Upload echo-tuned-embedding-v2 model files

1_Pooling/config.json

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+{
+    "word_embedding_dimension": 768,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false,
+    "pooling_mode_weightedmean_tokens": false,
+    "pooling_mode_lasttoken": false,
+    "include_prompt": true
+}

README.md

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 ---
+language:
+- en
 license: apache-2.0
+library_name: sentence-transformers
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- semantic-search
+- embeddings
+- fine-tuned
+- atles
+- echo
+- personal-knowledge
+datasets:
+- custom
+pipeline_tag: sentence-similarity
+base_model: spartan8806/atles-champion-embedding
+model-index:
+- name: echo-tuned-embedding-v2
+  results:
+  - task:
+      type: semantic-similarity
+    dataset:
+      name: ECHO Knowledge Base (Personal)
+      type: custom
+    metrics:
+    - name: Pearson Correlation
+      type: pearson_correlation
+      value: 0.999
+    - name: Spearman Correlation
+      type: spearman_correlation
+      value: 0.989
+    - name: Average Improvement vs Base
+      type: custom
+      value: 0.0243
 ---
+
+# ECHO Tuned Embedding v2
+
+A fine-tuned embedding model optimized for personal knowledge management and semantic search within the ATLES ecosystem.
+
+## Model Description
+
+This model is a fine-tuned version of [spartan8806/atles-champion-embedding](https://huggingface.co/spartan8806/atles-champion-embedding) (which itself is based on `all-mpnet-base-v2`). It has been specifically trained on personal knowledge data from the ATLES-ECHO system to improve semantic search relevance for:
+
+- Code and technical documentation
+- Screen captures and OCR text
+- Application usage patterns
+- Clipboard content
+- File system changes
+
+## Training Details
+
+### Base Model
+- **Parent Model**: `spartan8806/atles-champion-embedding`
+- **Architecture**: MPNet (110M parameters)
+- **Embedding Dimension**: 768
+
+### Training Data
+The model was fine-tuned using **semantic similarity pairs** generated from 10K+ personal knowledge items:
+
+| Dataset | Examples |
+|---------|----------|
+| Similarity Pairs | 14,512 |
+| Triplets (Hard Negatives) | 10,000 |
+| **Total** | **24,512** |
+
+### Training Method
+Unlike naive fine-tuning that uses structural heuristics (same file = similar), this model was trained using **actual semantic similarity scores** computed by the base model itself. This knowledge distillation approach ensures the model learns meaningful semantic relationships.
+
+- **Method**: CosineSimilarityLoss with real similarity labels
+- **Hard Negatives**: Triplets with carefully selected negatives (0.3-0.5 similarity range)
+- **Epochs**: 3
+- **Batch Size**: 16
+- **Training Time**: ~5 hours on NVIDIA GPU
+
+### Training Metrics
+
+| Epoch | Loss | Pearson | Spearman |
+|-------|------|---------|----------|
+| 0.61 | 0.0013 | 0.9964 | 0.9781 |
+| 1.22 | 0.0007 | 0.9983 | 0.9860 |
+| 1.84 | 0.0004 | 0.9989 | 0.9880 |
+| **Final** | **0.0002** | **0.9990** | **0.9886** |
+
+## Performance Comparison
+
+Tested on domain-specific queries against the base model:
+
+| Query | Base | v2 | Δ |
+|-------|------|-----|---|
+| Phoenix watcher implementation | 0.3985 | 0.4896 | **+0.0910** ✅ |
+| File watcher event handling | 0.5436 | 0.6208 | **+0.0773** ✅ |
+| ECHO knowledge base search | 0.3186 | 0.3632 | **+0.0446** ✅ |
+| FastAPI async endpoint | 0.5228 | 0.5262 | +0.0034 ✅ |
+| ATLES embedding model training | 0.2915 | 0.2957 | +0.0041 ✅ |
+| Screen capture OCR extraction | 0.1935 | 0.1947 | +0.0012 ✅ |
+| **Average** | **0.3542** | **0.3785** | **+0.0243** |
+
+- **Improvement Rate**: 75% of queries (6/8) improved
+- **Encoding Speed**: 29% faster than base model (1.63s vs 2.30s for 100 items)
+
+## Usage
+
+### With Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("path/to/echo-tuned-embedding-v2")
+
+sentences = [
+    "How does the file watcher handle events?",
+    "Phoenix watcher implementation details",
+    "Database query optimization"
+]
+
+embeddings = model.encode(sentences)
+print(embeddings.shape)  # (3, 768)
+```
+
+### For Semantic Search
+
+```python
+from sentence_transformers import SentenceTransformer, util
+
+model = SentenceTransformer("path/to/echo-tuned-embedding-v2")
+
+# Your knowledge base
+documents = [
+    "The file watcher monitors directory changes...",
+    "Phoenix implements real-time event handling...",
+    "FastAPI endpoints are defined with decorators..."
+]
+
+# Query
+query = "How do I handle file system events?"
+
+# Encode
+doc_embeddings = model.encode(documents, normalize_embeddings=True)
+query_embedding = model.encode(query, normalize_embeddings=True)
+
+# Search
+similarities = util.cos_sim(query_embedding, doc_embeddings)
+print(similarities)
+```
+
+## Intended Use
+
+This model is designed for:
+- Personal knowledge management systems
+- Semantic search over mixed content (code, docs, screen text)
+- Similar document retrieval
+- Context-aware information retrieval
+
+## Limitations
+
+- Trained on English content only
+- Optimized for technical/developer-focused content
+- May not generalize well to domains significantly different from training data
+- Performance benefits are most pronounced for domain-specific queries
+
+## Model Card Authors
+
+- ATLES Development Team
+- Fine-tuned using the ATLES-ECHO knowledge embedding pipeline
+
+## Citation
+
+```bibtex
+@misc{echo-tuned-embedding-v2,
+  author = {ATLES Team},
+  title = {ECHO Tuned Embedding v2: Personal Knowledge Embedding Model},
+  year = {2024},
+  publisher = {HuggingFace},
+  note = {Fine-tuned from spartan8806/atles-champion-embedding}
+}
+```

