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| 🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning. | |
| 🦥 Unsloth Zoo will now patch everything to make training faster! | |
| 2024-12-16 20:42:53,788 - INFO - Starting training with arguments: Namespace(input_file='./sharegpt_train_uniqueid_few_chars.jsonl', output_dir='tool_model', model='unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit', max_seq_length=4096, batch_size=2, num_epochs=3, learning_rate=2e-05, lora_rank=64, lora_alpha=128, save_model=False) | |
| 2024-12-16 20:42:53,788 - INFO - Loading dataset from ./sharegpt_train_uniqueid_few_chars.jsonl | |
| 2024-12-16 20:42:53,791 - INFO - Successfully loaded 104 examples | |
| Example from original dataset: | |
| { | |
| "conversations": [ | |
| { | |
| "from": "system", | |
| "value": "You are a help assistant. You are helping a user execute a tool" | |
| }, | |
| { | |
| "from": "system", | |
| "value": "Available tool: [{\"name\": \"designOrigamiSpacecraft\", \"description\": \"Simulates and optimizes the design of origami-inspired spacecraft structures for compact storage, efficient deployment, and reliable recovery\", \"parameters\": {\"type\": \"object\", \"properties\": {\"structureType\": {\"type\": \"string\", \"description\": \"Type of origami-inspired structure to design, such as solar panel or antenna\"}, \"materialProperties\": {\"type\": \"object\", \"description\": \"Physical properties of the 2D material used for the structure, such as thickness and stiffness\"}, \"deploymentConstraints\": {\"type\": \"object\", \"description\": \"Constraints for the deployment of the structure, such as available space and force limits\"}}, \"required\": [\"structureType\", \"materialProperties\", \"deploymentConstraints\"]}}]" | |
| }, | |
| { | |
| "from": "human", | |
| "value": "Design an origami-inspired solar panel structure with a material thickness of 0.1 mm and stiffness of 1000 N/m, to be deployed in a cubic space of 1 meter with a maximum force limit of 1000 N." | |
| }, | |
| { | |
| "from": "gpt", | |
| "value": "Tool call: [{'name': 'designOrigamiSpacecraft', 'arguments': {'deploymentConstraints': {'availableSpace': '1 cubic meter', 'forceLimit': 1000}, 'structureType': 'solar panel', 'materialProperties': {'thickness': 0.1, 'stiffness': 1000}}}]" | |
| }, | |
| { | |
| "from": "system", | |
| "value": "Tool response: {'structureDesign': {'type': 'solar panel', 'foldPattern': 'miura-ori', 'numPanels': 4, 'panelDimensions': {'length': 1.2, 'width': 0.8}, 'deploymentSequence': [{'step': 1, 'action': 'unfold panel 1', 'forceRequired': 200}, {'step': 2, 'action': 'unfold panel 2', 'forceRequired': 250}, {'step': 3, 'action': 'unfold panel 3', 'forceRequired': 200}, {'step': 4, 'action': 'unfold panel 4', 'forceRequired': 250}]}, 'performanceMetrics': {'compactnessRatio': 0.8, 'deploymentTime': 120, 'structuralIntegrity': 0.95, 'powerGeneration': 200}, 'materialUsage': {'totalMaterialUsed': 1.5, 'materialWaste': 0.1}, 'optimizationResults': {'optimumThickness': 0.12, 'optimumStiffness': 1200}}" | |
| }, | |
| { | |
| "from": "gpt", | |
| "value": "Tool designOrigamiSpacecraft has been executed successfully." | |
| } | |
| ] | |
| } | |
| Example after processing: | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "designOrigamiSpacecraft", "description": "Simulates and optimizes the design of origami-inspired spacecraft structures for compact storage, efficient deployment, and reliable recovery", "parameters": {"type": "object", "properties": {"structureType": {"type": "string", "description": "Type of origami-inspired structure to design, such as solar panel or antenna"}, "materialProperties": {"type": "object", "description": "Physical properties of the 2D material used for the structure, such as thickness and stiffness"}, "deploymentConstraints": {"type": "object", "description": "Constraints for the deployment of the structure, such as available space and force limits"}}, "required": ["structureType", "materialProperties", "deploymentConstraints"]}}]<|im_end|> | |
| <|im_start|>user | |
