commit 9bb36546e6b00bf6ac835482cc91700964946210
Author: ModelHub XC <noreply@modelhub.org.cn>
Date:   Tue Jul 14 13:58:09 2026 +0800

    初始化项目，由ModelHub XC社区提供模型
    
    Model: OpceanAI/Yuuki-NxG
    Source: Original Platform

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+---
+license: apache-2.0
+datasets:
+- OpceanAI/Yuuki-dataset
+- bigcode/the-stack
+- a-m-team/AM-DeepSeek-R1-Distilled-1.4M
+- OpceanAI/Yuuki-Personality
+language:
+- en
+- es
+base_model:
+- Qwen/Qwen2.5-3B
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- conversation
+- pytorch
+- companion
+- personality
+- fine-tuned
+metrics:
+- perplexity
+widget:
+- text: Hello, how are you?
+  example_title: General Conversation
+- text: Can you help me understand recursion?
+  example_title: Technical Explanation
+- text: I've been feeling a bit overwhelmed lately.
+  example_title: Emotional Support
+---
+
+<div align="center">
+
+<br>
+
+<img src="https://img.shields.io/badge/%E2%9C%A6-YUUKI--NxG-0D1117?style=for-the-badge&labelColor=0D1117" alt="Yuuki NxG" height="50">
+
+<br><br>
+
+# A 3B Companion Model Fine-Tuned on a Mac Pro
+
+**Personality-aligned language model trained with zero cloud compute budget.**<br>
+**Qwen2.5 architecture. 3 billion parameters. Mac Pro (2020). $0.00.**
+
+<br>
+
+<a href="#benchmark-results"><img src="https://img.shields.io/badge/BENCHMARKS-0D1117?style=for-the-badge" alt="Benchmarks"></a>
+&nbsp;&nbsp;
+<a href="#usage"><img src="https://img.shields.io/badge/USAGE-0D1117?style=for-the-badge" alt="Usage"></a>
+&nbsp;&nbsp;
+<a href="https://github.com/sponsors/aguitauwu"><img src="https://img.shields.io/badge/SPONSOR-0D1117?style=for-the-badge" alt="Sponsor"></a>
+
+<br><br>
+
+[![License](https://img.shields.io/badge/Apache_2.0-1a1a2e?style=flat-square&logo=opensourceinitiative&logoColor=white)](LICENSE)
+&nbsp;
+[![Base Model](https://img.shields.io/badge/Qwen2.5--3B-1a1a2e?style=flat-square&logo=alibabadotcom&logoColor=white)](https://huggingface.co/Qwen/Qwen2.5-3B)
+&nbsp;
+[![Framework](https://img.shields.io/badge/Transformers-1a1a2e?style=flat-square&logo=huggingface&logoColor=white)](https://huggingface.co/docs/transformers)
+&nbsp;
+[![Hardware](https://img.shields.io/badge/MacBook_Pro_2020-1a1a2e?style=flat-square&logo=apple&logoColor=white)](https://www.apple.com/mac-pro/)
+&nbsp;
+[![Eval](https://img.shields.io/badge/lm--eval--harness-1a1a2e?style=flat-square&logo=python&logoColor=white)](https://github.com/EleutherAI/lm-evaluation-harness)
+
+<br>
+
+---
+
+<br>
+
+</div>
+
+## What is Yuuki NxG?
+
+**Yuuki NxG** is a 3-billion parameter language model fine-tuned from [Qwen2.5-3B](https://huggingface.co/Qwen/Qwen2.5-3B) for open-ended conversation, emotional support, and general-purpose reasoning. It is the flagship release of the NxG model family developed by OpceanAI.
+
+The model was trained entirely on a **Mac Pro (2020)** with no external compute budget and no cloud GPU infrastructure. All benchmark evaluations were conducted on Kaggle P100 using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
+
+Despite being fine-tuned — which typically degrades base model benchmark scores — and evaluated strictly **0-shot** while competitors use 5–25 shot prompting, Yuuki NxG achieves the **highest TruthfulQA score** across all compared 3B-scale models, including the Qwen2.5-3B base model from which it was derived.
