commit b090b280ac7634b0794a8b3bbf5e022d109b7d58 Author: ModelHub XC Date: Sat May 9 14:34:37 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: enosislabs/midnight-mini-high-thinking-exp-gguf Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..4b8a3c4 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,38 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +unsloth.Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text +unsloth.Q8_0.gguf filter=lfs diff=lfs merge=lfs -text +unsloth.Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..62b26a7 --- /dev/null +++ b/README.md @@ -0,0 +1,311 @@ +--- +license: apache-2.0 +language: +- en +- es +- zh +tags: +- qwen +- qwen3-4b +- unsloth +- midnight-ai +- enosis-labs +- text-generation +- code-generation +- mathematics +- reasoning +- fine-tuned +- MMLU +- HumanEval +- HellaSwag +- Winogrande +- LAMBADA +- CEVAL +pipeline_tag: text-generation +model_name: Midnight Mini High Thinking GGIF +model_id: enosislabs/midnight-mini-high-thinking-exp-gguf +base_model: Qwen/Qwen3-4B +datasets: +- enosislabs/math-mini-shareGPT +- enosislabs/midnight-mini-think-shareGPT +library_name: transformers +--- + +# Midnight Mini High Thinking: Efficient Reasoning Architecture + +**Model ID:** `midnight-mini-high-thinking-05-25` +**Developed by:** Enosis Labs AI Research Division +**Model Version:** 05-25 (Production Release) +**Base Architecture:** Qwen3-4B + +## Executive Summary + +Midnight Mini High Thinking is a state-of-the-art causal language model engineered for complex reasoning applications within enterprise environments. This 4-billion parameter architecture delivers sophisticated analytical capabilities through advanced fine-tuning methodologies, demonstrating superior performance in mathematical computation, logical reasoning, and code synthesis tasks while maintaining computational efficiency for production deployment. + +## Technical Specifications + +### Core Architecture + +- **Base Model:** Qwen/Qwen3-4B +- **Parameter Count:** 4.02 billion trainable parameters +- **Model Type:** Autoregressive Transformer (Causal Language Model) +- **Fine-tuning Framework:** Unsloth optimization pipeline +- **Quantization Support:** Native 16-bit precision, GGUF quantized variants (Q4_K_M, Q5_K_M, Q8_0) +- **Maximum Context Length:** 32,768 tokens +- **Vocabulary Size:** 151,936 tokens +- **Attention Heads:** 32 (Multi-Head Attention) +- **Hidden Dimensions:** 2,048 +- **Feed-Forward Network Dimensions:** 11,008 + +### Performance Characteristics + +The model architecture incorporates several advanced optimizations: + +- **Enhanced Attention Mechanisms:** Specialized for multi-step reasoning workflows with improved long-range dependency modeling +- **Parameter-Efficient Fine-Tuning:** Utilizing LoRA (Low-Rank Adaptation) and QLoRA techniques for optimal training efficiency +- **Memory Optimization:** Gradient checkpointing and mixed-precision training for reduced memory footprint during inference +- **Inference Optimization:** Native support for key-value cache optimization and dynamic batching + +### Deployment Formats + +#### 16-bit Precision Model + +- **Memory Requirements:** ~8GB VRAM (inference) +- **Inference Speed:** ~150-200 tokens/second (RTX 4090) +- **Precision:** Full fp16 precision for maximum accuracy + +#### GGUF Quantized Variants + +- **Q4_K_M:** 2.6GB, optimal balance of quality and efficiency +- **Q5_K_M:** 3.2GB, enhanced quality with moderate compression +- **Q8_0:** 4.3GB, near-original quality with minimal compression + +## Core Capabilities & Design Objectives + +Midnight Mini High Thinking is specifically engineered for enterprise applications requiring sophisticated analytical capabilities: + +### Primary Capabilities + +- **Advanced Multi-Step Reasoning:** Demonstrates exceptional performance in complex logical sequences requiring iterative analysis and synthesis +- **Mathematical Computation & Analysis:** Excels in advanced mathematical operations, theorem proving, and