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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("openalchemy/MachFund")
model = AutoModelForCausalLM.from_pretrained("openalchemy/MachFund", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use openalchemy/MachFund with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

Llama new
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 Desktop

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 serve -hf openalchemy/MachFund:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

How to use openalchemy/MachFund with OpenClaw:

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "openalchemy/MachFund:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MachFund / README.md

firstsko

Add model card with training details and usage examples

a97dde9 verified 7 months ago

preview code

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---
license: mit
language:
- zh
base_model:
- Qwen/Qwen2.5-3B-Instruct
pipeline_tag: text-generation
library_name: transformers
tags:
- finance
- chinese
- qlora
- private-equity
- fund-analysis
- distillation
metrics:
- 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
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

```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)

```bash
./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

	---
	license: mit
	language:
	- zh
	base_model:
	- Qwen/Qwen2.5-3B-Instruct
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- finance
	- chinese
	- qlora
	- private-equity
	- fund-analysis
	- distillation
	metrics:
	- 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
	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

	```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)

	```bash
	./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