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LLaMA-2-7b-GTL-Delta

Text Generation
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Safetensors
llama
llm
transfer learning
in-context learning
tabular data
text-generation-inference
Inference Endpoints
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@@ -34,60 +34,49 @@ LLaMA-2-GTL supports a vocabulary size of up to `32000` tokens, which is same as
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  Given the nature of the training data, the LLaMA-2-GTL series model is best suited for prompts using the prompt format as follows:
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  ```markdown
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- You are an expert in healthcare data analysis.
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- Based on the patient medical records, please predict the length of stay in the hospital.
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  I will supply multiple instances with features and the corresponding label for your reference.
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  Please refer to the table below for detailed descriptions of the features and label:
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  --- feature description ---
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- hemo: Indicator of hemoglobin count
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- hematocrit: Hematocrit level
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- neutrophils: Neutrophils count
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- sodium: Sodium level
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- glucose: Glucose level
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- bloodureanitro: Blood urea nitrogen level
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- creatinine: Creatinine level
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- bmi: Body Mass Index of patient
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- pulse: Pulse rate of patient
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- respiration: Respiration rate of patient
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- rcount: Count of patient visits
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- gender: Patient gender
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- dialysisrenalendstage: Indicator of end stage renal disease requiring dialysis
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- asthma: Indicator of asthma
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- irondef: Indicator of iron deficiency
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- pneum: Indicator of pneumonia
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- substancedependence: Indicator of substance dependence
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- psychologicaldisordermajor: Indicator of major psychological disorder
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- depress: Indicator of depression
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- psychother: Indicator of psychotherapy
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- fibrosisandother: Indicator of fibrosis and other similar conditions
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- malnutrition: Indicator of malnutrition
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- secondarydiagnosisnonicd9: Indicator of secondary diagnosis other than ICD9
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- facid: Identifier of facility where treatment was provided
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- vdate: Date of patient visit to hospital
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- discharged: Date of patient discharge
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  --- label description ---
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- lengthofstay: Length of patient stay at hospital in days
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  --- data ---
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- |hemo|hematocrit|neutrophils|sodium|glucose|bloodureanitro|creatinine|bmi|pulse|respiration|rcount|gender|dialysisrenalendstage|asthma|irondef|pneum|substancedependence|psychologicaldisordermajor|depress|psychother|fibrosisandother|malnutrition|secondarydiagnosisnonicd9|facid|vdate|discharged|lengthofstay|
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- |0.0|15.2|12.3|141.74|188.88|21.0|0.93|33.48|76|5|0|M|1|0|0|1|0|1|0|0|0|0|0|E|9/3/2012|9/11/2012|8|
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- |0.0|11.0|9.9|140.98|167.7|8.0|1.24|30.98|78|8|0|F|0|0|0|0|0|1|0|0|0|0|2|E|6/13/2012|6/16/2012|3|
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- |0.0|11.9|9.4|138.75|148.82|12.0|1.09|29.51|53|6|3|F|0|0|0|0|0|0|0|0|0|0|1|B|10/19/2012|10/24/2012|5|
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- |0.0|11.9|9.4|137.19|164.71|12.0|1.09|31.98|84|6|1|F|0|0|0|0|0|0|0|0|0|0|2|B|1/16/2012|1/18/2012|2|
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- |0.0|15.1|11.2|134.7|132.43|12.0|1.05|29.12|73|6|0|F|0|0|0|0|0|0|0|0|0|0|1|A|2/21/2012|2/22/2012|1|
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- |0.0|15.8|13.9|137.13|129.93|9.0|1.38|29.93|66|6|0|M|0|0|0|0|0|0|0|0|0|0|1|B|7/16/2012|7/18/2012|2|
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- |0.0|11.9|9.4|140.12|161.36|12.0|1.0|28.55|63|6|0|F|0|0|0|0|0|0|0|0|0|0|1|A|8/16/2012|8/17/2012|1|
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- |0.0|11.9|9.4|134.43|154.18|12.0|1.16|28.14|78|6|4|M|0|0|0|0|0|0|0|0|0|0|3|B|12/8/2012|12/14/2012|6|
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- |0.0|11.3|5.2|137.89|119.99|19.0|1.22|27.82|91|6|0|F|0|0|0|0|0|1|0|0|0|0|0|E|2/23/2012|2/26/2012|3|
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- |0.0|8.9|7.3|139.25|105.44|9.0|0.85|28.89|73|6|5+|M|0|0|0|0|0|0|0|0|0|0|4|B|7/18/2012|7/26/2012|8|
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- |1.0|8.1|5.6|138.4|103.73|21.0|1.26|29.05|74|6|5+|M|0|0|0|0|0|0|0|1|0|0|1|E|2/19/2012|2/28/2012|9|
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- |0.0|11.1|9.9|138.44|115.05|12.0|1.05|29.17|72|6|0|F|0|0|0|0|0|0|0|0|0|0|3|A|12/4/2012|12/5/2012|1|
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- |0.0|13.7|8.1|142.21|160.48|13.0|1.24|29.06|74|6|0|F|0|0|1|0|0|0|0|0|1|0|1|D|9/2/2012|9/5/2012|3|
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- |0.0|13.5|6.3|136.41|96.48|15.0|0.95|30.21|89|8|2|F|0|0|0|0|0|0|0|0|0|0|1|B|6/21/2012|6/27/2012|6|
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- |0.0|11.9|9.4|138.96|121.66|12.0|1.21|30.75|69|6|0|M|0|0|0|0|0|0|0|0|0|0|1|B|9/28/2012|9/29/2012|1|
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- |1.0|8.2|7.9|144.1|145.29|11.0|0.98|30.92|73|5|0|F|0|0|1|1|0|0|0|0|0|1|10|D|8/6/2012|8/12/2012|6|
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- |0.0|10.9|7.8|134.47|141.91|10.0|1.24|26.75|63|6|1|M|0|0|0|0|0|0|0|0|0|0|2|A|11/7/2012|11/9/2012|<MASK>|
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- Please use the supplied data to predict the <MASK> lengthofstay.
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- Answer: 2
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  ```
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  ### Recover full model checkpoint
 
