初始化项目，由ModelHub XC社区提供模型
Model: QuantaSparkLabs/NYXIS-Pro Source: Original Platform
This commit is contained in:
ModelHub XC
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- rag
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- retrieval-augmented-generation
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- knowledge-base
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- emotion-detection
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- dual-stage-retrieval
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- coding
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- math
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- science
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- history
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- nyxis
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- quantasparklabs
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pipeline_tag: text-generation
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base_model:
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- QuantaSparkLabs/NYXIS-1.1B
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library_name: transformers
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datasets:
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- QuantaSparkLabs/NYXIS-AEGIS-Knowledge
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---
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<p align="center">
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<img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/preview imgagee.png"
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alt="NYXIS Logo"
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width="160"
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height="160"
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style="border-radius: 50%; object-fit: cover;">
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</p>
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<p align="center">
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<img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/logoname.png"
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alt="NYXIS Name"
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width="700"
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style="border-radius: 18px;">
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</p>
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# NYXIS-PRO 🛡️
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<p align="center">
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<a href="https://huggingface.co/QuantaSparkLabs/NYXIS-1.1B"><img src="https://img.shields.io/badge/Base-NYXIS--1.1B-purple" alt="Base Model"></a>
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<a href="https://huggingface.co/QuantaSparkLabs/NYXIS-AEGIS-KB"><img src="https://img.shields.io/badge/Knowledge-AEGIS-blue" alt="AEGIS KB"></a>
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<a href="#"><img src="https://img.shields.io/badge/Retrieval-Dual%20Stage-green" alt="Dual Stage"></a>
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<a href="#"><img src="https://img.shields.io/badge/Emotion-1--10%20Scale-orange" alt="Emotion Engine"></a>
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<a href="https://www.apache.org/licenses/LICENSE-2.0"><img src="https://img.shields.io/badge/License-Apache%202.0-yellow" alt="License"></a>
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</p>
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**The all-in-one reasoning engine with the AEGIS knowledge shield.**
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NYXIS-PRO combines the NYXIS1.1B base model with a curated 5,000+ chunk knowledge base covering coding, math, science, history, and common knowledge. A dual‑stage retriever grounds every factual answer in verified data — **zero hallucination.**
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## 🧠 What Makes It Different
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| Feature | Description |
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|---------|-------------|
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| **AEGIS Knowledge Base** | 5,426 curated Wikipedia, Trivia QA, and ArXiv chunks |
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| **Dual‑Stage Retriever** | Dense recall (bge‑small) + Cross‑encoder rerank (ms‑marco‑MiniLM) |
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| **Emotion Engine** | Detects user emotion on a 1‑10 scale and matches tone |
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| **4‑Tier Knowledge Cascade** | AEGIS → External Search → Model Brain → Honest Fallback |
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| **Zero Hallucination** | If it doesn't know, it says so — no fabrication |
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## 🚀 Quick Start
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```python
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from huggingface_hub import snapshot_download
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import sys
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model_dir = snapshot_download("QuantaSparkLabs/NYXIS-Pro")
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sys.path.insert(0, model_dir)
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from pipeline import NYXISPro
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nyxis = NYXISPro(model_dir)
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result = nyxis.generate("What is a binary search tree?")
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print(result["response"])
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```
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> **Requirements:** `sentence-transformers`, `faiss-cpu`, `transformers`, `accelerate`, `bitsandbytes`
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> **VRAM:** ~2.1 GB (4‑bit) | **Hardware:** T4 or better
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## 📦 What's Inside
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| Component | Model | Purpose |
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|-----------|-------|---------|
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| **Base LLM** | NYXIS1.1B) | Text generation |
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| **Dense Retriever** | bge‑small‑en‑v1.5 | Initial candidate recall |
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| **Cross‑Encoder** | ms‑marco‑MiniLM‑L‑6‑v2 | Precision re‑ranking |
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| **FAISS Index** | 5,426 chunks | Knowledge storage |
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| **Emotion Engine** | Custom keyword‑based | Tone matching |
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## 🤖 Emotion Intelligence
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NYXIS-PRO detects your emotional state from text and adjusts its personality:
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| Level | Emotion | Response Style |
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|:---:|---------|----------------|
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| 1‑3 | Sad / Angry | Gentle, supportive, empathetic |
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| 4‑6 | Neutral | Balanced, warm, helpful |
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| 7‑9 | Happy / Excited | Energetic, playful |
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| 10 | Overjoyed | Celebratory |
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## 🛡️ Knowledge Cascade
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When you ask a factual question, NYXIS-PRO follows a strict 4‑tier protocol:
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1. **AEGIS** — Searches internal verified knowledge base
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2. **External Search** — Falls back to web search (user‑provided API)
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3. **Model Brain** — Uses training data with honesty guard
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4. **Honest Fallback** — Admits "I don't have enough information"
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It **never fabricates** an answer.
