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
Teacher Model: Gemini 2.5 Pro generates ~50 Q&A pairs per fund across 8 categories for 178 Chinese private equity funds
Quality Scoring: Gemini 2.5 Flash scores each pair on 5 dimensions (accuracy, completeness, professionalism, data usage, coherence) with a threshold of 15/25
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
fromtransformersimportAutoModelForCausalLM,AutoTokenizermodel=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