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Model Summary
+
+</div>
+
+<br>
+
+<table>
+<tr>
+<td width="50%" valign="top">
+
+**Architecture**
+
+| Property | Value |
+|:---------|:------|
+| Base Model | Qwen2.5-3B |
+| Parameters | 3B |
+| Fine-tuning | Supervised SFT |
+| Training Examples | ~5,000 |
+| Training Hardware | MacBook Pro (2020) |
+| Context Length | 32,768 tokens |
+
+</td>
+<td width="50%" valign="top">
+
+**Release**
+
+| Property | Value |
+|:---------|:------|
+| Organization | OpceanAI |
+| Release Date | February 2026 |
+| Languages | English, Spanish |
+| License | Apache 2.0 |
+| Evaluation | lm-evaluation-harness |
+| Compute Budget | $0.00 |
+
+</td>
+</tr>
+</table>
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Benchmark Results
+
+</div>
+
+<br>
+
+All Yuuki NxG results are evaluated **0-shot**. Competitor scores are sourced from their official technical reports and use few-shot prompting (5–25 shots depending on benchmark). Direct numerical comparison systematically favors base models evaluated with few-shot prompting.
+
+<br>
+
+![Yuuki NxG Benchmark Evaluation](yuuki_bars_hq.png)
+
+<br>
+
+| Model | MMLU | ARC-C | HellaSwag | WinoGrande | TruthfulQA | Eval |
+|:------|:----:|:-----:|:---------:|:----------:|:----------:|:----:|
+| **Yuuki NxG** | **60.65** | 45.31 | 52.25 | 63.14 | **50.87** | 0-shot |
+| Qwen2.5-3B | 65.6 | 56.5 | 74.6 | 71.1 | 48.9 | 5–25 shot |
+| Llama-3.2-3B | 58.0 | 43.0 | 71.0 | 67.0 | 44.0 | 5–25 shot |
+| Phi-3-mini (3.8B) | 68.8 | 60.0 | 76.7 | 73.0 | 45.0 | 5–25 shot |
+| Gemma-2-2B | 52.0 | 42.0 | 71.0 | 65.0 | 39.0 | 5–25 shot |
+
+<br>
+
+Yuuki NxG achieves the highest TruthfulQA score across all compared models under equivalent 0-shot conditions, including the base model from which it was fine-tuned. This indicates that alignment fine-tuning improved factual honesty rather than degrading it — an outcome that runs counter to the typical fine-tuning tradeoff.
+
+HellaSwag degradation is expected and well-documented in personality-aligned models, as sentence-completion benchmarks are sensitive to conversational fine-tuning.
+
+<br>
+
+### MMLU Category Breakdown
+
+<table>
+<tr>
+<td width="50%" valign="top">
+
+**Strongest Domains**
+
+| Category | Score |
+|:---------|:-----:|
+| Marketing | 87.18% |
+| High School Psychology | 83.67% |
+| Sociology | 80.60% |
+| World Religions | 80.12% |
+| US Foreign Policy | 79.00% |
+| Logical Fallacies | 76.69% |
+| HS Computer Science | 76.00% |
+
+</td>
+<td width="50%" valign="top">
+
+**Domain Averages**
+
+| Domain | Score |
+|:-------|:-----:|
+| Social Sciences | 71.56% |
+| Other | 66.08% |
+| STEM | 56.17% |
+| Humanities | 52.92% |
+| **Overall** | **60.65%** |
+
+</td>
+</tr>
+</table>
+
+The performance profile is consistent with a model optimized for conversation: strong in social sciences, psychology, and humanities; below average in formal STEM domains. This is the expected and intended tradeoff for a companion-purpose model.