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## Limitations
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- Knowledge base covers ~5,400 chunks — not infinite
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- Emotion detection is keyword‑based, not deep sentiment analysis
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- External search requires user‑provided API function
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- English‑only, 1.5B model size limits complex reasoning
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## License
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Apache‑2.0
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---
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*Built with 🛡️ by QuantaSparkLabs*
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{
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"embedding_dimension": 384,
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"pooling_mode": "cls",
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"include_prompt": true
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}
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{
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"add_cross_attention": false,
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"architectures": [
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"BertModel"
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],
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"attention_probs_dropout_prob": 0.1,
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"bos_token_id": null,
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"dtype": "float32",
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"hidden_act": "gelu",
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"hidden_dropout_prob": 0.1,
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"hidden_size": 384,
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"id2label": {
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"0": "LABEL_0"
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},
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"initializer_range": 0.02,
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"intermediate_size": 1536,
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"is_decoder": false,
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"label2id": {
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"layer_norm_eps": 1e-12,
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"max_position_embeddings": 512,
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"model_type": "bert",
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"num_attention_heads": 12,
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"num_hidden_layers": 12,
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"pad_token_id": 0,
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"position_embedding_type": "absolute",
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"tie_word_embeddings": true,
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"transformers_version": "5.0.0",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 30522
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}
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aegis_embedder/config_sentence_transformers.json
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{
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"__version__": {
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"pytorch": "2.10.0+cu128",
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"sentence_transformers": "5.4.1",
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"transformers": "5.0.0"
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},
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"default_prompt_name": null,
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"model_type": "SentenceTransformer",
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"prompts": {
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"document": "",
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"query": ""
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},
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"similarity_fn_name": "cosine"
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}
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version https://git-lfs.github.com/spec/v1
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oid sha256:ba3eeb8008783b2a8e9aeb9415a511d9aa3bdfec2dbbdfe8e4bbf5bdde283c6b
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size 133462104
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[
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{
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"idx": 0,
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"name": "0",
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"path": "",
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"type": "sentence_transformers.base.modules.transformer.Transformer"
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},
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{
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"idx": 1,
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"name": "1",
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"path": "1_Pooling",
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"type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
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},
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{
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"idx": 2,
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"name": "2",
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"path": "2_Normalize",
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"type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
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}
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]
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{
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"transformer_task": "feature-extraction",
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"modality_config": {
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"text": {
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"method": "forward",
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"method_output_name": "last_hidden_state"
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}
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},
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"module_output_name": "token_embeddings"
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}
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aegis_embedder/tokenizer.json
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{
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"backend": "tokenizers",
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"clean_up_tokenization_spaces": true,
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"cls_token": "[CLS]",
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"do_basic_tokenize": true,
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"do_lower_case": true,
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"is_local": false,
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"mask_token": "[MASK]",
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"model_max_length": 512,
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"never_split": null,
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"pad_token": "[PAD]",
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"sep_token": "[SEP]",
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"strip_accents": null,
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"tokenize_chinese_chars": true,
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"tokenizer_class": "BertTokenizer",
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"unk_token": "[UNK]"
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}
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aegis_index.faiss
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version https://git-lfs.github.com/spec/v1
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oid sha256:dcf18b088fb09d53873e9e069dca8ad8634839370a4854d7c6be5050cc1fde86
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size 8334381
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---
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tags:
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- sentence-transformers
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- cross-encoder
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- reranker
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base_model: cross-encoder/ms-marco-MiniLM-L6-v2
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pipeline_tag: text-ranking
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library_name: sentence-transformers
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---
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# CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2
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This is a [Cross Encoder](https://www.sbert.net/docs/cross_encoder/usage/usage.html) model finetuned from [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) using the [sentence-transformers](https://www.SBERT.net) library. It computes scores for pairs of texts, which can be used for text reranking and semantic search.