| Design an origami-inspired solar panel structure with a material thickness of 0.1 mm and stiffness of 1000 N/m, to be deployed in a cubic space of 1 meter with a maximum force limit of 1000 N.<|im_end|> | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'designOrigamiSpacecraft', 'arguments': {'deploymentConstraints': {'availableSpace': '1 cubic meter', 'forceLimit': 1000}, 'structureType': 'solar panel', 'materialProperties': {'thickness': 0.1, 'stiffness': 1000}}}]<|im_end|> | |
| <|im_start|>system | |
| Tool response: {'structureDesign': {'type': 'solar panel', 'foldPattern': 'miura-ori', 'numPanels': 4, 'panelDimensions': {'length': 1.2, 'width': 0.8}, 'deploymentSequence': [{'step': 1, 'action': 'unfold panel 1', 'forceRequired': 200}, {'step': 2, 'action': 'unfold panel 2', 'forceRequired': 250}, {'step': 3, 'action': 'unfold panel 3', 'forceRequired': 200}, {'step': 4, 'action': 'unfold panel 4', 'forceRequired': 250}]}, 'performanceMetrics': {'compactnessRatio': 0.8, 'deploymentTime': 120, 'structuralIntegrity': 0.95, 'powerGeneration': 200}, 'materialUsage': {'totalMaterialUsed': 1.5, 'materialWaste': 0.1}, 'optimizationResults': {'optimumThickness': 0.12, 'optimumStiffness': 1200}}<|im_end|> | |
| <|im_start|>assistant | |
| Tool designOrigamiSpacecraft has been executed successfully.<|im_end|> | |
| 2024-12-16 20:42:54,206 - INFO - Processed dataset contains 104 examples | |
| 2024-12-16 20:42:54,206 - INFO - Loading model: unsloth/Qwen2.5-0.5B-Instruct-bnb-4bit | |
| ==((====))== Unsloth 2024.12.4: Fast Qwen2 patching. Transformers:4.46.3. | |
| \\ /| GPU: NVIDIA GeForce RTX 3090. Max memory: 23.676 GB. Platform: Linux. | |
| O^O/ \_/ \ Torch: 2.5.1+cu124. CUDA: 8.6. CUDA Toolkit: 12.4. Triton: 3.1.0 | |
| \ / Bfloat16 = TRUE. FA [Xformers = 0.0.28.post3. FA2 = True] | |
| "-____-" Free Apache license: http://github.com/unslothai/unsloth | |
| Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored! | |
| 2024-12-16 20:42:57,748 - INFO - Adding LoRA adapters | |
| 2024-12-16 20:43:00,190 - INFO - Starting training | |
| {'loss': 1.6489, 'grad_norm': 56.2844352722168, 'learning_rate': 2.5e-06, 'epoch': 0.04} | |
| {'loss': 1.5991, 'grad_norm': 45.4105224609375, 'learning_rate': 5e-06, 'epoch': 0.08} | |
| {'loss': 1.577, 'grad_norm': 42.69998550415039, 'learning_rate': 7.500000000000001e-06, 'epoch': 0.12} | |
| {'loss': 1.3579, 'grad_norm': 31.641265869140625, 'learning_rate': 1e-05, 'epoch': 0.15} | |
| {'loss': 1.2457, 'grad_norm': 23.406902313232422, 'learning_rate': 1.25e-05, 'epoch': 0.19} | |
| {'loss': 0.9524, 'grad_norm': 29.465269088745117, 'learning_rate': 1.5000000000000002e-05, 'epoch': 0.23} | |
| {'loss': 1.0444, 'grad_norm': 18.167865753173828, 'learning_rate': 1.7500000000000002e-05, 'epoch': 0.27} | |
| {'loss': 1.0352, 'grad_norm': 21.749923706054688, 'learning_rate': 2e-05, 'epoch': 0.31} | |
| {'loss': 0.9641, 'grad_norm': 20.359521865844727, 'learning_rate': 1.9714285714285718e-05, 'epoch': 0.35} | |
| {'loss': 1.1613, 'grad_norm': 19.242033004760742, 'learning_rate': 1.942857142857143e-05, 'epoch': 0.38} | |
| {'loss': 0.875, 'grad_norm': 22.187788009643555, 'learning_rate': 1.9142857142857146e-05, 'epoch': 0.42} | |
| {'loss': 0.9554, 'grad_norm': 16.594640731811523, 'learning_rate': 1.885714285714286e-05, 'epoch': 0.46} | |
| {'loss': 0.8826, 'grad_norm': 21.508604049682617, 'learning_rate': 1.8571428571428575e-05, 'epoch': 0.5} | |
| {'loss': 0.7997, 'grad_norm': 19.645261764526367, 'learning_rate': 1.8285714285714288e-05, 'epoch': 0.54} | |
| {'loss': 0.8494, 'grad_norm': 19.969411849975586, 'learning_rate': 1.8e-05, 'epoch': 0.58} | |
| {'loss': 0.8109, 'grad_norm': 13.0437593460083, 'learning_rate': 1.7714285714285717e-05, 'epoch': 0.62} | |
| {'loss': 0.8031, 'grad_norm': 14.634472846984863, 'learning_rate': 1.742857142857143e-05, 'epoch': 0.65} | |
| {'loss': 0.8174, 'grad_norm': 13.95881175994873, 'learning_rate': 1.7142857142857142e-05, 'epoch': 0.69} | |
| {'loss': 0.8679, 'grad_norm': 15.006111145019531, 'learning_rate': 1.6857142857142858e-05, 'epoch': 0.73} | |
| {'loss': 0.7248, 'grad_norm': 11.190885543823242, 'learning_rate': 1.6571428571428574e-05, 'epoch': 0.77} | |
| {'loss': 0.8064, 'grad_norm': 11.187055587768555, 'learning_rate': 1.6285714285714287e-05, 'epoch': 0.81} | |
| {'loss': 0.8157, 'grad_norm': 12.24456787109375, 'learning_rate': 1.6000000000000003e-05, 'epoch': 0.85} | |
| {'loss': 0.6385, 'grad_norm': 13.778310775756836, 'learning_rate': 1.5714285714285715e-05, 'epoch': 0.88} | |
