Instructions to use openalchemy/MachFund with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use openalchemy/MachFund with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="openalchemy/MachFund")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openalchemy/MachFund")
model = AutoModelForCausalLM.from_pretrained("openalchemy/MachFund")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use openalchemy/MachFund with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="openalchemy/MachFund",
	filename="gguf/mach-fund-1-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use openalchemy/MachFund with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf openalchemy/MachFund:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf openalchemy/MachFund:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf openalchemy/MachFund:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf openalchemy/MachFund:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf openalchemy/MachFund:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf openalchemy/MachFund:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf openalchemy/MachFund:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf openalchemy/MachFund:Q4_K_M

Use Docker

docker model run hf.co/openalchemy/MachFund:Q4_K_M

LM Studio
Jan

vLLM

How to use openalchemy/MachFund with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "openalchemy/MachFund"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openalchemy/MachFund",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/openalchemy/MachFund:Q4_K_M

SGLang

How to use openalchemy/MachFund with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "openalchemy/MachFund" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openalchemy/MachFund",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "openalchemy/MachFund" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "openalchemy/MachFund",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use openalchemy/MachFund with Ollama:
```
ollama run hf.co/openalchemy/MachFund:Q4_K_M
```

Unsloth Studio new

How to use openalchemy/MachFund with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for openalchemy/MachFund to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for openalchemy/MachFund to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for openalchemy/MachFund to start chatting

Pi new

How to use openalchemy/MachFund with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf openalchemy/MachFund:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "openalchemy/MachFund:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use openalchemy/MachFund with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf openalchemy/MachFund:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default openalchemy/MachFund:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use openalchemy/MachFund with Docker Model Runner:
```
docker model run hf.co/openalchemy/MachFund:Q4_K_M
```

Lemonade

How to use openalchemy/MachFund with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull openalchemy/MachFund:Q4_K_M

Run and chat with the model

lemonade run user.MachFund-Q4_K_M

List all available models

lemonade list

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

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

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

Downloads last month: 58

Safetensors

Model size

3B params

Tensor type

F16

Model tree for openalchemy/MachFund

Base model

Qwen/Qwen2.5-3B

Finetuned

Qwen/Qwen2.5-3B-Instruct

Quantized

(220)

this model

openalchemy
/

MachFund