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## Model Details
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### Model Description
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- **Model Type:** Cross Encoder
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- **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) <!-- at revision c5ee24cb16019beea0893ab7796b1df96625c6b8 -->
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- **Maximum Sequence Length:** 512 tokens
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- **Number of Output Labels:** 1 label
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- **Supported Modality:** Text
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<!-- - **Training Dataset:** Unknown -->
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<!-- - **Language:** Unknown -->
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<!-- - **License:** Unknown -->
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### Model Sources
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
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- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
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- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
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### Full Model Architecture
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```
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CrossEncoder(
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|
(0): Transformer({'transformer_task': 'sequence-classification', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'logits'}}, 'module_output_name': 'scores', 'architecture': 'BertForSequenceClassification'})
|
||||||
|
)
|
||||||
|
```
|
||||||
|
|
||||||
|
## Usage
|
||||||
|
|
||||||
|
### Direct Usage (Sentence Transformers)
|
||||||
|
|
||||||
|
First install the Sentence Transformers library:
|
||||||
|
|
||||||
|
```bash
|
||||||
|
pip install -U sentence-transformers
|
||||||
|
```
|
||||||
|
|
||||||
|
Then you can load this model and run inference.
|
||||||
|
```python
|
||||||
|
from sentence_transformers import CrossEncoder
|
||||||
|
|
||||||
|
# Download from the 🤗 Hub
|
||||||
|
model = CrossEncoder("cross_encoder_model_id")
|
||||||
|
# Get scores for pairs of inputs
|
||||||
|
pairs = [
|
||||||
|
['How many calories in an egg', 'There are on average between 55 and 80 calories in an egg depending on its size.'],
|
||||||
|
['How many calories in an egg', 'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.'],
|
||||||
|
['How many calories in an egg', 'Most of the calories in an egg come from the yellow yolk in the center.'],
|
||||||
|
]
|
||||||
|
scores = model.predict(pairs)
|
||||||
|
print(scores)
|
||||||
|
# [ 9.9541 -2.0108 0.9186]
|
||||||
|
|
||||||
|
# Or rank different texts based on similarity to a single text
|
||||||
|
ranks = model.rank(
|
||||||
|
'How many calories in an egg',
|
||||||
|
[
|
||||||
|
'There are on average between 55 and 80 calories in an egg depending on its size.',
|
||||||
|
'Egg whites are very low in calories, have no fat, no cholesterol, and are loaded with protein.',
|
||||||
|
'Most of the calories in an egg come from the yellow yolk in the center.',
|
||||||
|
]
|
||||||
|
)
|
||||||
|
# [{'corpus_id': ..., 'score': ...}, {'corpus_id': ..., 'score': ...}, ...]
|
||||||
|
```
|
||||||
|
|
||||||
|
<!--
|
||||||
|
### Direct Usage (Transformers)
|
||||||
|
|
||||||
|
<details><summary>Click to see the direct usage in Transformers</summary>
|
||||||
|
|
||||||
|
</details>
|
||||||
|
-->
|
||||||
|
|
||||||
|
<!--
|
||||||
|
### Downstream Usage (Sentence Transformers)
|
||||||
|
|
||||||
|
You can finetune this model on your own dataset.
