初始化项目，由ModelHub XC社区提供模型
Model: openalchemy/MachFund Source: Original Platform
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README.md
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README.md
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---
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license: mit
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language:
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- zh
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base_model:
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- Qwen/Qwen2.5-3B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- finance
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- chinese
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- qlora
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- private-equity
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- fund-analysis
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- distillation
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metrics:
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- loss
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---
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# MachFund-1
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A specialized Chinese private equity fund analysis model, fine-tuned from [Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct) using QLoRA knowledge distillation.
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## Overview
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MachFund-1 is trained to analyze Chinese private equity funds across multiple dimensions: performance analysis, risk assessment, strategy evaluation, manager background, fund comparisons, and investment advice. The model demonstrates a **68.75% improvement** over the base model on domain-specific tasks.
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## Training Details
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| Parameter | Value |
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|---|---|
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| Base Model | Qwen2.5-3B-Instruct |
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| Method | QLoRA (4-bit NF4 quantization) |
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| LoRA Rank / Alpha | 32 / 64 |
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| Training Samples | 6,976 (eval: 769) |
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| Effective Batch Size | 16 (2 x 8 grad accumulation) |
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| Learning Rate | 2e-4 (cosine schedule) |
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| Epochs | 2 |
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| Max Sequence Length | 6,144 tokens |
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| Final Training Loss | 0.9269 |
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| Training Time | 141 min on NVIDIA A100 80GB |
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| Total Steps | 872 |
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### Knowledge Distillation Pipeline
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1. **Teacher Model**: Gemini 2.5 Pro generates ~50 Q&A pairs per fund across 8 categories for 178 Chinese private equity funds
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2. **Quality Scoring**: Gemini 2.5 Flash scores each pair on 5 dimensions (accuracy, completeness, professionalism, data usage, coherence) with a threshold of 15/25
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3. **Student Training**: QLoRA fine-tuning on 6,976 high-quality filtered samples
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### Question Categories
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- Fund overview and basic information
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- Performance analysis and benchmarking
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- Risk assessment and drawdown analysis
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- Strategy analysis and market positioning
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- Manager background and track record
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- Fund comparisons (peer and category)
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- Investment advice and suitability
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- Structured data extraction
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## Evaluation
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| Gate | Metric | Result |
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|---|---|---|
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| Training Lift | Base vs Fine-tuned Score | **PASS** (4.8 to 8.1, +68.75%, threshold: 30%) |
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| Speed (FP16) | Tokens/sec on RTX 5080 | 30.1 tok/s (threshold: 50) |
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## Available Formats
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| Format | File | Size | Use Case |
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|---|---|---|---|
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| SafeTensors (FP16) | `model.safetensors` | 6.17 GB | Full precision inference |
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| GGUF Q8_0 | `gguf/mach-fund-1-Q8_0.gguf` | 3.29 GB | High-quality quantized inference |
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| GGUF Q4_K_M | `gguf/mach-fund-1-Q4_K_M.gguf` | 1.93 GB | Efficient inference, recommended |
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| GGUF F16 | `gguf/mach-fund-1-f16.gguf` | 6.18 GB | Full precision GGUF |
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## Usage
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### Transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("openalchemy/MachFund", torch_dtype="auto", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained("openalchemy/MachFund")
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messages = [
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{"role": "system", "content": "You are a professional private equity fund analyst."},
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{"role": "user", "content": "Analyze the performance of this fund"}
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]
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text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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inputs = tokenizer(text, return_tensors="pt").to(model.device)
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outputs = model.generate(**inputs, max_new_tokens=1024)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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### llama.cpp (GGUF)
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```bash
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./llama-cli -m mach-fund-1-Q4_K_M.gguf -p "Analyze the risk profile of this fund" -n 512
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```
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### Ollama
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```bash
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echo 'FROM ./mach-fund-1-Q4_K_M.gguf' > Modelfile
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ollama create machfund -f Modelfile
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ollama run machfund "What is the Sharpe ratio of this fund?"
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```
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## Limitations
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- Trained specifically on Chinese private equity fund data; may not generalize to other financial domains
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- Training data reflects fund information available up to early 2026
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- Should not be used as the sole basis for investment decisions
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- Speed on consumer GPUs (RTX 5080) is below the 50 tok/s target at FP16; use GGUF Q4_K_M for faster inference
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## License
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MIT
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