MachFund-1

A specialized Chinese private equity fund analysis model, fine-tuned from Qwen2.5-3B-Instruct using QLoRA knowledge distillation.

Overview

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.

Training Details

Parameter	Value
Base Model	Qwen2.5-3B-Instruct
Method	QLoRA (4-bit NF4 quantization)
LoRA Rank / Alpha	32 / 64
Training Samples	6,976 (eval: 769)
Effective Batch Size	16 (2 x 8 grad accumulation)
Learning Rate	2e-4 (cosine schedule)
Epochs	2
Max Sequence Length	6,144 tokens
Final Training Loss	0.9269
Training Time	141 min on NVIDIA A100 80GB
Total Steps	872

Knowledge Distillation Pipeline

1. Teacher Model: Gemini 2.5 Pro generates ~50 Q&A pairs per fund across 8 categories for 178 Chinese private equity funds
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
3. Student Training: QLoRA fine-tuning on 6,976 high-quality filtered samples

Question Categories

• Fund overview and basic information
• Performance analysis and benchmarking
• Risk assessment and drawdown analysis
• Strategy analysis and market positioning
• Manager background and track record
• Fund comparisons (peer and category)
• Investment advice and suitability
• Structured data extraction

Evaluation

Gate	Metric	Result
Training Lift	Base vs Fine-tuned Score	PASS (4.8 to 8.1, +68.75%, threshold: 30%)
Speed (FP16)	Tokens/sec on RTX 5080	30.1 tok/s (threshold: 50)

Available Formats

Format	File	Size	Use Case
SafeTensors (FP16)	model.safetensors	6.17 GB	Full precision inference
GGUF Q8_0	gguf/mach-fund-1-Q8_0.gguf	3.29 GB	High-quality quantized inference
GGUF Q4_K_M	gguf/mach-fund-1-Q4_K_M.gguf	1.93 GB	Efficient inference, recommended
GGUF F16	gguf/mach-fund-1-f16.gguf	6.18 GB	Full precision GGUF

Usage

Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("openalchemy/MachFund", torch_dtype="auto", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained("openalchemy/MachFund")

messages = [
    {"role": "system", "content": "You are a professional private equity fund analyst."},
    {"role": "user", "content": "Analyze the performance of this fund"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

llama.cpp (GGUF)

./llama-cli -m mach-fund-1-Q4_K_M.gguf -p "Analyze the risk profile of this fund" -n 512

Ollama

echo 'FROM ./mach-fund-1-Q4_K_M.gguf' > Modelfile
ollama create machfund -f Modelfile
ollama run machfund "What is the Sharpe ratio of this fund?"

Limitations

• Trained specifically on Chinese private equity fund data; may not generalize to other financial domains
• Training data reflects fund information available up to early 2026
• Should not be used as the sole basis for investment decisions
• Speed on consumer GPUs (RTX 5080) is below the 50 tok/s target at FP16; use GGUF Q4_K_M for faster inference

License

MIT