| ---
|
| license: mit
|
| language:
|
| - zh
|
| base_model:
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| - Qwen/Qwen2.5-3B-Instruct
|
| pipeline_tag: text-generation
|
| library_name: transformers
|
| tags:
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| - finance
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| - chinese
|
| - qlora
|
| - private-equity
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| - fund-analysis
|
| - distillation
|
| metrics:
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| - loss
|
| ---
|
|
|
| # MachFund-1
|
|
|
| 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.
|
|
|
| ## 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
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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
|
|
|
| ## Evaluation
|
|
|
| | Gate | Metric | Result |
|
| |---|---|---|
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| | 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
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| from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
| model = AutoModelForCausalLM.from_pretrained("openalchemy/MachFund", torch_dtype="auto", device_map="auto")
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| tokenizer = AutoTokenizer.from_pretrained("openalchemy/MachFund")
|
|
|
| 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"}
|
| ]
|
|
|
| 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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| ```
|
|
|
| ### llama.cpp (GGUF)
|
|
|
| ```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
|
| ```
|
|
|
| ### Ollama
|
|
|
| ```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?"
|
| ```
|
|
|
| ## Limitations
|
|
|
| - 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
|
|
|
| ## License
|
|
|
| MIT
|
|
|
