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Model: QuantaSparkLabs/NYXIS-Pro Source: Original Platform
2026-06-30 22:08:44 +08:00
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--- a/README.md
+++ b/README.md
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+---
+license: apache-2.0
+language:
+- en
+tags:
+- rag
+- retrieval-augmented-generation
+- knowledge-base
+- emotion-detection
+- dual-stage-retrieval
+- coding
+- math
+- science
+- history
+- nyxis
+- quantasparklabs
+pipeline_tag: text-generation
+base_model:
+- QuantaSparkLabs/NYXIS-1.1B
+library_name: transformers
+datasets:
+- QuantaSparkLabs/NYXIS-AEGIS-Knowledge
+---
+<p align="center">
+  <img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/preview imgagee.png"
+       alt="NYXIS Logo"
+       width="160"
+       height="160"
+       style="border-radius: 50%; object-fit: cover;">
+</p>
+
+<p align="center">
+  <img src="https://huggingface.co/QuantaSparkLabs/NYXIS-Pro/resolve/main/logoname.png"
+       alt="NYXIS Name"
+       width="700"
+       style="border-radius: 18px;">
+</p>
+# NYXIS-PRO 🛡️
+
+<p align="center">
+  <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>
+  <a href="https://huggingface.co/QuantaSparkLabs/NYXIS-AEGIS-KB"><img src="https://img.shields.io/badge/Knowledge-AEGIS-blue" alt="AEGIS KB"></a>
+  <a href="#"><img src="https://img.shields.io/badge/Retrieval-Dual%20Stage-green" alt="Dual Stage"></a>
+  <a href="#"><img src="https://img.shields.io/badge/Emotion-1--10%20Scale-orange" alt="Emotion Engine"></a>
+  <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>
+</p>
+
+**The all-in-one reasoning engine with the AEGIS knowledge shield.**
+
+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.**
+
+## 🧠 What Makes It Different
+
+| Feature | Description |
+|---------|-------------|
+| **AEGIS Knowledge Base** | 5,426 curated Wikipedia, Trivia QA, and ArXiv chunks |
+| **Dual‑Stage Retriever** | Dense recall (bge‑small) + Cross‑encoder rerank (ms‑marco‑MiniLM) |
+| **Emotion Engine** | Detects user emotion on a 1‑10 scale and matches tone |
+| **4‑Tier Knowledge Cascade** | AEGIS → External Search → Model Brain → Honest Fallback |
+| **Zero Hallucination** | If it doesn't know, it says so — no fabrication |
+
+## 🚀 Quick Start
+
+```python
+from huggingface_hub import snapshot_download
+import sys
+
+model_dir = snapshot_download("QuantaSparkLabs/NYXIS-Pro")
+sys.path.insert(0, model_dir)
+
+from pipeline import NYXISPro
+nyxis = NYXISPro(model_dir)
+
+result = nyxis.generate("What is a binary search tree?")
+print(result["response"])
+```
+
+> **Requirements:** `sentence-transformers`, `faiss-cpu`, `transformers`, `accelerate`, `bitsandbytes`  
+> **VRAM:** ~2.1 GB (4‑bit) | **Hardware:** T4 or better
+
+## 📦 What's Inside
+
+| Component | Model | Purpose |
+|-----------|-------|---------|
+| **Base LLM** | NYXIS1.1B) | Text generation |
+| **Dense Retriever** | bge‑small‑en‑v1.5 | Initial candidate recall |
+| **Cross‑Encoder** | ms‑marco‑MiniLM‑L‑6‑v2 | Precision re‑ranking |
+| **FAISS Index** | 5,426 chunks | Knowledge storage |
+| **Emotion Engine** | Custom keyword‑based | Tone matching |
+
+## 🤖 Emotion Intelligence
+
+NYXIS-PRO detects your emotional state from text and adjusts its personality:
+
+| Level | Emotion | Response Style |
+|:---:|---------|----------------|
+| 1‑3 | Sad / Angry | Gentle, supportive, empathetic |
+| 4‑6 | Neutral | Balanced, warm, helpful |
+| 7‑9 | Happy / Excited | Energetic, playful |
+| 10 | Overjoyed | Celebratory |
+
+## 🛡️ Knowledge Cascade
+
+When you ask a factual question, NYXIS-PRO follows a strict 4‑tier protocol:
+
+1. **AEGIS** — Searches internal verified knowledge base
+2. **External Search** — Falls back to web search (user‑provided API)
+3. **Model Brain** — Uses training data with honesty guard
+4. **Honest Fallback** — Admits "I don't have enough information"
+
+It **never fabricates** an answer.