|
||||||
|
|
||||||
|
<details><summary>Click to expand</summary>
|
||||||
|
|
||||||
|
</details>
|
||||||
|
-->
|
||||||
|
|
||||||
|
<!--
|
||||||
|
### Out-of-Scope Use
|
||||||
|
|
||||||
|
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
|
||||||
|
-->
|
||||||
|
|
||||||
|
<!--
|
||||||
|
## Bias, Risks and Limitations
|
||||||
|
|
||||||
|
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
|
||||||
|
-->
|
||||||
|
|
||||||
|
<!--
|
||||||
|
### Recommendations
|
||||||
|
|
||||||
|
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
|
||||||
|
-->
|
||||||
|
|
||||||
|
## Training Details
|
||||||
|
|
||||||
|
### Framework Versions
|
||||||
|
- Python: 3.12.13
|
||||||
|
- Sentence Transformers: 5.4.1
|
||||||
|
- Transformers: 5.0.0
|
||||||
|
- PyTorch: 2.10.0+cu128
|
||||||
|
- Accelerate: 1.13.0
|
||||||
|
- Datasets: 4.0.0
|
||||||
|
- Tokenizers: 0.22.2
|
||||||
|
|
||||||
|
## Citation
|
||||||
|
|
||||||
|
### BibTeX
|
||||||
|
|
||||||
|
<!--
|
||||||
|
## Glossary
|
||||||
|
|
||||||
|
*Clearly define terms in order to be accessible across audiences.*
|
||||||
|
-->
|
||||||
|
|
||||||
|
<!--
|
||||||
|
## Model Card Authors
|
||||||
|
|
||||||
|
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
|
||||||
|
-->
|
||||||
|
|
||||||
|
<!--
|
||||||
|
## Model Card Contact
|
||||||
|
|
||||||
|
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
|
||||||
|
-->
|
||||||
36
aegis_reranker/config.json
Normal file
36
aegis_reranker/config.json
Normal file
@@ -0,0 +1,36 @@
|
|||||||
|
{
|
||||||
|
"add_cross_attention": false,
|
||||||
|
"architectures": [
|
||||||
|
"BertForSequenceClassification"
|
||||||
|
],
|
||||||
|
"attention_probs_dropout_prob": 0.1,
|
||||||
|
"bos_token_id": null,
|
||||||
|
"classifier_dropout": null,
|
||||||
|
"dtype": "float32",
|
||||||
|
"eos_token_id": null,
|
||||||
|
"gradient_checkpointing": false,
|
||||||
|
"hidden_act": "gelu",
|
||||||
|
"hidden_dropout_prob": 0.1,
|
||||||
|
"hidden_size": 384,
|
||||||
|
"id2label": {
|
||||||
|
"0": "LABEL_0"
|
||||||
|
},
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 1536,
|
||||||
|
"is_decoder": false,
|
||||||
|
"label2id": {
|
||||||
|
"LABEL_0": 0
|
||||||
|
},
|
||||||
|
"layer_norm_eps": 1e-12,
|
||||||
|
"max_position_embeddings": 512,
|
||||||
|
"model_type": "bert",
|
||||||
|
"num_attention_heads": 12,
|
||||||
|
"num_hidden_layers": 6,
|
||||||
|
"pad_token_id": 0,
|
||||||
|
"position_embedding_type": "absolute",
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"transformers_version": "5.0.0",
|
||||||
|
"type_vocab_size": 2,
|
||||||
|
"use_cache": true,
|
||||||
|
"vocab_size": 30522
|
||||||
|
}
|
||||||
11
aegis_reranker/config_sentence_transformers.json
Normal file
11
aegis_reranker/config_sentence_transformers.json
Normal file
@@ -0,0 +1,11 @@
|
|||||||
|
{
|
||||||
|
"__version__": {
|
||||||
|
"pytorch": "2.10.0+cu128",
|
||||||
|
"sentence_transformers": "5.4.1",
|
||||||
|
"transformers": "5.0.0"
|
||||||
|
},
|
||||||
|
"activation_fn": "torch.nn.modules.linear.Identity",
|
||||||
|
"default_prompt_name": null,
|
||||||
|
"model_type": "CrossEncoder",
|
||||||
|
"prompts": {}
|
||||||
|
}
|
||||||
3
aegis_reranker/model.safetensors
Normal file
3
aegis_reranker/model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:5d5a1f2650eeb40eea5f06ddf0802b0ace0f102a28bddaa3a2cfbe65d54459ba
|
||||||
|
size 90866404
|
||||||
8
aegis_reranker/modules.json