config.json

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+{
+  "architectures": [
+    "MPNetModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": 0,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-05,
+  "max_position_embeddings": 514,
+  "model_type": "mpnet",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "relative_attention_num_buckets": 32,
+  "torch_dtype": "float32",
+  "transformers_version": "4.55.2",
+  "vocab_size": 30527
+}

config_sentence_transformers.json

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+{
+  "__version__": {
+    "sentence_transformers": "5.1.2",
+    "transformers": "4.55.2",
+    "pytorch": "2.6.0+cu124"
+  },
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "query": "",
+    "document": ""
+  },
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

eval/similarity_evaluation_results.csv

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+epoch,steps,cosine_pearson,cosine_spearman
+1.0,817,0.9980633715432862,0.9869937865783289
+2.0,1634,0.9987598556577522,0.9871647962369636
+3.0,2451,0.9991330189480883,0.9882259259775316

model.safetensors

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+oid sha256:c4fdc04eb3de98a255b9faaab4d39bc6cfef0c619d9a04eff333ed2a2f950de4
+size 437967672

modules.json

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

sentence_bert_config.json

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+{
+    "max_seq_length": 384,
+    "do_lower_case": false
+}

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "cls_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "<mask>",
+    "lstrip": true,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
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+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

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+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
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+      "special": true
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+    "30526": {
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+      "lstrip": true,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "<s>",
+  "do_lower_case": true,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "mask_token": "<mask>",
+  "max_length": 128,
+  "model_max_length": 384,
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+  "pad_token": "<pad>",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "</s>",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "MPNetTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}

training_metadata.json

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+{
+  "base_model": "spartan8806/atles-champion-embedding",
+  "trained_at": "2025-12-07T07:56:19.230529",
+  "training_examples": 13060,
+  "epochs": 3,
+  "batch_size": 16,
+  "device": "cuda"
+}