarise or comment on health-related metrics conversationally.
 
 
 ## Model Details
@@ -20,19 +21,19 @@ base_model:
 
 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]
-- **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]
+- **Developed by:** Sally S. Simmons
+- **Funded by [optional]:** NA
+- **Shared by [optional]:** https://huggingface.co/SallySims
+- **Model type:** Causal Language Model (LLM) with Instruction Tuning
+- **Language(s) (NLP):** English
+- **License:** Apache 2.0 (or specify if different)
+- **Finetuned from model [optional]:** meta-llama/Llama-3.2-1B-Instruct
 
 ### Model Sources [optional]
 
 <!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
+- **Repository:** https://huggingface.co/SallySims/AnthroBot_Model_Lora
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
 
@@ -44,23 +45,34 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
+The model is intended to analyze structured health-related user inputs and return conversational, 
+personalized feedback.It is designed for educational, wellness, or research purposes.
+
 [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 -->
-
+This model can be incorporated into chatbot systems or mobile health platforms that require 
+health-data-aware natural language interaction.
 [More Information Needed]
 
 ### Out-of-Scope Use
 
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+*Medical diagnosis or treatment
+
+*Critical healthcare decision-making
+
+*Inputs in languages other than English
 
 [More Information Needed]
 
 ## Bias, Risks, and Limitations
 
 <!-- This section is meant to convey both technical and sociotechnical limitations. -->
+The model is trained on 20000 observations based on anthropometric data collected during the WHO STEPS survey and not in clinical settings. 
+Outputs may reflect biases present in the training prompts or may misinterpret edge cases.
 
 [More Information Needed]
 
@@ -68,12 +80,26 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 <!-- 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.
+Seek professional guidance in addition to the outcomes produced by the model
 
 ## How to Get Started with the Model
 
 Use the code below to get started with the model.
 
+from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
+
+model_id = "your-username/AnthroBot"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto")
+
+pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
+
+input_text = "Age: 30, Sex: female, Height: 150.5 cm, Weight: 75.3 kg, WC: 68.0 cm"
+output = pipe(input_text, max_new_tokens=150, do_sample=True)
+print(output[0]['generated_text'])
+
+
 [More Information Needed]
 
 ## Training Details
@@ -81,6 +107,8 @@ Use the code below to get started with the model.
 ### Training Data
 
 <!-- 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. -->
+Custom curated structured anthropometric prompts designed to simulate 
+health-focused instruction-following behavior.
 
 [More Information Needed]
 
@@ -90,12 +118,28 @@ Use the code below to get started with the model.
 
 #### Preprocessing [optional]
 
-[More Information Needed]
+Prompts were normalised for consistent numerical formats and tokenization performance.
 
 
 #### 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 -->
+Epochs: 5
+
+Batch size: 2 (accumulation: 4 steps)
+
+Learning rate: 2e-4
+
+Precision: Mixed precision (fp16 / bf16)
+
+LoRA Parameters:
+
+r=16, alpha=32, dropout=0.05
+
+Quantization
+4-bit quantization using BitsAndBytesConfig
+
+Enabled llm_int8_enable_fp32_cpu_offload
 
 #### Speeds, Sizes, Times [optional]
 
@@ -112,6 +156,8 @@ Use the code below to get started with the model.
 #### Testing Data
 
 <!-- This should link to a Dataset Card if possible. -->
+Evaluation performed on held-out anthropometricindices and recommendations prompts 
+with expected interpretive outputs.
 
 [More Information Needed]
 
@@ -124,12 +170,14 @@ Use the code below to get started with the model.
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
+Human-judged relevance, clarity, and accuracy.
 
 [More Information Needed]
 
 ### Results
 
-[More Information Needed]
+Manual inspection shows clear, concise, and useful summaries in the majority of cases. 
+Some rare edge cases may produce vague or overly generic responses.
 
 #### Summary
 
@@ -147,17 +195,17 @@ Use the code below to get started with the model.
 
 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]
+- **Hardware Type:** NVIDIA T4 GPU
+- **Hours used:** ~ 2 hours
+- **Cloud Provider:** Google Colab
+- **Compute Region:** USA
+- **Carbon Emitted:** ~1.2 kg CO₂eq (approx.)
 
 ## Technical Specifications [optional]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+Decoder-only transformer based on the LLaMA 3.2B architecture.
 
 ### Compute Infrastructure
 
@@ -165,15 +213,21 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 #### Hardware
 
-[More Information Needed]
+ Google Colab (A100)
 
 #### Software
 
-[More Information Needed]
+PyTorch, Hugging Face Transformers, PEFT, BitsAndBytes
 
 ## 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. -->
+@misc{AnthroBot2025,
+  author = {Sally Sonia Simmons},
+  title = {AnthroBot: Instruction-Tuned LLaMA-3.2-1B for Anthropometric Reasoning},
+  year = {2025},
+  url = {https://huggingface.co/SallySimmons/AnthroBot_Model_Lora}
+}
 
 **BibTeX:**
 
@@ -199,4 +253,4 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ## Model Card Contact
 
-[More Information Needed]
+simmonssallysonia@gmail.com