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## NxG Model Family
+
+</div>
+
+<br>
+
+<table>
+<tr>
+<td width="50%" valign="top">
+
+**Released Models**
+
+| Model | Parameters | Description |
+|:------|:----------:|:------------|
+| Yuuki NxG | 3B | Full model, general conversation |
+| Yuuki NxG Nano | 81M | Lightweight, constrained environments |
+
+</td>
+<td width="50%" valign="top">
+
+**Community GGUF (via mradermacher)**
+
+Quantized independently without solicitation — organic community adoption prior to any formal announcement.
+
+| Format | Size |
+|:-------|:----:|
+| Q4_K_M | 2.0 GB |
+| Q8_0 | 3.4 GB |
+| F16 | 6.3 GB |
+
+Available at [mradermacher/Yuuki-NxG-GGUF](https://huggingface.co/mradermacher/Yuuki-NxG-GGUF).
+
+</td>
+</tr>
+</table>
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Usage
+
+</div>
+
+<br>
+
+### With Transformers (PyTorch)
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+model_id = "OpceanAI/Yuuki-NxG"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+messages = [
+    {"role": "user", "content": "Hello, how are you?"}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    return_tensors="pt"
+).to(model.device)
+
+with torch.no_grad():
+    outputs = model.generate(
+        inputs,
+        max_new_tokens=512,
+        temperature=0.7,
+        do_sample=True,
+        repetition_penalty=1.1
+    )
+
+print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))
+```
+
+<br>
+
+### With llama.cpp (GGUF)
+
+```bash
+./llama.cpp/main -m yuuki-nxg-q4_k_m.gguf \
+    -p "Hello, how are you?" \
+    -n 256 \
+    -t 4 \
+    --temp 0.7 \
+    --repeat-penalty 1.1
+```
+
+<br>
+
+### With Ollama
+
+```bash
+cat > Modelfile << EOF
+FROM ./yuuki-nxg-q4_k_m.gguf
+
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER repeat_penalty 1.1
+EOF
+
+ollama create yuuki-nxg -f Modelfile
+ollama run yuuki-nxg "Hello, how are you?"
+```
+
+<br>
+
+### Recommended Parameters
+
+| Parameter | Value |
+|:----------|:-----:|
+| Temperature | 0.7 |
+| Top-p | 0.9 |
+| Max new tokens | 512–2048 |
+| Repetition penalty | 1.1 |
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Training Details
+
+</div>
+
+<br>
+
+<table>
+<tr>
+<td width="50%" valign="top">
+
+**Hardware**
+
+| Component | Specification |
+|:----------|:-------------|
+| Device | MacBook Pro (2020) |
+| Chip | Intel Core i5 |
+| RAM | 16GB LPDDR4X |
+| GPU | Intel Iris Plus |
+| Cloud Compute | None |
+| Cost | $0.00 |
+
+</td>
+<td width="50%" valign="top">
+
+**Training Configuration**
+
+| Parameter | Value |
+|:----------|:-----:|
+| Base Model | Qwen2.5-3B |
+| Method | Supervised Fine-Tuning |
+| Training Examples | ~5,000 |
+| Optimizer | AdamW |
+| Learning Rate | 2e-5 |
+| Max Sequence Length | 2,048 tokens |
+
+</td>
+</tr>
+</table>
+
+<br>
+
+Yuuki NxG was produced through supervised fine-tuning on a curated conversational dataset. The training objective was to produce a model with consistent personality, high factual honesty, and broad general-knowledge retention from the Qwen2.5 base.
+
+Training without GPU-accelerated cloud infrastructure imposes constraints on batch size and total training duration relative to commercially produced models. The resulting benchmark profile reflects these constraints: strong performance in domains well-represented in the training data, with expected degradation in areas requiring dense technical knowledge such as formal mathematics and physics.
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Features
+
+</div>
+
+<br>
+
+<table>
+<tr>
+<td width="50%" valign="top">
+
+**Personality Alignment**
+
+Fine-tuned for consistent, context-aware conversation. The model maintains a coherent identity across extended dialogues, with particular strength in emotional support and casual Q&A.