| {'loss': 0.5677, 'grad_norm': 11.04747200012207, 'learning_rate': 1.542857142857143e-05, 'epoch': 0.92} | |
| {'loss': 0.8459, 'grad_norm': 15.128291130065918, 'learning_rate': 1.5142857142857144e-05, 'epoch': 0.96} | |
| {'loss': 0.7374, 'grad_norm': 11.4290132522583, 'learning_rate': 1.4857142857142858e-05, 'epoch': 1.0} | |
| {'loss': 0.609, 'grad_norm': 10.047399520874023, 'learning_rate': 1.4571428571428573e-05, 'epoch': 1.04} | |
| {'loss': 0.4997, 'grad_norm': 10.968252182006836, 'learning_rate': 1.4285714285714287e-05, 'epoch': 1.08} | |
| {'loss': 0.6103, 'grad_norm': 10.889334678649902, 'learning_rate': 1.4e-05, 'epoch': 1.12} | |
| {'loss': 0.7341, 'grad_norm': 12.112325668334961, 'learning_rate': 1.3714285714285716e-05, 'epoch': 1.15} | |
| {'loss': 0.4807, 'grad_norm': 9.100838661193848, 'learning_rate': 1.3428571428571429e-05, 'epoch': 1.19} | |
| {'loss': 0.5163, 'grad_norm': 9.655740737915039, 'learning_rate': 1.3142857142857145e-05, 'epoch': 1.23} | |
| {'loss': 0.4057, 'grad_norm': 10.210302352905273, 'learning_rate': 1.2857142857142859e-05, 'epoch': 1.27} | |
| {'loss': 0.5873, 'grad_norm': 12.235896110534668, 'learning_rate': 1.2571428571428572e-05, 'epoch': 1.31} | |
| {'loss': 0.5055, 'grad_norm': 10.173627853393555, 'learning_rate': 1.2285714285714288e-05, 'epoch': 1.35} | |
| {'loss': 0.5704, 'grad_norm': 12.496862411499023, 'learning_rate': 1.2e-05, 'epoch': 1.38} | |
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| {'loss': 0.5046, 'grad_norm': 10.790641784667969, 'learning_rate': 1.1428571428571429e-05, 'epoch': 1.46} | |
| {'loss': 0.4484, 'grad_norm': 10.88908576965332, 'learning_rate': 1.1142857142857143e-05, 'epoch': 1.5} | |
| {'loss': 0.4523, 'grad_norm': 9.64620304107666, 'learning_rate': 1.0857142857142858e-05, 'epoch': 1.54} | |
| {'loss': 0.5247, 'grad_norm': 14.251874923706055, 'learning_rate': 1.0571428571428572e-05, 'epoch': 1.58} | |
| {'loss': 0.6043, 'grad_norm': 13.406927108764648, 'learning_rate': 1.0285714285714285e-05, 'epoch': 1.62} | |
| {'loss': 0.3808, 'grad_norm': 10.372373580932617, 'learning_rate': 1e-05, 'epoch': 1.65} | |
| {'loss': 0.5708, 'grad_norm': 11.89356803894043, 'learning_rate': 9.714285714285715e-06, 'epoch': 1.69} | |
| {'loss': 0.4659, 'grad_norm': 9.849249839782715, 'learning_rate': 9.42857142857143e-06, 'epoch': 1.73} | |
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| {'loss': 0.5425, 'grad_norm': 11.218565940856934, 'learning_rate': 8.000000000000001e-06, 'epoch': 1.92} | |
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| {'loss': 0.4596, 'grad_norm': 9.647382736206055, 'learning_rate': 7.428571428571429e-06, 'epoch': 2.0} | |
| {'loss': 0.3557, 'grad_norm': 8.199199676513672, 'learning_rate': 7.1428571428571436e-06, 'epoch': 2.04} | |
| {'loss': 0.4121, 'grad_norm': 9.679134368896484, 'learning_rate': 6.857142857142858e-06, 'epoch': 2.08} | |
| {'loss': 0.3131, 'grad_norm': 7.958026885986328, 'learning_rate': 6.571428571428572e-06, 'epoch': 2.12} | |
| {'loss': 0.3985, 'grad_norm': 7.949954032897949, 'learning_rate': 6.285714285714286e-06, 'epoch': 2.15} | |
| {'loss': 0.3645, 'grad_norm': 8.335827827453613, 'learning_rate': 6e-06, 'epoch': 2.19} | |
| {'loss': 0.3351, 'grad_norm': 9.364089012145996, 'learning_rate': 5.7142857142857145e-06, 'epoch': 2.23} | |
| {'loss': 0.3766, 'grad_norm': 9.394909858703613, 'learning_rate': 5.428571428571429e-06, 'epoch': 2.27} | |
| {'loss': 0.3551, 'grad_norm': 8.39985179901123, 'learning_rate': 5.142857142857142e-06, 'epoch': 2.31} | |
| {'loss': 0.271, 'grad_norm': 8.268387794494629, 'learning_rate': 4.857142857142858e-06, 'epoch': 2.35} | |
| {'loss': 0.36, 'grad_norm': 11.230920791625977, 'learning_rate': 4.571428571428572e-06, 'epoch': 2.38} | |
| {'loss': 0.4393, 'grad_norm': 10.73487663269043, 'learning_rate': 4.2857142857142855e-06, 'epoch': 2.42} | |
| {'loss': 0.419, 'grad_norm': 11.545412063598633, 'learning_rate': 4.000000000000001e-06, 'epoch': 2.46} | |
| {'loss': 0.3569, 'grad_norm': 8.766847610473633, 'learning_rate': 3.7142857142857146e-06, 'epoch': 2.5} | |
| {'loss': 0.3106, 'grad_norm': 9.218771934509277, 'learning_rate': 3.428571428571429e-06, 'epoch': 2.54} | |
| {'loss': 0.3889, 'grad_norm': 12.289376258850098, 'learning_rate': 3.142857142857143e-06, 'epoch': 2.58} | |