quantitative analysis +- **Code Generation & Software Engineering:** Proficient in generating, debugging, and optimizing code across multiple programming languages +- **Technical Documentation Processing:** Advanced comprehension and generation of technical documentation, research papers, and analytical reports +- **Multilingual Intelligence:** Primary optimization for English with demonstrated capabilities in Spanish and Chinese for specialized tasks + +### Design Principles + +- **Ethical AI Framework:** Integrated safety mechanisms for responsible AI deployment +- **Bias Mitigation:** Advanced training protocols designed to minimize harmful biases and promote equitable outputs +- **Computational Efficiency:** Optimized for production environments with resource-conscious design +- **Scalability:** Architecture designed for horizontal scaling in enterprise deployments + +## Enterprise Applications & Use Cases + +Midnight Mini High Thinking is architected for professional environments requiring sophisticated analytical capabilities: + +### Primary Application Domains + +- **Advanced Mathematical Research:** Complex problem solving, theorem verification, mathematical proof assistance, and quantitative analysis +- **Software Engineering & Development:** Code generation, debugging assistance, architecture planning, and technical documentation +- **Business Intelligence & Analytics:** Data analysis interpretation, report generation, and strategic decision support +- **Academic Research Support:** Literature analysis, research methodology assistance, and technical writing enhancement +- **Educational Technology:** Advanced tutoring systems, curriculum development, and personalized learning assistance + +### Implementation Examples + +#### Mathematical Analysis Implementation + +```python +from transformers import AutoTokenizer, AutoModelForCausalLM +import torch + +# Initialize model with optimized settings +model_id = "enosislabs/midnight-mini-high-thinking-05-25" +tokenizer = AutoTokenizer.from_pretrained(model_id) +model = AutoModelForCausalLM.from_pretrained( + model_id, + torch_dtype=torch.float16, + device_map="auto" +) + +# Mathematical reasoning example +prompt = """Analyze the convergence properties of the Taylor series for e^x around x=0. +Provide a rigorous mathematical explanation including convergence radius and error bounds.""" + +inputs = tokenizer(prompt, return_tensors="pt") +with torch.no_grad(): + outputs = model.generate( + **inputs, + max_new_tokens=400, + temperature=0.7, + do_sample=True, + top_p=0.9 + ) + +response = tokenizer.decode(outputs[0], skip_special_tokens=True) +print(f"Mathematical Analysis:\n{response}") +``` + +#### Code Generation & Technical Documentation + +```python +# Advanced code generation with documentation +coding_prompt = """Design a Python class for implementing a thread-safe LRU cache +with TTL (time-to-live) functionality. Include comprehensive documentation +and error handling.""" + +inputs = tokenizer(coding_prompt, return_tensors="pt") +with torch.no_grad(): + outputs = model.generate( + **inputs, + max_new_tokens=500, + temperature=0.3, + do_sample=True + ) + +code_response = tokenizer.decode(outputs[0], skip_special_tokens=True) +print(f"Generated Solution:\n{code_response}") +``` + +## Training Methodology & Data Engineering + +### Training Infrastructure + +- **Base Model:** Qwen/Qwen3-4B +- **Fine-tuning Framework:** Unsloth optimization pipeline with custom extensions +- **Hardware Configuration:** Multi-GPU training environment (A100 80GB clusters) +- **Training Duration:** 72 hours of optimized training across distributed systems +- **Optimization Strategy:** Parameter-efficient fine-tuning with LoRA and gradient accumulation + +### Dataset Composition & Curation + +The training regimen incorporates a proprietary, meticulously curated dataset collection designed to enhance analytical capabilities: + +- **Mathematical Reasoning Corpus:** Advanced mathematical problems, proofs, and analytical