Normal file
8
aegis_reranker/modules.json
Normal file
@@ -0,0 +1,8 @@
|
|||||||
|
[
|
||||||
|
{
|
||||||
|
"idx": 0,
|
||||||
|
"name": "0",
|
||||||
|
"path": "",
|
||||||
|
"type": "sentence_transformers.base.modules.transformer.Transformer"
|
||||||
|
}
|
||||||
|
]
|
||||||
10
aegis_reranker/sentence_bert_config.json
Normal file
10
aegis_reranker/sentence_bert_config.json
Normal file
@@ -0,0 +1,10 @@
|
|||||||
|
{
|
||||||
|
"transformer_task": "sequence-classification",
|
||||||
|
"modality_config": {
|
||||||
|
"text": {
|
||||||
|
"method": "forward",
|
||||||
|
"method_output_name": "logits"
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"module_output_name": "scores"
|
||||||
|
}
|
||||||
BIN
aegis_reranker/tokenizer.json
(Stored with Git LFS)
Normal file
BIN
aegis_reranker/tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
18
aegis_reranker/tokenizer_config.json
Normal file
18
aegis_reranker/tokenizer_config.json
Normal file
@@ -0,0 +1,18 @@
|
|||||||
|
{
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"clean_up_tokenization_spaces": true,
|
||||||
|
"cls_token": "[CLS]",
|
||||||
|
"do_basic_tokenize": true,
|
||||||
|
"do_lower_case": true,
|
||||||
|
"is_local": false,
|
||||||
|
"mask_token": "[MASK]",
|
||||||
|
"model_max_length": 512,
|
||||||
|
"model_specific_special_tokens": {},
|
||||||
|
"never_split": null,
|
||||||
|
"pad_token": "[PAD]",
|
||||||
|
"sep_token": "[SEP]",
|
||||||
|
"strip_accents": null,
|
||||||
|
"tokenize_chinese_chars": true,
|
||||||
|
"tokenizer_class": "BertTokenizer",
|
||||||
|
"unk_token": "[UNK]"
|
||||||
|
}
|
||||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
|||||||
|
{%- if tools %}
|
||||||
|
{{- '<|im_start|>system\n' }}
|
||||||
|
{%- if messages[0]['role'] == 'system' %}
|
||||||
|
{{- messages[0]['content'] }}
|
||||||
|
{%- else %}
|
||||||
|
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||||
|
{%- for tool in tools %}
|
||||||
|
{{- "\n" }}
|
||||||
|
{{- tool | tojson }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||||
|
{%- else %}
|
||||||
|
{%- if messages[0]['role'] == 'system' %}
|
||||||
|
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||||
|
{%- else %}
|
||||||
|
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- for message in messages %}
|
||||||
|
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||||
|
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||||
|
{%- elif message.role == "assistant" %}
|
||||||
|
{{- '<|im_start|>' + message.role }}
|
||||||
|
{%- if message.content %}
|
||||||
|
{{- '\n' + message.content }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- for tool_call in message.tool_calls %}
|
||||||
|
{%- if tool_call.function is defined %}
|
||||||
|
{%- set tool_call = tool_call.function %}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_call>\n{"name": "' }}
|
||||||
|
{{- tool_call.name }}
|
||||||
|
{{- '", "arguments": ' }}
|
||||||
|
{{- tool_call.arguments | tojson }}
|
||||||
|
{{- '}\n</tool_call>' }}
|
||||||
|
{%- endfor %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- elif message.role == "tool" %}
|
||||||
|
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||||
|
{{- '<|im_start|>user' }}
|
||||||
|
{%- endif %}
|
||||||
|
{{- '\n<tool_response>\n' }}
|
||||||
|
{{- message.content }}
|
||||||
|
{{- '\n</tool_response>' }}
|
||||||
|
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||||
|