| {'loss': 0.2912, 'grad_norm': 9.809981346130371, 'learning_rate': 2.8571428571428573e-06, 'epoch': 2.62} | |
| {'loss': 0.4218, 'grad_norm': 10.914944648742676, 'learning_rate': 2.571428571428571e-06, 'epoch': 2.65} | |
| {'loss': 0.4336, 'grad_norm': 11.314194679260254, 'learning_rate': 2.285714285714286e-06, 'epoch': 2.69} | |
| {'loss': 0.3426, 'grad_norm': 13.153592109680176, 'learning_rate': 2.0000000000000003e-06, 'epoch': 2.73} | |
| {'loss': 0.3093, 'grad_norm': 8.912984848022461, 'learning_rate': 1.7142857142857145e-06, 'epoch': 2.77} | |
| {'loss': 0.3986, 'grad_norm': 12.450846672058105, 'learning_rate': 1.4285714285714286e-06, 'epoch': 2.81} | |
| {'loss': 0.2214, 'grad_norm': 8.969927787780762, 'learning_rate': 1.142857142857143e-06, 'epoch': 2.85} | |
| {'loss': 0.6335, 'grad_norm': 12.591325759887695, 'learning_rate': 8.571428571428572e-07, 'epoch': 2.88} | |
| {'loss': 0.3744, 'grad_norm': 11.540536880493164, 'learning_rate': 5.714285714285715e-07, 'epoch': 2.92} | |
| {'loss': 0.338, 'grad_norm': 9.730376243591309, 'learning_rate': 2.8571428571428575e-07, 'epoch': 2.96} | |
| {'loss': 0.4337, 'grad_norm': 10.06140422821045, 'learning_rate': 0.0, 'epoch': 3.0} | |
| {'train_runtime': 26.5877, 'train_samples_per_second': 11.735, 'train_steps_per_second': 2.934, 'train_loss': 0.6247573277124991, 'epoch': 3.0} | |
| 2024-12-16 20:43:27,618 - INFO - Training completed. Stats: TrainOutput(global_step=78, training_loss=0.6247573277124991, metrics={'train_runtime': 26.5877, 'train_samples_per_second': 11.735, 'train_steps_per_second': 2.934, 'total_flos': 545838223019520.0, 'train_loss': 0.6247573277124991, 'epoch': 3.0}) | |
| Testing examples... | |
| ==== Initial Input ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "orchestraRehearsalTool", "description": "A digital tool to assist the conductor in annotating and distributing scores, providing real-time feedback on timing and expression during rehearsals", "parameters": {"type": "object", "properties": {"scoreId": {"type": "string", "description": "Unique identifier of the musical score"}, "rehearsalId": {"type": "string", "description": "Unique identifier of the rehearsal session"}, "musicianIds": {"type": "array", "items": {"type": "string"}, "description": "List of unique identifiers of the musicians participating in the rehearsal"}, "annotationData": {"type": "object", "description": "Annotations and notes for the score, including timing and expression feedback"}}, "required": ["scoreId", "rehearsalId", "musicianIds", "annotationData"]}}]<|im_end|> | |
| <|im_start|>user | |
| Assist the conductor in annotating and distributing the score with id "symphony123" for the rehearsal session with id "rehearsal456" to musicians with ids "musician1", "musician2", and "musician3" using the annotation data {"timing": "slow", "expression": "more dynamics"}.<|im_end|> | |
| ==== Tool Call ==== | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'orchestraRehearsalTool', 'arguments': {'annotationData': {'timing': 'slow', 'expression': 'more dynamics'}, 'scoreId': 'symphony123', 'rehearsalId': 'rehearsal456', 'musicianIds': ['musician1', 'musician2', 'musician3']}}]<|im_end|> | |
| ==== Second Input (With Tool Response) ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "orchestraRehearsalTool", "description": "A digital tool to assist the conductor in annotating and distributing scores, providing real-time feedback on timing and expression during rehearsals", "parameters": {"type": "object", "properties": {"scoreId": {"type": "string", "description": "Unique identifier of the musical score"}, "rehearsalId": {"type": "string", "description": "Unique identifier of the rehearsal session"}, "musicianIds": {"type": "array", "items": {"type": "string"}, "description": "List of unique identifiers of the musicians participating in the rehearsal"}, "annotationData": {"type": "object", "description": "Annotations and notes for the score, including timing and expression feedback"}}, "required": ["scoreId", "rehearsalId", "musicianIds", "annotationData"]}}]<|im_end|> | |
| <|im_start|>user | |
| Assist the conductor in annotating and distributing the score with id "symphony123" for the rehearsal session with id "rehearsal456" to musicians with ids "musician1", "musician2", and "musician3" using the annotation data {"timing": "slow", "expression": "more dynamics"}.<|im_end|> | |
