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"

firstsko commited on Apr 1

Commit

a97dde9

verified ·

1 Parent(s): 6d8c2c4

Add model card with training details and usage examples

Browse files

Files changed (1) hide show

README.md +121 -18

README.md CHANGED Viewed

@@ -1,18 +1,121 @@
----
-  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
-  ---

+---
+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