SallySims
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AnthroBot_Model_Lora

Transformers

Safetensors

English

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SallySims commited on Apr 21, 2025

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@@ -4,6 +4,8 @@ language:
 - en
 base_model:
 - meta-llama/Llama-3.2-1B-Instruct
 ---
 # Model Card for AnthroBot (Llama-3.2-1B-Instruct Fine-tuned)
@@ -48,14 +50,13 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 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
@@ -66,7 +67,6 @@ health-data-aware natural language interaction.
 *Inputs in languages other than English
-[More Information Needed]
 ## Bias, Risks, and Limitations
@@ -74,7 +74,6 @@ health-data-aware natural language interaction.
 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]
 ### Recommendations
@@ -100,7 +99,6 @@ output = pipe(input_text, max_new_tokens=150, do_sample=True)
 print(output[0]['generated_text'])
-[More Information Needed]
 ## Training Details
@@ -110,7 +108,6 @@ print(output[0]['generated_text'])
 Custom curated structured anthropometric prompts designed to simulate
 health-focused instruction-following behavior.
-[More Information Needed]
 ### Training Procedure
@@ -145,7 +142,6 @@ Enabled llm_int8_enable_fp32_cpu_offload
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
 ## Evaluation
@@ -159,20 +155,17 @@ Enabled llm_int8_enable_fp32_cpu_offload
 Evaluation performed on held-out anthropometricindices and recommendations prompts
 with expected interpretive outputs.
-[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. -->
 Human-judged relevance, clarity, and accuracy.
-[More Information Needed]
 ### Results
@@ -187,7 +180,6 @@ Some rare edge cases may produce vague or overly generic responses.
 <!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
 ## Environmental Impact
@@ -209,11 +201,10 @@ Decoder-only transformer based on the LLaMA 3.2B architecture.
 ### Compute Infrastructure
-[More Information Needed]
 #### Hardware
- Google Colab (A100)
 #### Software
@@ -231,25 +222,20 @@ PyTorch, Hugging Face Transformers, PEFT, BitsAndBytes
 **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]
 ## More Information [optional]
-[More Information Needed]
 ## Model Card Authors [optional]
-[More Information Needed]
 ## Model Card Contact

 - en
 base_model:
 - meta-llama/Llama-3.2-1B-Instruct
+datasets:
+- SallySims/AnthroBotdata
 ---
 # Model Card for AnthroBot (Llama-3.2-1B-Instruct Fine-tuned)
 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.
 ### 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.
 ### Out-of-Scope Use
 *Inputs in languages other than English
 ## Bias, Risks, and 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.
 ### Recommendations
 print(output[0]['generated_text'])
 ## Training Details
 Custom curated structured anthropometric prompts designed to simulate
 health-focused instruction-following behavior.
 ### Training Procedure
 <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
 ## Evaluation
 Evaluation performed on held-out anthropometricindices and recommendations prompts
 with expected interpretive outputs.
 #### Factors
 <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
 #### Metrics
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
 Human-judged relevance, clarity, and accuracy.
 ### Results
 <!-- Relevant interpretability work for the model goes here -->
 ## Environmental Impact
 ### Compute Infrastructure
 #### Hardware
+Google Colab (A100)
 #### Software
 **BibTeX:**
 **APA:**
 ## Glossary [optional]
 <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+NA
 ## More Information [optional]
+NA
 ## Model Card Authors [optional]
+NA
 ## Model Card Contact