| <|im_start|>assistant | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'orchestraRehearsalTool', 'arguments': {'annotationData': {'timing': 'slow', 'expression': 'more dynamics'}, 'scoreId': 'symphony123', 'rehearsalId': 'rehearsal456', 'musicianIds': ['musician1', 'musician2', 'musician3']}}]<|im_end|><|im_end|> | |
| <|im_start|>user | |
| <tool_response> | |
| {'rehearsalSummary': {'scoreId': 'symphony123', 'rehearsalId': 'rehearsal456', 'musicianCount': 3, 'averageTimingAccuracy': 0.85, 'averageExpressionScore': 7.2}, 'musicianFeedback': [{'musicianId': 'musician1', 'timingAccuracy': 0.9, 'expressionScore': 7.5, 'personalizedFeedback': 'Good job on the tempo, focus on adding more contrast in dynamics'}, {'musicianId': 'musician2', 'timingAccuracy': 0.8, 'expressionScore': 6.8, 'personalizedFeedback': 'Work on synchronizing with the rest of the section, and explore deeper tones'}, {'musicianId': 'musician3', 'timingAccuracy': 0.92, 'expressionScore': 7.8, 'personalizedFeedback': 'Excellent timing, consider adding more nuance to your phrasing'}], 'annotationData': {'timing': 'slow', 'expression': 'more dynamics', 'additionalNotes': 'Focus on the transition between movements 2 and 3, and emphasize the crescendo in movement 1'}, 'recommendedAdjustments': {'tempoAdjustment': -0.05, 'expressionEmphasis': 'increased dynamics and articulation', 'sectionalFocus': 'strings and woodwinds'}, 'nextRehearsalRecommendations': {'rehearsalFocus': 'movement 3 and finale', 'musicianGroupsToFocusOn': ['strings', 'brass'], 'scheduledRehearsalTime': '2024-04-15T14:00:00Z'}} | |
| </tool_response><|im_end|> | |
| ==== Final Analysis ==== | |
| <|im_start|>assistant | |
| Tool orchestraRehearsalTool has been executed successfully.<|im_end|> | |
| Press Enter for next example... | |
| ==== Initial Input ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "automateBucketPolicyMonitoring", "description": "Automates the monitoring of AWS bucket policy changes by integrating with AWS CloudTrail and SNS to detect PutBucketPolicy events and trigger notifications based on predefined rules", "parameters": {"type": "object", "properties": {"bucketName": {"type": "string", "description": "The name of the AWS bucket to monitor for policy changes"}, "snsTopicArn": {"type": "string", "description": "The ARN of the SNS topic to send notifications to"}, "cloudTrailTrailName": {"type": "string", "description": "The name of the CloudTrail trail to capture PutBucketPolicy events from"}}, "required": ["bucketName", "snsTopicArn", "cloudTrailTrailName"]}}]<|im_end|> | |
| <|im_start|>user | |
| Automate the monitoring of AWS bucket policy changes for my bucket named "my-sensitive-data" by integrating with the CloudTrail trail named "my-trail" and sending notifications to the SNS topic with ARN "arn:aws:sns:us-east-1:123456789012:my-topic".<|im_end|> | |
| ==== Tool Call ==== | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'automateBucketPolicyMonitoring', 'arguments': {'cloudTrailTrailName': 'my-trail', 'bucketName': 'my-sensitive-data', 'snsTopicArn': 'arn:aws:sns:us-east-1:123456789012:my-topic'}}]<|im_end|> | |
| ==== Second Input (With Tool Response) ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "automateBucketPolicyMonitoring", "description": "Automates the monitoring of AWS bucket policy changes by integrating with AWS CloudTrail and SNS to detect PutBucketPolicy events and trigger notifications based on predefined rules", "parameters": {"type": "object", "properties": {"bucketName": {"type": "string", "description": "The name of the AWS bucket to monitor for policy changes"}, "snsTopicArn": {"type": "string", "description": "The ARN of the SNS topic to send notifications to"}, "cloudTrailTrailName": {"type": "string", "description": "The name of the CloudTrail trail to capture PutBucketPolicy events from"}}, "required": ["bucketName", "snsTopicArn", "cloudTrailTrailName"]}}]<|im_end|> | |
| <|im_start|>user | |
| Automate the monitoring of AWS bucket policy changes for my bucket named "my-sensitive-data" by integrating with the CloudTrail trail named "my-trail" and sending notifications to the SNS topic with ARN "arn:aws:sns:us-east-1:123456789012:my-topic".<|im_end|> | |
