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

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

python

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

bash

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

openalchemy/MachFund

MachFund-1

Overview