{{- '<|im_end|>\n' }}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endif %}
|
||||||
|
{%- endfor %}
|
||||||
|
{%- if add_generation_prompt %}
|
||||||
|
{{- '<|im_start|>assistant\n' }}
|
||||||
|
{%- endif %}
|
||||||
62
config.json
Normal file
62
config.json
Normal file
@@ -0,0 +1,62 @@
|
|||||||
|
{
|
||||||
|
"architectures": [
|
||||||
|
"Qwen2ForCausalLM"
|
||||||
|
],
|
||||||
|
"attention_dropout": 0.0,
|
||||||
|
"bos_token_id": null,
|
||||||
|
"torch_dtype": "float16",
|
||||||
|
"eos_token_id": 151645,
|
||||||
|
"hidden_act": "silu",
|
||||||
|
"hidden_size": 1536,
|
||||||
|
"initializer_range": 0.02,
|
||||||
|
"intermediate_size": 8960,
|
||||||
|
"layer_types": [
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention",
|
||||||
|
"full_attention"
|
||||||
|
],
|
||||||
|
"max_position_embeddings": 32768,
|
||||||
|
"max_window_layers": 21,
|
||||||
|
"model_type": "qwen2",
|
||||||
|
"num_attention_heads": 12,
|
||||||
|
"num_hidden_layers": 28,
|
||||||
|
"num_key_value_heads": 2,
|
||||||
|
"pad_token_id": 151665,
|
||||||
|
"rms_norm_eps": 1e-06,
|
||||||
|
"rope_parameters": {
|
||||||
|
"rope_theta": 1000000.0,
|
||||||
|
"rope_type": "default"
|
||||||
|
},
|
||||||
|
"sliding_window": null,
|
||||||
|
"tie_word_embeddings": true,
|
||||||
|
"unsloth_fixed": true,
|
||||||
|
"unsloth_version": "2026.5.2",
|
||||||
|
"use_cache": false,
|
||||||
|
"use_sliding_window": false,
|
||||||
|
"vocab_size": 151936
|
||||||
|
}
|
||||||
8
generation_config.json
Normal file
8
generation_config.json
Normal file
@@ -0,0 +1,8 @@
|
|||||||
|
{
|
||||||
|
"max_new_tokens": 512,
|
||||||
|
"temperature": 0.7,
|
||||||
|
"do_sample": true,
|
||||||
|
"top_p": 0.95,
|
||||||
|
"pad_token_id": 151643,
|
||||||
|
"eos_token_id": 151645
|
||||||
|
}
|
||||||
3
logoname.png
Normal file
3
logoname.png
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:512ce2e088c1c4d86622bd667d6a842e37fbd0bf847f410242bce87445f64876
|
||||||
|
size 605773
|
||||||
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:1faa857d8a8c6e1c59fe124a258799cd1de4cc285b67f8112835ba5156b1e1b9
|
||||||
|
size 3087467144
|
||||||
94
pipeline.py
Normal file
94
pipeline.py
Normal file
@@ -0,0 +1,94 @@
|
|||||||
|
|
||||||
|
import faiss, numpy as np, torch, os, re
|
||||||
|
from sentence_transformers import SentenceTransformer, CrossEncoder
|
||||||
|
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
|
||||||
|
|
||||||
|
class NYXISPro:
|
||||||
|
def __init__(self, model_dir="."):
|
||||||
|
self.embedder = SentenceTransformer(f"{model_dir}/aegis_embedder")
|
||||||
|
self.reranker = CrossEncoder(f"{model_dir}/aegis_reranker")
|
||||||
|
self.index = faiss.read_index(f"{model_dir}/aegis_index.faiss")
|
||||||
|
with open(f"{model_dir}/aegis_chunks.txt", "r") as f:
|
||||||
|
self.chunks = [c.strip() for c in f.read().split("<|CHUNK_END|>") if c.strip()]
|
||||||
|
bnb = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4",
|
||||||
|
bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True)
|
||||||
|
self.tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
|
||||||
|
self.model = AutoModelForCausalLM.from_pretrained(model_dir,
|
||||||
|
quantization_config=bnb, device_map="auto", trust_remote_code=True)
|
||||||
|
self.identity = "You are NYXIS, a sharp, warm, and endlessly curious mind. You speak like a clever friend — casual, direct, and human. You notice how people feel and match their energy."