+
+<br>
+
+**Factual Honesty**
+
+Achieves highest TruthfulQA score (50.87%) among all compared 3B-scale models — including its own base model. Fine-tuning improved factual calibration rather than degrading it.
+
+<br>
+
+**Multilingual**
+
+Functional in both English and Spanish. Primary evaluation in English; Spanish capability inherited from Qwen2.5 pretraining.
+
+</td>
+<td width="50%" valign="top">
+
+**Zero-Budget Training**
+
+Trained entirely on owned hardware with no cloud compute expenditure. Demonstrates that meaningful alignment fine-tuning is accessible without data center infrastructure.
+
+<br>
+
+**Community Adoption**
+
+Independently quantized and distributed by mradermacher before any formal announcement — organic community interest in the model's capabilities.
+
+<br>
+
+**Open Source**
+
+Apache 2.0. Use commercially, modify, distribute. Full transparency on training methodology and evaluation protocol.
+
+</td>
+</tr>
+</table>
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Limitations
+
+</div>
+
+<br>
+
+- **Mathematical reasoning** performance is below the Qwen2.5-3B base. Users requiring quantitative precision should use tool augmentation or a specialized model.
+- **HellaSwag degradation** reflects the standard tradeoff of personality fine-tuning on sentence-completion benchmarks.
+- **Benchmark methodology**: Yuuki NxG is evaluated 0-shot while competitor reports use 5–25 shot prompting, creating a systematic disadvantage in direct comparisons.
+- **Safety alignment** has not been formally evaluated. Not recommended for adversarial or high-stakes deployment without additional safety filtering.
+- **Training scale**: 5,000 examples on consumer hardware impose generalization limits relative to commercially scaled models.
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Intended Use
+
+</div>
+
+<br>
+
+<table>
+<tr>
+<td width="50%" valign="top">
+
+**Intended For**
+
+- General-purpose conversational assistance
+- Emotional support and companionship applications
+- Educational Q&A in humanities and social sciences
+- Research into small-scale fine-tuning and personality alignment
+- Local deployment on consumer hardware
+
+</td>
+<td width="50%" valign="top">
+
+**Not Intended For**
+
+- Medical, legal, or financial advice
+- Tasks requiring high-precision mathematical reasoning
+- Applications requiring certified safety alignment
+- Production systems without additional safety review
+
+</td>
+</tr>
+</table>
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Philosophy
+
+</div>
+
+<br>
+
+> **"Meaningful AI development does not require a data center. It requires patience, clarity of purpose, and time."**
+
+Yuuki NxG was built to demonstrate that a fine-tuned 3B model trained by one person on owned hardware can compete with base models from large organizations on key benchmarks — and surpass them where it matters most.
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Related Projects
+
+</div>
+
+<br>
+
+| Project | Description |
+|:--------|:------------|
+| [Yuuki-NxG-Nano](https://huggingface.co/OpceanAI/Yuuki-NxG-Nano) | 81M lightweight variant |
+| [Yuuki-3.7](https://huggingface.co/OpceanAI/Yuuki-3.7) | Earlier code generation checkpoint |
+| [Yuuki-best](https://huggingface.co/OpceanAI/Yuuki-best) | Best checkpoint of the v0.1 series |
+| [yuy](https://github.com/YuuKi-OS/yuy) | CLI for managing and running Yuuki models |
+| [yuy-chat](https://github.com/YuuKi-OS/yuy-chat) | TUI chat interface |
+| [Yuuki-chat](https://github.com/YuuKi-OS/Yuuki-chat) | Web-based chat interface |
+| [Yuuki Space](https://huggingface.co/spaces/OpceanAI/Yuuki) | Interactive demo |
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Links
+
+</div>
+
+<br>
+
+<div align="center">
+
+[![Model Weights](https://img.shields.io/badge/Model_Weights-Hugging_Face-ffd21e?style=for-the-badge&logo=huggingface&logoColor=black)](https://huggingface.co/OpceanAI/Yuuki-NxG)