reasoning chains +- **Code Generation Suite:** Multi-language programming challenges with comprehensive documentation requirements +- **Technical Documentation Archive:** Scientific papers, technical specifications, and analytical reports +- **Ethical Alignment Dataset:** Carefully curated examples promoting responsible AI behavior and bias mitigation +- **Multilingual Reasoning Collection:** Cross-linguistic reasoning tasks with emphasis on knowledge transfer + +### Training Optimization Techniques + +- **Gradient Checkpointing:** Memory-efficient training enabling larger effective batch sizes +- **Mixed Precision Training:** FP16 optimization for accelerated training without precision loss +- **Dynamic Learning Rate Scheduling:** Adaptive learning rate adjustment based on validation performance +- **Regularization Strategies:** Dropout, weight decay, and label smoothing for improved generalization + +## Performance Benchmarks & Evaluation Results + +Midnight Mini High Thinking has undergone comprehensive evaluation across industry-standard benchmarks, demonstrating exceptional performance characteristics for its parameter class. + +### Benchmark Results Overview + +| Benchmark Category | Task Specification | Metric | Score | Standard Error | +|:-------------------|:-------------------|:-------|:------|:---------------| +| **Code Generation** | | | | | +| | HumanEval | `pass@1` | 0.5920 | ±0.0389 | +| **Common Sense Reasoning** | | | | | +| | HellaSwag | `acc` | 0.5074 | ±0.0050 | +| | | `acc_norm` | 0.6782 | ±0.0047 | +| | Winogrande | `acc` | 0.6748 | ±0.0132 | +| **Language Modeling** | | | | | +| | LAMBADA OpenAI (English) | `acc` | 0.6218 | ±0.0068 | +| | | `perplexity` | 5.8048 | ±0.1720 | +| **Knowledge & Reasoning** | | | | | +| | MMLU (English) - General | `acc` | 0.6920 | ±0.0453 | +| | MMLU (English) - STEM | `acc` | 0.5870 | ±0.0734 | +| | MMLU (Spanish) - General | `acc` | 0.6050 | ±0.0246 | +| | MMLU (Spanish) - STEM | `acc` | 0.6304 | ±0.0720 | +| **Specialized Knowledge** | | | | | +| | CEVAL - Advanced Mathematics | `acc` | 0.5863 | ±0.1177 | + +### Performance Analysis + +**Code Generation Excellence:** The 59.2% pass@1 score on HumanEval demonstrates superior code synthesis capabilities, positioning the model among the top performers in its parameter class for software engineering applications. + +**Knowledge Integration:** MMLU performance of 69.2% (English) indicates strong knowledge retention and application across diverse domains, with particularly notable STEM performance in Spanish (63.04%) suggesting effective cross-linguistic knowledge transfer. + +**Reasoning Capabilities:** Winogrande accuracy of 67.48% and HellaSwag normalized accuracy of 67.82% demonstrate robust common-sense reasoning and contextual understanding. + +**Mathematical Proficiency:** CEVAL mathematics performance of 58.63% showcases specialized mathematical reasoning capabilities, particularly valuable for technical and scientific applications. + +## Model Limitations & Risk Assessment + +### Technical Constraints + +- **Knowledge Temporal Boundary:** Training data cutoff limits real-time information access and contemporary knowledge integration +- **Computational Resource Requirements:** 4B parameter architecture demands significant computational resources for optimal performance +- **Context Window Limitations:** 32,768 token limit may constrain processing of extremely large documents or extended conversations +- **Quantization Trade-offs:** GGUF variants exhibit quality degradation proportional to compression level + +### Performance Limitations + +- **Hallucination Potential:** Like all large language models, may generate factually incorrect or logically inconsistent outputs +- **Domain-Specific Accuracy:** Performance varies across specialized domains; validation recommended for critical applications +- **Language Proficiency Variance:** Optimal performance in English with graduated capabilities in Spanish and Chinese +- **Reasoning Depth