| <|im_start|>assistant | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'automateBucketPolicyMonitoring', 'arguments': {'cloudTrailTrailName': 'my-trail', 'bucketName': 'my-sensitive-data', 'snsTopicArn': 'arn:aws:sns:us-east-1:123456789012:my-topic'}}]<|im_end|><|im_end|> | |
| <|im_start|>user | |
| <tool_response> | |
| {'status': 'success', 'message': 'Automated bucket policy monitoring setup complete', 'configuration': {'bucketName': 'my-sensitive-data', 'snsTopicArn': 'arn:aws:sns:us-east-1:123456789012:my-topic', 'cloudTrailTrailName': 'my-trail'}, 'metrics': {'eventDetectionLatency': 1500, 'notificationDeliveryTime': 500, 'cloudTrailEventCount': 10, 'snsNotificationCount': 5}, 'rules': [{'ruleId': 'put-bucket-policy-rule', 'eventName': 'PutBucketPolicy', 'eventSource': 'aws.s3', 'notificationStatus': 'enabled'}], 'cloudTrailTrailDetails': {'trailName': 'my-trail', 'trailArn': 'arn:aws:cloudtrail:us-east-1:123456789012:trail/my-trail', 'homeRegion': 'us-east-1'}, 'snsTopicDetails': {'topicArn': 'arn:aws:sns:us-east-1:123456789012:my-topic', 'topicName': 'my-topic', 'subscriptionCount': 2}} | |
| </tool_response><|im_end|> | |
| ==== Final Analysis ==== | |
| <|im_start|>assistant | |
| Tool automateBucketPolicyMonitoring has been executed successfully.<|im_end|> | |
| Press Enter for next example... | |
| ==== Initial Input ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "designOrigamiSpacecraft", "description": "Simulates and optimizes the design of origami-inspired spacecraft structures for compact storage, efficient deployment, and reliable recovery", "parameters": {"type": "object", "properties": {"structureType": {"type": "string", "description": "Type of origami-inspired structure to design, such as solar panel or antenna"}, "materialProperties": {"type": "object", "description": "Physical properties of the 2D material used for the structure, such as thickness and stiffness"}, "deploymentConstraints": {"type": "object", "description": "Constraints for the deployment of the structure, such as available space and force limits"}}, "required": ["structureType", "materialProperties", "deploymentConstraints"]}}]<|im_end|> | |
| <|im_start|>user | |
| Design an origami-inspired solar panel structure with a material thickness of 0.1 mm and stiffness of 1000 N/m, to be deployed in a cubic space of 1 meter with a maximum force limit of 1000 N.<|im_end|> | |
| ==== Tool Call ==== | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'designOrigamiSpacecraft', 'arguments': {'deploymentConstraints': {'availableSpace': 'cubic', 'forceLimit': 1000}, 'structureType': 'solar panel', 'materialProperties': {'thickness': 0.1, 'stiffness': 1000}}}]<|im_end|> | |
| ==== Second Input (With Tool Response) ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "designOrigamiSpacecraft", "description": "Simulates and optimizes the design of origami-inspired spacecraft structures for compact storage, efficient deployment, and reliable recovery", "parameters": {"type": "object", "properties": {"structureType": {"type": "string", "description": "Type of origami-inspired structure to design, such as solar panel or antenna"}, "materialProperties": {"type": "object", "description": "Physical properties of the 2D material used for the structure, such as thickness and stiffness"}, "deploymentConstraints": {"type": "object", "description": "Constraints for the deployment of the structure, such as available space and force limits"}}, "required": ["structureType", "materialProperties", "deploymentConstraints"]}}]<|im_end|> | |
| <|im_start|>user | |
| Design an origami-inspired solar panel structure with a material thickness of 0.1 mm and stiffness of 1000 N/m, to be deployed in a cubic space of 1 meter with a maximum force limit of 1000 N.<|im_end|> | |
| <|im_start|>assistant | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'designOrigamiSpacecraft', 'arguments': {'deploymentConstraints': {'availableSpace': 'cubic', 'forceLimit': 1000}, 'structureType': 'solar panel', 'materialProperties': {'thickness': 0.1, 'stiffness': 1000}}}]<|im_end|><|im_end|> | |
| <|im_start|>user | |
| <tool_response> | |