+
+## Limitations
+
+- Knowledge base covers ~5,400 chunks — not infinite
+- Emotion detection is keyword‑based, not deep sentiment analysis
+- External search requires user‑provided API function
+- English‑only, 1.5B model size limits complex reasoning
+
+## License
+
+Apache‑2.0
+
+---
+
+*Built with 🛡️ by QuantaSparkLabs*
--- a/aegis_chunks.txt
+++ b/aegis_chunks.txt
--- a/aegis_embedder/1_Pooling/config.json
+++ b/aegis_embedder/1_Pooling/config.json
@@ -0,0 +1,5 @@
+{
+    "embedding_dimension": 384,
+    "pooling_mode": "cls",
+    "include_prompt": true
+}
--- a/aegis_embedder/README.md
+++ b/aegis_embedder/README.md
--- a/aegis_embedder/config.json
+++ b/aegis_embedder/config.json
@@ -0,0 +1,35 @@
+{
+  "add_cross_attention": false,
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "bos_token_id": null,
+  "classifier_dropout": null,
+  "dtype": "float32",
+  "eos_token_id": null,
+  "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": 12,
+  "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
+}
--- a/aegis_embedder/config_sentence_transformers.json
+++ b/aegis_embedder/config_sentence_transformers.json
@@ -0,0 +1,14 @@
+{
+  "__version__": {
+    "pytorch": "2.10.0+cu128",
+    "sentence_transformers": "5.4.1",
+    "transformers": "5.0.0"
+  },
+  "default_prompt_name": null,
+  "model_type": "SentenceTransformer",
+  "prompts": {
+    "document": "",
+    "query": ""
+  },
+  "similarity_fn_name": "cosine"
+}
--- a/aegis_embedder/model.safetensors
+++ b/aegis_embedder/model.safetensors
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+oid sha256:ba3eeb8008783b2a8e9aeb9415a511d9aa3bdfec2dbbdfe8e4bbf5bdde283c6b
+size 133462104
--- a/aegis_embedder/modules.json
+++ b/aegis_embedder/modules.json
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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.sentence_transformer.modules.pooling.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.sentence_transformer.modules.normalize.Normalize"
+  }
+]
--- a/aegis_embedder/sentence_bert_config.json
+++ b/aegis_embedder/sentence_bert_config.json
@@ -0,0 +1,10 @@
+{
+    "transformer_task": "feature-extraction",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "last_hidden_state"
+        }
+    },
+    "module_output_name": "token_embeddings"
+}
--- a/aegis_embedder/tokenizer.json
+++ b/aegis_embedder/tokenizer.json
--- a/aegis_embedder/tokenizer_config.json
+++ b/aegis_embedder/tokenizer_config.json
@@ -0,0 +1,17 @@
+{
+  "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,
+  "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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+size 8334381
--- a/aegis_reranker/README.md
+++ b/aegis_reranker/README.md
@@ -0,0 +1,147 @@
+---
+tags:
+- sentence-transformers
+- cross-encoder
+- reranker
+base_model: cross-encoder/ms-marco-MiniLM-L6-v2
+pipeline_tag: text-ranking
+library_name: sentence-transformers
+---
+
+# CrossEncoder based on cross-encoder/ms-marco-MiniLM-L6-v2
+
+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.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Cross Encoder
+- **Base model:** [cross-encoder/ms-marco-MiniLM-L6-v2](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) <!-- at revision c5ee24cb16019beea0893ab7796b1df96625c6b8 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Number of Output Labels:** 1 label
+- **Supported Modality:** Text
+<!-- - **Training Dataset:** Unknown -->
+<!-- - **Language:** Unknown -->
+<!-- - **License:** Unknown -->
+
+### Model Sources
+
+- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
+- **Documentation:** [Cross Encoder Documentation](https://www.sbert.net/docs/cross_encoder/usage/usage.html)
+- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
+- **Hugging Face:** [Cross Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=cross-encoder)
+
+### Full Model Architecture
+
+```
+CrossEncoder(
+  (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.*
+-->
--- a/aegis_reranker/config.json
+++ b/aegis_reranker/config.json
@@ -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
+}
--- a/aegis_reranker/config_sentence_transformers.json
+++ b/aegis_reranker/config_sentence_transformers.json
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+{
+  "__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": {}
+}
--- a/aegis_reranker/model.safetensors
+++ b/aegis_reranker/model.safetensors
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+version https://git-lfs.github.com/spec/v1
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+size 90866404
--- a/aegis_reranker/modules.json
+++ b/aegis_reranker/modules.json
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+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.base.modules.transformer.Transformer"
+  }
+]
--- a/aegis_reranker/sentence_bert_config.json
+++ b/aegis_reranker/sentence_bert_config.json
@@ -0,0 +1,10 @@
+{
+    "transformer_task": "sequence-classification",
+    "modality_config": {
+        "text": {
+            "method": "forward",
+            "method_output_name": "logits"
+        }
+    },
+    "module_output_name": "scores"
+}
--- a/aegis_reranker/tokenizer.json
+++ b/aegis_reranker/tokenizer.json
--- a/aegis_reranker/tokenizer_config.json
+++ b/aegis_reranker/tokenizer_config.json
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+{
+  "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]"
+}
--- a/chat_template.jinja
+++ b/chat_template.jinja
@@ -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 %}
--- a/config.json
+++ b/config.json
@@ -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
+}
--- a/generation_config.json
+++ b/generation_config.json
@@ -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
+}
--- a/logoname.png
+++ b/logoname.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:512ce2e088c1c4d86622bd667d6a842e37fbd0bf847f410242bce87445f64876
+size 605773
--- a/model.safetensors
+++ b/model.safetensors
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1faa857d8a8c6e1c59fe124a258799cd1de4cc285b67f8112835ba5156b1e1b9
+size 3087467144
--- a/pipeline.py
+++ b/pipeline.py
@@ -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"}
--- a/imgagee.png
+++ b/imgagee.png
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:57ffe1cd65b4c235d413c9cb2cbaf7d71c092f8a74b9554f28c44c2cd3206094
+size 967975
--- a/tokenizer.json
+++ b/tokenizer.json
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd5948af71b4f56cf697f7580814c7ce8b80595ef985544efcacf716126a2e31
+size 11422356
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -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"
+}