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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+embeddings.snapshot filter=lfs diff=lfs merge=lfs -text
+scores.db filter=lfs diff=lfs merge=lfs -text
+embedding-model/model.safetensors filter=lfs diff=lfs merge=lfs -text

README.md

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+# pulze-intent-v0.1
+
+Intent-tuned LLM router that selects the best LLM for a user query. Use with [knn-router](https://github.com/pulzeai-oss/knn-router).
+
+## Models
+
+- claude-3-haiku-20240307
+- claude-3-opus-20240229
+- claude-3-sonnet-20240229
+- command-r
+- command-r-plus
+- dbrx-instruct
+- gpt-3.5-turbo-0125
+- gpt-4-turbo-2024-04-09
+- llama-3-70b-instruct
+- mistral-large
+- mistral-medium
+- mistral-small
+- mixtral-8x7b-instruct
+
+## Data
+
+### Prompts and Intent Categories
+Prompt and intent categories are derived from the [GAIR-NLP/Auto-J scenario classification dataset](https://github.com/GAIR-NLP/auto-j/blob/2ae17a3965d933232e9cd50302aa0f176249c83b/README.md?plain=1#L582).
+
+Citation:
+```
+@article{li2023generative,
+  title={Generative Judge for Evaluating Alignment},
+  author={Li, Junlong and Sun, Shichao and Yuan, Weizhe and Fan, Run-Ze and Zhao, Hai and Liu, Pengfei},
+  journal={arXiv preprint arXiv:2310.05470},
+  year={2023}
+}
+```
+
+### Response Evaluation
+
+Candidate model responses were evaluated pairwise using `openai/gpt-4-turbo-2024-04-09`, with the following prompt:
+
+```
+You are an expert, impartial judge tasked with evaluating the quality of responses generated by two AI assistants.
+
+Think step by step, and evaluate the responses, <response1> and <response2> to the instruction, <instruction>. Follow these guidelines:
+- Avoid any position bias and ensure that the order in which the responses were presented does not influence your judgement
+- Do not allow the length of the responses to influence your judgement - a concise response can be as effective as a longer one
+- Consider factors such as adherence to the given instruction, helpfulness, relevance, accuracy, depth, creativity, and level of detail
+- Be as objective as possible
+
+Make your decision on which of the two responses is better for the given instruction from the following choices:
+If <response1> is better, use "1".
+If <response2> is better, use "2".
+If both answers are equally good, use "0".
+If both answers are equally bad, use "0".
+
+<instruction>
+{INSTRUCTION}
+</instruction>
+
+<response1>
+{RESPONSE1}
+</response1>
+
+<response2>
+{RESPONSE2}
+</response2>
+```
+
+Each pair of models is subject to 2 matches, with the positions of the respective responses swapped in the evaluation prompt. A model is considered a winner only if 
+it wins both matches.
+
+For each prompt, we then compute Bradley-Terry scores for the respective models using the same [method](https://github.com/lm-sys/FastChat/blob/f2e6ca964af7ad0585cadcf16ab98e57297e2133/fastchat/serve/monitor/elo_analysis.py#L57) as that used in the [LMSYS Chatbot Arena Leaderboard](https://chat.lmsys.org/?leaderboard). Finally, we normalize all scores to a scale from 0 to 1 for interoperability with other weighted ranking systems.

embedding-model/1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

embedding-model/README.md

@@ -0,0 +1,57 @@
+---
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+
+---
+
+# {MODEL_NAME}
+
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
+
+<!--- Describe your model here -->
+
+## Usage (Sentence-Transformers)
+
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
+
+```
+pip install -U sentence-transformers
+```
+
+Then you can use the model like this:
+
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
+
+model = SentenceTransformer('{MODEL_NAME}')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
+
+
+
+## Evaluation Results
+
+<!--- Describe how your model was evaluated -->
+
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
+
+
+
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Citing & Authors
+
+<!--- Describe where people can find more information -->

embedding-model/config.json

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+{
+  "_name_or_path": "/tmp/kg/fine-tuning-2-0.6-cocktail",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
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+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
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+  "intermediate_size": 3072,
+  "label2id": {
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+  },
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+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.37.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

embedding-model/config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "2.5.1",
+    "transformers": "4.37.2",
+    "pytorch": "2.2.0+cu121"
+  },
+  "prompts": {},
+  "default_prompt_name": null
+}

embedding-model/model.safetensors

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embedding-model/modules.json

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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
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+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

embedding-model/sentence_bert_config.json

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+{
+  "max_seq_length": 512,
+  "do_lower_case": false
+}

embedding-model/special_tokens_map.json

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+{
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

embedding-model/tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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+    }
+  },
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+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

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