MiniLMv2-agentflow-v2-onnx

MiniLMv2-agentflow-v2-onnx/.gitattributes

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MiniLMv2-agentflow-v2-onnx/README.md

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+---
+
+language:
+- en
+license: apache-2.0
+inference: false
+tags:
+- text-classification
+- onnx
+- int8
+- optimum
+- ONNXRuntime
+---
+# LLM agent flow text classification
+
+This model identifies common LLM agent events and patterns within the conversation flow. 
+Such events include an apology, where the LLM acknowledges a mistake.
+The flow labels can serve as foundational elements for sophisticated LLM analytics.
+
+It is ONNX quantized and is a fined-tune of [MiniLMv2-L6-H384](https://huggingface.co/nreimers/MiniLMv2-L6-H384-distilled-from-RoBERTa-Large).
+The base model can be found [here](https://huggingface.co/minuva/MiniLMv2-agentflow-v2)
+
+This model is *only* for the LLM agent texts in the dialog. For the user texts [use this model](https://huggingface.co/minuva/MiniLMv2-userflow-v2-onnx/).
+
+
+# Optimum
+
+## Installation
+
+Install from source: 
+```bash
+python -m pip install optimum[onnxruntime]@git+https://github.com/huggingface/optimum.git
+```
+
+
+## Run the Model
+```py
+from optimum.onnxruntime import ORTModelForSequenceClassification
+from transformers import AutoTokenizer, pipeline
+
+model = ORTModelForSequenceClassification.from_pretrained('minuva/MiniLMv2-agentflow-v2-onnx', provider="CPUExecutionProvider")
+tokenizer = AutoTokenizer.from_pretrained('minuva/MiniLMv2-agentflow-v2-onnx', use_fast=True, model_max_length=256, truncation=True, padding='max_length')
+
+pipe = pipeline(task='text-classification', model=model, tokenizer=tokenizer, )
+texts = ["My apologies", "Im not sure what you mean"]
+pipe(texts)
+# [{'label': 'agent_apology_error_mistake', 'score': 0.9967106580734253},
+# {'label': 'agent_didnt_understand', 'score': 0.9975798726081848}]
+```
+
+# ONNX Runtime only
+
+A lighter solution for deployment
+
+
+## Installation
+```bash
+pip install tokenizers
+pip install onnxruntime
+git clone https://huggingface.co/minuva/MiniLMv2-agentflow-v2-onnx
+```
+
+## Run the Model
+
+```py
+import os
+import numpy as np
+import json
+
+from tokenizers import Tokenizer
+from onnxruntime import InferenceSession
+
+
+model_name = "minuva/MiniLMv2-agentflow-v2-onnx"
+
+tokenizer = Tokenizer.from_pretrained(model_name)
+tokenizer.enable_padding(
+    pad_token="<pad>",
+    pad_id=1,
+)
+tokenizer.enable_truncation(max_length=256)
+batch_size = 16
+
+texts = ["thats my mistake"]
+outputs = []
+model = InferenceSession("MiniLMv2-agentflow-v2-onnx/model_optimized_quantized.onnx", providers=['CPUExecutionProvider'])
+
+with open(os.path.join("MiniLMv2-agentflow-v2-onnx", "config.json"), "r") as f:
+            config = json.load(f)
+
+output_names = [output.name for output in model.get_outputs()]
+input_names = [input.name for input in model.get_inputs()]
+
+for subtexts in np.array_split(np.array(texts), len(texts) // batch_size + 1):
+            encodings = tokenizer.encode_batch(list(subtexts))
+            inputs = {
+                "input_ids": np.vstack(
+                    [encoding.ids for encoding in encodings],
+                ),
+                "attention_mask": np.vstack(
+                    [encoding.attention_mask for encoding in encodings],
+                ),
+                "token_type_ids": np.vstack(
+                    [encoding.type_ids for encoding in encodings],
+                ),
+            }
+
+            for input_name in input_names:
+                if input_name not in inputs:
+                    raise ValueError(f"Input name {input_name} not found in inputs")
+
+            inputs = {input_name: inputs[input_name] for input_name in input_names}
+            output = np.squeeze(
+                np.stack(
+                    model.run(output_names=output_names, input_feed=inputs)
+                ),
+                axis=0,
+            )
+            outputs.append(output)
+
+outputs = np.concatenate(outputs, axis=0)
+scores = 1 / (1 + np.exp(-outputs))
+results = []
+for item in scores:
+    labels = []
+    scores = []
+    for idx, s in enumerate(item):
+        labels.append(config["id2label"][str(idx)])
+        scores.append(float(s))
+    results.append({"labels": labels, "scores": scores})
+
+
+res = []
+
+for result in results:
+    joined = list(zip(result['labels'], result['scores']))
+    max_score = max(joined, key=lambda x: x[1])    
+    res.append(max_score)
+
+res
+# [('agent_apology_error_mistake', 0.9991968274116516),
+# ('agent_didnt_understand', 0.9993669390678406)]
+```
+
+# Categories Explanation
+
+<details>
+  <summary>Click to expand!</summary>
+  
+    - OTHER: Responses or actions by the agent that do not fit into the predefined categories or are outside the scope of the specific interactions listed.
+
+    - agent_apology_error_mistake: When the agent acknowledges an error or mistake in the information provided or in the handling of the request.
+
+    - agent_apology_unsatisfactory: The agent expresses an apology for providing an unsatisfactory response or for any dissatisfaction experienced by the user.
+
+    - agent_didnt_understand: Indicates that the agent did not understand the user's request or question.
+
+    - agent_limited_capabilities: The agent communicates its limitations in addressing certain requests or providing certain types of information.
+
+    - agent_refuses_answer: When the agent explicitly refuses to answer a question or fulfill a request, due to policy restrictions or ethical considerations.
+
+    - image_limitations": The agent points out limitations related to handling or interpreting images.
+
+    - no_information_doesnt_know": The agent indicates that it has no information available or does not know the answer to the user's question.
+
+    - success_and_followup_assistance": The agent successfully provides the requested information or service and offers further assistance or follow-up actions if needed.
+</details>
+
+<br>
+
+
+# Metrics in our private test dataset
+| Model (params)    |    Loss      |    Accuracy |  F1 |
+|--------------------|-------------|----------|--------| 
+| minuva/MiniLMv2-agentflow-v2 (33M) |   0.1462 | 0.9616 |  0.9618 |
+| minuva/MiniLMv2-agentflow-v2-onnx (33M) |   -  |  0.9624 | 0.9626  |
+
+# Deployment
+
+Check our [llm-flow-classification repository](https://github.com/minuva/llm-flow-classification) for a FastAPI and ONNX based server to deploy this model on CPU devices.