|
||||||
|
self.last_emotion = 5
|
||||||
|
|
||||||
|
def detect_emotion(self, text):
|
||||||
|
t = text.lower()
|
||||||
|
joy = sum(1 for w in ["happy","excited","great","awesome","love","wonderful","joy","thrilled","good","nice"] if w in t)
|
||||||
|
sadness = sum(1 for w in ["sad","depressed","upset","crying","lonely","hurt","broken","bad","awful"] if w in t)
|
||||||
|
anger = sum(1 for w in ["angry","furious","mad","rage","annoyed","frustrated","pissed","stupid","hate"] if w in t)
|
||||||
|
if joy: return min(10, self.last_emotion + joy)
|
||||||
|
if sadness: return max(1, self.last_emotion - sadness)
|
||||||
|
if anger: return max(1, self.last_emotion - anger)
|
||||||
|
return self.last_emotion
|
||||||
|
|
||||||
|
def rag_lookup(self, query, top_k=5):
|
||||||
|
q_emb = self.embedder.encode([query], normalize_embeddings=True).astype('float32')
|
||||||
|
_, indices = self.index.search(q_emb, 20)
|
||||||
|
texts = [self.chunks[idx] for idx in indices[0] if idx < len(self.chunks)]
|
||||||
|
if not texts: return None
|
||||||
|
pairs = [(query, t[:500]) for t in texts]
|
||||||
|
scores = self.reranker.predict(pairs)
|
||||||
|
ranked = sorted(zip(scores, texts), reverse=True)[:top_k]
|
||||||
|
results = [t[:600] for s, t in ranked if s > -4.0]
|
||||||
|
return "\n\n".join(results) if results else None
|
||||||
|
|
||||||
|
def _generate(self, system, user, max_tokens=256):
|
||||||
|
prompt = f"<|im_start|>system\n{system}<|im_end|>\n<|im_start|>user\n{user}<|im_end|>\n<|im_start|>assistant\n"
|
||||||
|
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.model.device)
|
||||||
|
with torch.no_grad():
|
||||||
|
outputs = self.model.generate(**inputs, max_new_tokens=max_tokens, temperature=0.8,
|
||||||
|
do_sample=True, top_p=0.92, repetition_penalty=1.1,
|
||||||
|
pad_token_id=self.tokenizer.eos_token_id,
|
||||||
|
eos_token_id=self.tokenizer.encode("<|im_end|>", add_special_tokens=False)[0])
|
||||||
|
raw = self.tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True).strip()
|
||||||
|
raw = re.sub(r'\n*assistant\s*$', '', raw, flags=re.IGNORECASE).strip()
|
||||||
|
return raw
|
||||||
|
|
||||||
|
def generate(self, user_message, external_search_fn=None):
|
||||||
|
self.last_emotion = self.detect_emotion(user_message)
|
||||||
|
msg = user_message.lower().strip()
|
||||||
|
casual_starts = ["hi","hey","hello","yo","sup","good morning","good evening","how are you","what's up","good job","thanks","bye","okay","huh"]
|
||||||
|
casual_patterns = ["good job","well done","nice one","thank","lol","haha","bro","dude","mate","nigga","chill","relax","talk","chat","joke","story","what do you think","opinion","favorite","you stupid","you dumb","be free","normal","boring","lifeless","your name","who are you","what are you","tell me about yourself","how old are you","what is your name","who made you"]
|
||||||
|
is_casual = any(msg.startswith(s) for s in casual_starts) or any(p in msg for p in casual_patterns)
|
||||||
|
factual_starts = ["what","who","when","where","why","how","explain","define","list","compare","describe","find","search","calculate","solve"]
|
||||||
|
math_patterns = re.search(r'\d+[\+\-\*\/\=]\d+', msg)
|
||||||
|
is_factual = any(msg.startswith(s) for s in factual_starts) or bool(math_patterns) or len(msg) > 50
|
||||||
|
|
||||||
|
if is_casual and not is_factual:
|
||||||
|
system = f"{self.identity}\nThe user's vibe is {self.last_emotion}/10. Match it.\nYou're having a casual conversation. Be warm, brief, and real."