| {'structureDesign': {'type': 'solar panel', 'foldPattern': 'miura-ori', 'numPanels': 4, 'panelDimensions': {'length': 1.2, 'width': 0.8}, 'deploymentSequence': [{'step': 1, 'action': 'unfold panel 1', 'forceRequired': 200}, {'step': 2, 'action': 'unfold panel 2', 'forceRequired': 250}, {'step': 3, 'action': 'unfold panel 3', 'forceRequired': 200}, {'step': 4, 'action': 'unfold panel 4', 'forceRequired': 250}]}, 'performanceMetrics': {'compactnessRatio': 0.8, 'deploymentTime': 120, 'structuralIntegrity': 0.95, 'powerGeneration': 200}, 'materialUsage': {'totalMaterialUsed': 1.5, 'materialWaste': 0.1}, 'optimizationResults': {'optimumThickness': 0.12, 'optimumStiffness': 1200}} | |
| </tool_response><|im_end|> | |
| ==== Final Analysis ==== | |
| <|im_start|>assistant | |
| Tool designOrigamiSpacecraft has been executed successfully.<|im_end|> | |
| Press Enter for next example... | |
| ==== Initial Input ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "verifyS3Replication", "description": "Automates the comparison of object metadata across multiple AWS S3 buckets to detect replication lag and inconsistencies", "parameters": {"type": "object", "properties": {"sourceBucket": {"type": "string", "description": "The name of the source S3 bucket"}, "targetBucket": {"type": "string", "description": "The name of the target S3 bucket"}, "regions": {"type": "array", "items": {"type": "string"}, "description": "A list of AWS regions where the S3 buckets are located"}, "threshold": {"type": "integer", "description": "The maximum allowed replication lag in seconds"}, "notificationEmail": {"type": "string", "description": "The email address to send alerts to in case of replication lag or inconsistencies"}}, "required": ["sourceBucket", "targetBucket", "regions", "threshold", "notificationEmail"]}}]<|im_end|> | |
| <|im_start|>user | |
| Verify S3 replication between source bucket "my-source-bucket" and target bucket "my-target-bucket" across regions "us-east-1" and "us-west-2" with a threshold of 300 seconds and send notifications to "admin@example.com".<|im_end|> | |
| ==== Tool Call ==== | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'verifyS3Replication', 'arguments': {'notificationEmail': 'admin@example.com', 'sourceBucket': 'my-source-bucket', 'targetBucket': 'my-target-bucket', 'regions': ['us-east-1', 'us-west-2'], 'threshold': 300}}]<|im_end|> | |
| ==== Second Input (With Tool Response) ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "verifyS3Replication", "description": "Automates the comparison of object metadata across multiple AWS S3 buckets to detect replication lag and inconsistencies", "parameters": {"type": "object", "properties": {"sourceBucket": {"type": "string", "description": "The name of the source S3 bucket"}, "targetBucket": {"type": "string", "description": "The name of the target S3 bucket"}, "regions": {"type": "array", "items": {"type": "string"}, "description": "A list of AWS regions where the S3 buckets are located"}, "threshold": {"type": "integer", "description": "The maximum allowed replication lag in seconds"}, "notificationEmail": {"type": "string", "description": "The email address to send alerts to in case of replication lag or inconsistencies"}}, "required": ["sourceBucket", "targetBucket", "regions", "threshold", "notificationEmail"]}}]<|im_end|> | |
| <|im_start|>user | |
| Verify S3 replication between source bucket "my-source-bucket" and target bucket "my-target-bucket" across regions "us-east-1" and "us-west-2" with a threshold of 300 seconds and send notifications to "admin@example.com".<|im_end|> | |
| <|im_start|>assistant | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'verifyS3Replication', 'arguments': {'notificationEmail': 'admin@example.com', 'sourceBucket': 'my-source-bucket', 'targetBucket': 'my-target-bucket', 'regions': ['us-east-1', 'us-west-2'], 'threshold': 300}}]<|im_end|><|im_end|> | |
| <|im_start|>user | |
| <tool_response> | |
| {'verificationResult': {'status': 'partial_failure', 'summary': {'totalObjects': 1000, 'consistentObjects': 900, 'inconsistentObjects': 50, 'replicationLag': {'average': 120, 'max': 250}}, 'regionResults': [{'region': 'us-east-1', 'consistentObjects': 500, 'inconsistentObjects': 20, 'replicationLag': {'average': 100, 'max': 200}}, {'region': 'us-west-2', 'consistentObjects': 400, 'inconsistentObjects': 30, 'replicationLag': {'average': 140, 'max': 250}}], 'alerts': [{'type': 'replication_lag', 'message': 'Replication lag exceeded threshold of 300 seconds in us-west-2 region', 'details': {'region': 'us-west-2', 'maxLag': 250}}, {'type': 'inconsistency_detected', 'message': 'Inconsistencies detected in 50 objects across all regions', 'details': {'totalInconsistentObjects': 50}}]}} | |