Constraints:** Complex multi-step reasoning may occasionally exhibit logical gaps or incomplete analysis + +### Bias & Fairness Considerations + +- **Training Data Bias Inheritance:** May reflect societal biases present in training corpora despite mitigation efforts +- **Cultural Context Limitations:** Responses may exhibit Western-centric perspectives due to training data composition +- **Demographic Representation:** Potential underrepresentation of certain demographic groups in training examples +- **Professional Domain Bias:** May exhibit preferences toward certain professional or academic perspectives + +## Ethical Framework & Responsible AI Implementation + +### Safety Mechanisms + +- **Content Safety Filters:** Integrated mechanisms to identify and refuse harmful content generation +- **Bias Detection & Mitigation:** Ongoing monitoring for discriminatory outputs with corrective measures +- **Harmful Use Prevention:** Design features to discourage malicious applications and misuse +- **Privacy Protection:** No retention of user inputs or personal data during inference + +### Deployment Guidelines + +- **Human Oversight Requirement:** Critical decisions should maintain human validation and review +- **Domain-Specific Validation:** Professional applications require subject matter expert verification +- **Continuous Monitoring:** Regular assessment of outputs for quality and ethical compliance +- **User Education:** Clear communication of model capabilities and limitations to end users + +### Research Ethics Compliance + +Development adheres to established AI research ethics principles: + +- **Beneficence:** Designed to augment human capabilities and provide positive societal impact +- **Non-maleficence:** Active measures to prevent harmful applications and negative consequences +- **Autonomy:** Respects user agency while providing transparent information about model behavior +- **Justice:** Efforts to ensure equitable access and fair treatment across user populations + +## Technical Support & Model Citation + +### Model Attribution + +When utilizing Midnight Mini High Thinking in research or production environments, please cite: + +```bibtex +@software{midnight_mini_high_thinking_2025, + author = {Enosis Labs AI Research Division}, + title = { Midnight Mini High Thinking: Efficient Reasoning Architecture}, + version = {05-25}, + year = {2025}, + publisher = {Enosis Labs}, + url = {https://huggingface.co/enosislabs/midnight-mini-high-thinking-exp} +} +``` + +### Technical Support Channels + +For technical inquiries, deployment assistance, or research collaboration: + +- **Primary Contact:** +- **Model Repository:** [Hugging Face Model Hub](https://huggingface.co/enosislabs/midnight-mini-high-thinking-exp) + +### License & Distribution + +Licensed under Apache 2.0, permitting commercial use, modification, and distribution with appropriate attribution. + +--- + +**Enosis Labs AI Research Division** +*Advancing the frontiers of artificial intelligence through responsible innovation* \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..5d0c447 --- /dev/null +++ b/config.json @@ -0,0 +1,3 @@ +{ + "model_type": "qwen3" +} \ No newline at end of file diff --git a/unsloth.Q4_K_M.gguf b/unsloth.Q4_K_M.gguf new file mode 100644 index 0000000..4901650 --- /dev/null +++ b/unsloth.Q4_K_M.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:d808ad5fdaab342a1598678a88ea382cc64d4518fd7d38f6340bb1c5418e90d4 +size 2497280800 diff --git a/unsloth.Q5_K_M.gguf b/unsloth.Q5_K_M.gguf new file mode 100644 index 0000000..9ad4ca3 --- /dev/null +++ b/unsloth.Q5_K_M.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9e502bab7dd909225405af4cc82304d333e8e1f34c46c58e642ebd83c0023761 +size 2889513760 diff --git a/unsloth.Q8_0.gguf b/unsloth.Q8_0.gguf new file mode 100644 index 0000000..22ae09e --- /dev/null +++ b/unsloth.Q8_0.gguf @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:70093dfd8f5edadd05c5d948a6b16ab2cdad39aba70cc8c73c12ac8516aa98d1 +size 4280405280