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  Given the nature of the training data, the LLaMA-2-GTL series model is best suited for prompts using the prompt format as follows:
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  ```markdown
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+ You are an expert in health and fitness.
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+ Based on the physical features of the individual, please predict the body fat percentage.
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  I will supply multiple instances with features and the corresponding label for your reference.
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  Please refer to the table below for detailed descriptions of the features and label:
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  --- feature description ---
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+ Age: Age of the individual in years
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+ Weight: Weight of the individual in kilograms
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+ Height: Height of the individual in centimeters
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+ Neck: Circumference of the neck in centimeters
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+ Chest: Circumference of the chest in centimeters
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+ Abdomen: Circumference of the abdomen in centimeters
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+ Hip: Circumference of the hip in centimeters
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+ Thigh: Circumference of the thigh in centimeters
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+ Knee: Circumference of the knee in centimeters
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+ Ankle: Circumference of the ankle in centimeters
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+ Biceps: Circumference of the biceps in centimeters
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+ Forearm: Circumference of the forearm in centimeters
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+ Wrist: Circumference of the wrist in centimeters
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+ Original: Indicates if the record is from the original dataset (Y) or if it was generated (N)
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+ Sex: Gender of the individual (M for male, F for female)
 
 
 
 
 
 
 
 
 
 
 
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  --- label description ---
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+ BodyFat: Percentage of body fat
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  --- data ---
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+ |Age|Weight|Height|Neck|Chest|Abdomen|Hip|Thigh|Knee|Ankle|Biceps|Forearm|Wrist|Original|Sex|BodyFat|
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+ |33|83.58|1.75|40.7|98.9|92.1|103.5|64.0|37.3|23.5|33.5|30.6|19.7|Y|M|13.0|
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+ |18|70.31|1.73|33.0|90.1|73.0|103.0|58.1|39.1|22.0|29.5|27.5|16.5|N|F|24.4|
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+ |23|54.89|1.54|32.4|88.5|67.2|94.0|49.3|35.0|20.5|26.0|23.5|14.6|N|F|20.3|
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+ |20|65.77|1.73|30.5|85.0|65.3|105.0|58.3|38.3|20.5|27.3|23.5|15.5|N|F|25.2|
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+ |18|74.84|1.71|33.0|84.0|96.0|106.0|52.0|39.0|21.5|29.5|25.3|17.3|N|F|33.8|
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+ |21|69.85|1.69|31.0|89.0|76.0|104.5|55.0|39.5|22.5|29.5|26.5|16.3|N|F|26.3|
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+ |41|95.48|1.83|38.5|107.4|98.9|104.1|63.5|39.8|23.5|36.4|30.4|19.1|Y|M|20.4|
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+ |27|97.98|1.93|39.4|103.6|90.9|107.7|66.2|39.2|25.9|37.2|30.2|19.0|Y|M|7.8|
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+ |19|65.77|1.73|34.5|86.5|72.0|100.3|53.3|35.5|22.3|29.0|24.0|16.5|N|F|22.9|
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+ |20|73.03|1.69|34.0|95.4|80.0|104.0|56.5|36.0|24.3|33.0|27.0|17.5|N|F|28.6|
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+ |58|73.37|1.71|35.1|94.9|94.9|100.2|56.8|35.9|21.0|27.8|26.1|17.6|Y|M|26.7|
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+ |19|64.86|1.63|32.3|85.5|68.3|98.3|55.0|39.0|24.0|26.5|24.5|16.2|N|F|23.3|
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+ |19|74.39|1.68|34.0|96.0|87.0|107.0|56.0|39.0|22.4|29.5|24.5|16.0|N|F|31.4|
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+ |24|83.58|1.81|34.4|97.3|100.0|101.9|63.2|42.2|24.0|32.2|27.7|17.7|Y|M|28.7|
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+ |28|93.33|1.75|38.5|105.6|105.0|106.4|68.6|40.0|25.2|35.2|30.7|19.1|Y|M|31.2|
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+ |41|99.11|1.8|39.8|111.7|100.5|108.3|67.1|44.2|25.2|37.5|31.5|18.7|Y|M|21.3|
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+ |32|94.92|1.8|42.1|107.6|97.5|107.0|66.9|40.0|24.4|38.2|31.6|19.3|Y|M|<MASK>|
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+ Please use the supplied data to predict the <MASK> BodyFat.
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+ Answer: 22.9
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  ```
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  ### Recover full model checkpoint