|
||||||
|
response = self._generate(system, user_message)
|
||||||
|
return {"response": response, "emotion_level": self.last_emotion, "knowledge_source": "chat"}
|
||||||
|
|
||||||
|
if is_factual:
|
||||||
|
aegis = self.rag_lookup(user_message)
|
||||||
|
if aegis:
|
||||||
|
system = f"{self.identity}\nVibe: {self.last_emotion}/10.\nUse ONLY this verified context to answer:\n\n{aegis}"
|
||||||
|
response = self._generate(system, user_message)
|
||||||
|
return {"response": response, "emotion_level": self.last_emotion, "knowledge_source": "AEGIS"}
|
||||||
|
if external_search_fn:
|
||||||
|
try:
|
||||||
|
ext = external_search_fn(user_message)
|
||||||
|
if ext and len(ext) > 50:
|
||||||
|
system = f"{self.identity}\nVibe: {self.last_emotion}/10.\nUse this web-sourced context:\n\n{ext}"
|
||||||
|
response = self._generate(system, user_message)
|
||||||
|
return {"response": response, "emotion_level": self.last_emotion, "knowledge_source": "external"}
|
||||||
|
except: pass
|
||||||
|
system = f"{self.identity}\nVibe: {self.last_emotion}/10.\nAEGIS and web failed. Answer from your own knowledge IF confident.\nIf you don't truly know, say exactly: 'I'd need to look that up.'"
|
||||||
|
response = self._generate(system, user_message)
|
||||||
|
source = "model" if "look that up" not in response.lower() else "none"
|
||||||
|
return {"response": response, "emotion_level": self.last_emotion, "knowledge_source": source}
|
||||||
|
|
||||||
|
aegis = self.rag_lookup(user_message)
|
||||||
|
if aegis:
|
||||||
|
system = f"{self.identity}\nVibe: {self.last_emotion}/10.\nUse this relevant context if helpful, but stay conversational:\n\n{aegis}"
|
||||||
|
else:
|
||||||
|
system = f"{self.identity}\nVibe: {self.last_emotion}/10.\nStay conversational."
|
||||||
|
response = self._generate(system, user_message)
|
||||||
|
return {"response": response, "emotion_level": self.last_emotion, "knowledge_source": "hybrid"}
|
||||||
3
preview imgagee.png
Normal file
3
preview imgagee.png
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:57ffe1cd65b4c235d413c9cb2cbaf7d71c092f8a74b9554f28c44c2cd3206094
|
||||||
|
size 967975
|
||||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
|
||||||
|
size 11422356
|
||||||
202
tokenizer_config.json
Normal file
202
tokenizer_config.json
Normal file
@@ -0,0 +1,202 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"backend": "tokenizers",
|
||||||
|
"bos_token": null,
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"is_local": false,
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|PAD_TOKEN|>",
|
||||||
|
"padding_side": "left",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151646": {
|
||||||
|
"content": "<|object_ref_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151647": {
|
||||||
|
"content": "<|object_ref_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151648": {
|
||||||
|
"content": "<|box_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151649": {
|
||||||
|
"content": "<|box_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151650": {
|
||||||
|
"content": "<|quad_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151651": {
|
||||||
|
"content": "<|quad_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151652": {
|
||||||
|
"content": "<|vision_start|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151653": {
|
||||||
|
"content": "<|vision_end|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151654": {
|
||||||
|
"content": "<|vision_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151655": {
|
||||||
|
"content": "<|image_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151656": {
|
||||||
|
"content": "<|video_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151657": {
|
||||||
|
"content": "<tool_call>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151658": {
|
||||||
|
"content": "</tool_call>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151659": {
|
||||||
|
"content": "<|fim_prefix|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151660": {
|
||||||
|
"content": "<|fim_middle|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151661": {
|
||||||
|
"content": "<|fim_suffix|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151662": {
|
||||||
|
"content": "<|fim_pad|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151663": {
|
||||||
|
"content": "<|repo_name|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151664": {
|
||||||
|
"content": "<|file_sep|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"151665": {
|
||||||
|
"content": "<|PAD_TOKEN|>",
|
||||||
|
"single_word": false,
|
||||||
|
"lstrip": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n"
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user