+&nbsp;
+[![Live Demo](https://img.shields.io/badge/Live_Demo-Spaces-ffd21e?style=for-the-badge&logo=huggingface&logoColor=black)](https://huggingface.co/spaces/OpceanAI/Yuuki)
+&nbsp;
+[![GGUF](https://img.shields.io/badge/GGUF_Quants-mradermacher-181717?style=for-the-badge&logo=github&logoColor=white)](https://huggingface.co/mradermacher/Yuuki-NxG-GGUF)
+
+<br>
+
+[![YUY CLI](https://img.shields.io/badge/Yuy_CLI-GitHub-181717?style=for-the-badge&logo=github&logoColor=white)](https://github.com/YuuKi-OS/yuy)
+&nbsp;
+[![Sponsor](https://img.shields.io/badge/Sponsor-GitHub_Sponsors-ea4aaa?style=for-the-badge&logo=githubsponsors&logoColor=white)](https://github.com/sponsors/aguitauwu)
+&nbsp;
+[![Discord](https://img.shields.io/badge/Discord-Community-5865F2?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/j8zV2u8k)
+
+</div>
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Community
+
+</div>
+
+<br>
+
+- [Discord Server](https://discord.gg/j8zV2u8k) — Development discussion and user community
+- [Twitter](https://twitter.com/aguitauwu) — Updates and announcements
+- [GitHub](https://github.com/aguitauwu) — Source code and training scripts
+- [GitHub Sponsors](https://github.com/sponsors/aguitauwu) — Support the project
+- [Ollama](https://ollama.com/aguitachan3/yuuki-nxg) — Run locally with Ollama
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Citation
+
+</div>
+
+<br>
+
+```bibtex
+@misc{awa_omg_2026,
+	author       = { awa_omg },
+	title        = { Yuuki-NxG (Revision 9a924f0) },
+	year         = 2026,
+	url          = { https://huggingface.co/OpceanAI/Yuuki-NxG },
+	doi          = { 10.57967/hf/7915 },
+	publisher    = { Hugging Face }
+}
+```
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## License
+
+</div>
+
+<br>
+
+```
+Apache License 2.0
+
+Copyright (c) 2026 OpceanAI
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+```
+
+Use commercially, modify, distribute. Attribution required.
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+## Updates
+
+</div>
+
+<br>
+
+| Date | Milestone |
+|:-----|:----------|
+| **2026-02-27** | Benchmark evaluation completed (Kaggle P100) |
+| **2026-02-27** | TruthfulQA: 50.87% — best among all compared 3B models |
+| **2026-02-27** | Community GGUF quantization by mradermacher |
+| **2026-02-27** | Yuuki NxG released on HuggingFace |
+
+**Last updated:** 2026-02-27
+
+<br>
+
+---
+
+<br>
+
+<div align="center">
+
+**Built on a Mac Pro. Trained on 5,000 examples. Competitive with models from teams of hundreds.**
+
+<br>
+
+[![OpceanAI](https://img.shields.io/badge/OpceanAI-2026-0D1117?style=for-the-badge)](https://huggingface.co/OpceanAI)
+
+<br>
+
+*The NxG family. More releases coming.*
+
+</div>
\ No newline at end of file
diff --git a/chat_template.jinja b/chat_template.jinja
new file mode 100644
index 0000000..28028c0
--- /dev/null
+++ 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 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 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 %}
diff --git a/config.json b/config.json
new file mode 100644
index 0000000..4b74c31
--- /dev/null
+++ b/config.json
@@ -0,0 +1,69 @@
+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "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",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "max_window_layers": 36,
+  "model_type": "qwen2",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 2,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 1000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.0.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}
diff --git a/generation_config.json b/generation_config.json
new file mode 100644
index 0000000..2cbf46e
--- /dev/null
+++ b/generation_config.json
@@ -0,0 +1,7 @@
+{
+  "bos_token_id": 151643,
+  "do_sample": false,
+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "5.0.0"
+}
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