Instructions to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with Transformers:

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

pipe = pipeline("text-generation", model="maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking")
model = AutoModelForCausalLM.from_pretrained("maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking")
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 maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking",
	filename="LFM2.5-1.2B-Thinking-F16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16
# Run inference directly in the terminal:
llama-cli -hf maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16
# Run inference directly in the terminal:
llama-cli -hf maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

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 maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16
# Run inference directly in the terminal:
./llama-cli -hf maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

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 maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

Use Docker

docker model run hf.co/maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

LM Studio
Jan

vLLM

How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

SGLang

How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking 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 "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking" \
    --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": "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking",
		"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 "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking" \
        --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": "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with Ollama:
```
ollama run hf.co/maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16
```

Unsloth Studio new

How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking 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 maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking 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 maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking to start chatting

Pi new

How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

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": "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

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 maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

Run Hermes

hermes

Docker Model Runner
How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with Docker Model Runner:
```
docker model run hf.co/maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16
```

Lemonade

How to use maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking:F16

Run and chat with the model

lemonade run user.LFM2.5-1.2B-Financial-Analyst-Thinking-F16

List all available models

lemonade list

LFM2.5-1.2B-Thinking-Financial-Analyst (LFM2.5 1.2B 金融分析专家版)

Overview | 概述

This model is a specialized version of the Liquid LFM2.5-1.2B-Thinking model, fine-tuned to act as a professional Financial Analyst. It is specifically optimized for analyzing Chinese A-share individual stocks, interpreting CFA-level financial principles, and generating structured investment logic.

本模型是基于 Liquid LFM2.5-1.2B-Thinking 的深度微调版本，旨在打造专业的金融分析助手。模型针对中国A股个股咨询、CFA专业财务知识以及结构化投资逻辑进行了深度优化。

What's New | 模型特性

Enhanced A-Share Analysis (A股深度分析): Learned the specific narrative style and logic of Chinese equity research reports. 更擅长以中国证券行研报告的风格和逻辑进行个股分析。
CFA Professional Knowledge (CFA专业知识支撑): Integrated high-quality data covering accounting standards, valuation models, and ethical frameworks from the CFA curriculum. 整合了涵盖会计准则、估值模型和CFA体系下的专业财务知识。
Thinking Process (逻辑推理过程): Retains and refines the "Thinking" capability of the base model, providing a step-by-step logical deduction before outputting the final financial conclusion. 继承并优化了原模型的“思考”能力，在给出金融结论前进行严密的逻辑推导。

Data & Direction | 微调资料与方向

The fine-tuning involved a vast amount of specialized financial data, moving away from general conversational AI toward a domain-specific expert:

Chinese Equity Research (中国行研数据): Massive collection of A-share individual stock analyses and market commentary. 累计了大量A股个股研报及市场评论。
CFA Knowledge Base (CFA财务知识库): Structured data on financial statement analysis, corporate finance, and accounting logic. 系统化的财务报表分析、公司理财及会计逻辑数据。
Specialized Financial Topics (金融专项课题): Deep dives into niches like Green Bonds (based on 2021 data) and the impact of cross-border capital flows. 涵盖绿色债券（基于2021年数据）及跨境资金流动影响等专项课题。

Origin | 模型渊源

Base Model (原模型): liquidai/lfm-2.5-1.2b-thinking.
Transformation (演变): Transformed from a general-purpose reasoning model into a structured, data-driven financial analyst. 从通用型逻辑模型演变为结构化、数据驱动的金融领域专家。

Usage | 使用方法

Option 1: LM Studio (Recommended)

Download the .gguf file.
- LFM2.5-1.2B-Thinking-F16.gguf: Full precision (Best quality, ~2.3GB).
- LFM2.5-1.2B-Thinking-Q8_0.gguf: 8-bit quantization (Faster, smaller, ~1.3GB).
Import via lms import or Drag & Drop.
The model is optimized for structured financial queries.

Option 2: Transformers (Python)

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "maximaverick/LFM2.5-1.2B-Financial-Analyst-Thinking" 
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, device_map="cuda")
tokenizer = AutoTokenizer.from_pretrained(model_id)

prompt = "User: 请从CFA财务分析角度，评价某A股公司的现金流质量。\n\nAssistant:"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
output = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(output[0]))

Disclaimer | 免责声明

This model is for informational purposes only and does not constitute financial advice. Small models (1.2B) may produce hallucinations; always verify critical data. 本模型仅供参考，不构成任何投资建议。1.2B量级模型可能产生幻觉，请务必核实关键数据。

Project Links

GitHub Repository: https://github.com/SirusAI/LFM2.5-Financial-Analyst-Finetune.git

Downloads last month: 185

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

8-bit

16-bit

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