MiniLMv2-agentflow-v2-onnx/config.json

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+{
+  "_name_or_path": "../output/MiniLMv2-agentflow-v2.1-opt",
+  "architectures": [
+    "RobertaForSequenceClassification"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "classifier_dropout": null,
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "OTHER",
+    "1": "agent_apology_error_mistake",
+    "2": "agent_apology_unsatisfactory",
+    "3": "agent_didnt_understand",
+    "4": "agent_limited_capabilities",
+    "5": "agent_refuses_answer",
+    "6": "image_limitations",
+    "7": "no_information_doesnt_know",
+    "8": "success_and_followup_assistance"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "OTHER": 0,
+    "agent_apology_error_mistake": 1,
+    "agent_apology_unsatisfactory": 2,
+    "agent_didnt_understand": 3,
+    "agent_limited_capabilities": 4,
+    "agent_refuses_answer": 5,
+    "image_limitations": 6,
+    "no_information_doesnt_know": 7,
+    "success_and_followup_assistance": 8
+  },
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "roberta",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 6,
+  "pad_token_id": 1,
+  "position_embedding_type": "absolute",
+  "problem_type": "single_label_classification",
+  "torch_dtype": "float32",
+  "transformers_version": "4.30.0",
+  "type_vocab_size": 1,
+  "use_cache": true,
+  "vocab_size": 50265
+}

MiniLMv2-agentflow-v2-onnx/model_optimized_quantized.onnx

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MiniLMv2-agentflow-v2-onnx/ort_config.json

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+{
+  "one_external_file": true,
+  "opset": null,
+  "optimization": {},
+  "optimum_version": "1.16.1",
+  "quantization": {
+    "activations_dtype": "QUInt8",
+    "activations_symmetric": false,
+    "format": "QOperator",
+    "is_static": false,
+    "mode": "IntegerOps",
+    "nodes_to_exclude": [],
+    "nodes_to_quantize": [],
+    "operators_to_quantize": [
+      "Conv",
+      "MatMul",
+      "Attention",
+      "LSTM",
+      "Gather",
+      "Transpose",
+      "EmbedLayerNormalization"
+    ],
+    "per_channel": false,
+    "qdq_add_pair_to_weight": false,
+    "qdq_dedicated_pair": false,
+    "qdq_op_type_per_channel_support_to_axis": {
+      "MatMul": 1
+    },
+    "reduce_range": false,
+    "weights_dtype": "QInt8",
+    "weights_symmetric": true
+  },
+  "transformers_version": "4.30.0",
+  "use_external_data_format": false
+}

MiniLMv2-agentflow-v2-onnx/source.txt

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+https://huggingface.co/minuva/MiniLMv2-agentflow-v2-onnx

MiniLMv2-agentflow-v2-onnx/special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
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+    "lstrip": false,
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+    "rstrip": false,
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+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": true,
+    "rstrip": false,
+    "single_word": false
+  }
+}

MiniLMv2-agentflow-v2-onnx/tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": true,
+      "rstrip": false,
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+      "special": true
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+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "<s>",
+  "eos_token": "</s>",
+  "errors": "replace",
+  "mask_token": "<mask>",
+  "max_length": 256,
+  "model_max_length": 512,
+  "pad_token": "<pad>",
+  "sep_token": "</s>",
+  "stride": 0,
+  "tokenizer_class": "RobertaTokenizer",
+  "trim_offsets": true,
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "<unk>"
+}