| </tool_response><|im_end|> | |
| ==== Final Analysis ==== | |
| <|im_start|>assistant | |
| Tool verifyS3Replication has been executed successfully.<|im_end|> | |
| Press Enter for next example... | |
| ==== Initial Input ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "analyzeRouteDifficulty", "description": "Analyzes the difficulty of a climbing route and suggests a personalized climb sequence based on the climber's style, abilities, and equipment", "parameters": {"type": "object", "properties": {"routeId": {"type": "string", "description": "Unique identifier of the climbing route"}, "climberId": {"type": "string", "description": "Unique identifier of the climber"}, "equipment": {"type": "array", "items": {"type": "string"}, "description": "List of equipment used by the climber"}, "injuryStatus": {"type": "boolean", "description": "Indicates whether the climber has any injuries"}}, "required": ["routeId", "climberId"]}}]<|im_end|> | |
| <|im_start|>user | |
| Analyze the difficulty of route "Eldorado" for climber "JohnDoe" with equipment "rope, harness, chalk" and considering his current injury status as "false".<|im_end|> | |
| ==== Tool Call ==== | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'analyzeRouteDifficulty', 'arguments': {'injuryStatus': False, 'routeId': 'Eldorado', 'climberId': 'JohnDoe', 'equipment': ['rope', 'harness', 'chalk']}}]<|im_end|> | |
| ==== Second Input (With Tool Response) ==== | |
| <|im_start|>system | |
| You are a help assistant. You are helping a user execute a tool<|im_end|> | |
| <|im_start|>system | |
| Available tool: [{"name": "analyzeRouteDifficulty", "description": "Analyzes the difficulty of a climbing route and suggests a personalized climb sequence based on the climber's style, abilities, and equipment", "parameters": {"type": "object", "properties": {"routeId": {"type": "string", "description": "Unique identifier of the climbing route"}, "climberId": {"type": "string", "description": "Unique identifier of the climber"}, "equipment": {"type": "array", "items": {"type": "string"}, "description": "List of equipment used by the climber"}, "injuryStatus": {"type": "boolean", "description": "Indicates whether the climber has any injuries"}}, "required": ["routeId", "climberId"]}}]<|im_end|> | |
| <|im_start|>user | |
| Analyze the difficulty of route "Eldorado" for climber "JohnDoe" with equipment "rope, harness, chalk" and considering his current injury status as "false".<|im_end|> | |
| <|im_start|>assistant | |
| <|im_start|>assistant | |
| Tool call: [{'name': 'analyzeRouteDifficulty', 'arguments': {'injuryStatus': False, 'routeId': 'Eldorado', 'climberId': 'JohnDoe', 'equipment': ['rope', 'harness', 'chalk']}}]<|im_end|><|im_end|> | |
| <|im_start|>user | |
| <tool_response> | |
| {'routeId': 'Eldorado', 'climberId': 'JohnDoe', 'difficultyAnalysis': {'overallDifficulty': 7.5, 'technicalDifficulty': 8.2, 'physicalDifficulty': 7.0, 'sequenceSuggestions': [{'sequenceId': 1, 'moves': [{'moveId': 1, 'holdType': 'crimp', 'angle': 45, 'distance': 2.5}, {'moveId': 2, 'holdType': 'slopers', 'angle': 60, 'distance': 1.8}], 'difficulty': 7.8}, {'sequenceId': 2, 'moves': [{'moveId': 1, 'holdType': 'pockets', 'angle': 30, 'distance': 2.2}, {'moveId': 2, 'holdType': 'pinches', 'angle': 50, 'distance': 2.0}], 'difficulty': 7.2}]}, 'personalizedRecommendations': {'optimalSequence': 1, 'recommendedEquipment': ['rope', 'harness', 'chalk', 'climbing shoes'], 'warmUpRoutine': [{'exerciseId': 1, 'exerciseName': 'fingerboard', 'duration': 10}, {'exerciseId': 2, 'exerciseName': 'campus board', 'duration': 8}]}, 'injuryRiskAssessment': {'riskLevel': 'low', 'recommendedPrecautions': ['warm up thoroughly', 'use proper footwork']}} | |
| </tool_response><|im_end|> | |
| ==== Final Analysis ==== | |
| <|im_start|>assistant | |
| Tool analyzeRouteDifficulty has been executed successfully.<|im_end|> | |
| Press Enter for next example... |
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