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Model: muverqqw/Noir-Ultra
Source: Original Platform

.gitattributes

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README.md

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+---
+base_model: unsloth/Qwen2.5-7B-Instruct # Уточни, если база другая
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- qwen2
+- reasoning
+- science
+- logic
+- trl
+- sft
+license: apache-2.0
+language:
+- en
+- ru
+- zh
+model-index:
+- name: Noir-Ultra
+  results:
+  - task:
+      type: text-generation
+      name: STEM (Science)
+    dataset:
+      name: SciQ
+      type: sciq
+    metrics:
+    - name: accuracy
+      type: exact_match
+      value: 91.0
+  - task:
+      type: text-generation
+      name: Mathematics
+    dataset:
+      name: GSM8K
+      type: gsm8k
+    metrics:
+    - name: accuracy
+      type: exact_match
+      value: 84.0
+  - task:
+      type: text-generation
+      name: Logic
+    dataset:
+      name: ARC Challenge
+      type: ai2_arc
+    metrics:
+    - name: accuracy
+      type: exact_match
+      value: 86.0
+---
+
+# 🌌 Noir-Ultra (7B)
+
+<div align="center">
+
+[Noir Family](https://huggingface.co/collections/muverqqw/noir) | [GGUF Versions](https://huggingface.co/muverqqw/Noir-GGUF) | [Technical Report](#-noir-ultra-evaluation-dashboard)
+
+</div>
+
+**Noir-Ultra** is the elite 7-billion parameter model of the Noir series. It represents a breakthrough in training efficiency: where previous 7B iterations required 6 epochs to reach stability, **Noir-Ultra achieved superior results in just a single epoch**.
+
+This model is a "compact titan," delivering scientific reasoning and mathematical accuracy that rival much larger architectures.
+
+---
+
+## 🚀 The Ultra Advantage
+
+* 🧬 **Unrivaled STEM:** With a **91.0% score on SciQ**, it is a specialized tool for scientific inquiry.
+* 📐 **Mathematical Precision:** Scoring **84.0% on GSM8K**, it handles complex chains of thought with ease.
+* 🧠 **Logical Depth:** An ARC-Challenge score of **86.0%** places it at the top of its weight class for reasoning tasks.
+
+---
+
+## 📊 Evaluation Dashboard (Noir Ultra Report)
+
+Based on the latest evaluation, Noir-Ultra shows an exceptionally strong profile in technical domains:
+
+| Domain | Benchmark | Result (%) | Status |
+| :--- | :--- | :---: | :--- |
+| **STEM** | SciQ | **91.0%** | 🏆 Master |
+| **Logic** | ARC-C | **86.0%** | 🔥 Elite |
+| **Math** | GSM8K | **84.0%** | ✅ Advanced |
+| **Medicine** | MedQA | **65.0%** | 🩺 Competent |
+| **Physics** | MMLU-Physics| **70.0%** | 🧪 Specialist |
+
+
+
+---
+
+## 📦 Noir Model Family Matrix
+
+| Model | Parameters | Role | Key Strength |
+| :--- | :--- | :--- | :--- |
+| **Noir-Lightning** | 0.5B | The Pocket Assistant | Ultra-fast, runs on anything |
+| **Noir-Mini** | 1.5B | The Balanced Thinker | High speed with solid grammar |
+| **Noir-Standard** | 3B | The Versatile Workhorse | 65% GSM8K, perfect for 8GB VRAM |
+| **Noir-Ultra** | **7B** | **The Reasoning Master** | **91% SciQ & 84% Math** |
+| **Noir-Starlight** | 14B | The Galactic Intelligence | Deep logic & Expert-level STEM |
+
+---
+
+## 🛠 Quick Start (Transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_name = "muverqqw/Noir-Ultra" 
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name, 
+    load_in_4bit=True, # Recommended for 8GB VRAM
+    device_map="auto"
+)
+```
+
+---
+
+### 👤 Credits
+* **Developer:** IceL1ghtning
+
+* **Architecture:** Qwen 2.5 (7B)
+
+* **Release:** 2026
+
+* **License:** Apache 2.0
+
+<div align="center">
+<sub>Efficiency meets intelligence. Built with passion for the open-source community.</sub>
+</div>

chat_template.jinja

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+{%- 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 %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": null,
+  "dtype": "float16",
+  "eos_token_id": 151645,
+  "hidden_act": "silu",
+  "hidden_size": 3584,
+  "initializer_range": 0.02,
+  "intermediate_size": 18944,
+  "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": 28,
+  "model_type": "qwen2",
+  "num_attention_heads": 28,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 4,
+  "pad_token_id": 151643,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.3.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "5.3.0"
+}

model.safetensors

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+size 15231271816

tokenizer.json

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tokenizer_config.json

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+{
+  "add_prefix_space": false,
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "is_local": false,
+  "model_max_length": 131072,
+  "pad_token": "<|endoftext|>",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}