Instructions to use AtomicChat/Ling-3.0-flash-Fin-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use AtomicChat/Ling-3.0-flash-Fin-GGUF 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 AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

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 AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

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 AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

Use Docker

docker model run hf.co/AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

LM Studio
Jan

vLLM

How to use AtomicChat/Ling-3.0-flash-Fin-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AtomicChat/Ling-3.0-flash-Fin-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AtomicChat/Ling-3.0-flash-Fin-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

Ollama
How to use AtomicChat/Ling-3.0-flash-Fin-GGUF with Ollama:
```
ollama run hf.co/AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K
```
Unsloth Desktop

How to use AtomicChat/Ling-3.0-flash-Fin-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

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": "AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use AtomicChat/Ling-3.0-flash-Fin-GGUF with Docker Model Runner:
```
docker model run hf.co/AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K
```

Lemonade

How to use AtomicChat/Ling-3.0-flash-Fin-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

Run and chat with the model

lemonade run user.Ling-3.0-flash-Fin-GGUF-Q6_K

List all available models

lemonade list

Hermes Agent

How to use AtomicChat/Ling-3.0-flash-Fin-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

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 AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use AtomicChat/Ling-3.0-flash-Fin-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K

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 "AtomicChat/Ling-3.0-flash-Fin-GGUF:Q6_K" \
  --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"

How to Run Ling 3.0 Flash Fin Locally

Built from InclusionAI's original weights with our own importance matrix. The calibration corpora behind our builds are public.

Ling 3.0 Flash Fin is InclusionAI's finance-enhanced Ling model for research, valuation, spreadsheets, and long-horizon agent workflows.
These GGUFs are self-quantized from InclusionAI's original BF16 weights with our own importance matrix.
The repo currently includes BF16, AD-Q8_0, and AD-Q6_K builds; lower-bit quants are still uploading.

Ling 3.0 Flash Fin, self-quantized to GGUF by Atomic Chat. It has 124B total parameters, activates 5.1B per token, and supports a 256K context window. The checkpoint extends Ling 3.0 Flash with continued training on high-quality financial data.

Highlights

End-to-end financial research: connects retrieval, evidence review, calculation, modeling, and report preparation in one workflow.
Source-grounded search: prioritizes authoritative sources and traceable answers. InclusionAI publishes FinFIRST for transparent evaluation.
Multi-document reasoning: reconciles periods, definitions, assumptions, and conflicting figures across filings, earnings materials, and research.
Valuation and spreadsheet workflows: understands formulas, estimate updates, cross-sheet dependencies, balance checks, and scenario analysis.
Research-ready output: separates facts, analysis, judgments, and charts into material that can be reviewed and edited.

These GGUFs are self-quantized from the original weights, not a repack.

Thinking mode is enabled by default. InclusionAI recommends temperature=1.0, top_p=0.95, and top_k=20.

Model overview

Property	Value
Base model	`inclusionAI/Ling-3.0-flash-Fin`
Type	Finance-enhanced mixture-of-experts language model
Total / active parameters	124B total / 5.1B active
Context length	256K tokens
Focus	Financial research, source review, valuation, spreadsheets, and agent workflows
This repo	GGUF builds made directly from the original BF16 checkpoint

Pick a file

Build	Download size	Notes
`AD-Q6_K`	108.5 GB	Recommended current download. Near-lossless and 30 GB smaller than Q8_0.
`AD-Q8_0`	138.5 GB	Reference-quality quant for machines with enough memory.
`BF16`	255.1 GB	Full-precision GGUF reference. Not intended for most local systems.

The download must fit alongside the context cache and runtime overhead. Leave several gigabytes of headroom beyond the file size.

Get started

Run Ling 3.0 Flash Fin locally with:

Atomic Chat: open the app, search AtomicChat/Ling-3.0-flash-Fin-GGUF, pick a build, and select Use this model.
llama.cpp: download the AD-Q6_K folder and open its first shard with llama-server.
LM Studio / Jan: search the repo ID and download the build that fits your machine.

Download the recommended build:

hf download AtomicChat/Ling-3.0-flash-Fin-GGUF \
  --include "AD-Q6_K/*" \
  --local-dir Ling-3.0-flash-Fin-GGUF

Run it:

llama-server \
  -m Ling-3.0-flash-Fin-GGUF/AD-Q6_K/Ling-3.0-flash-Fin-AD-Q6_K-00001-of-00003.gguf \
  --jinja -ngl 99 -c 32768

The chat template is embedded in the GGUF. Keep --jinja enabled so thinking mode and tool-call formatting are applied correctly.

Best practices

Parameter	Value
temperature	1.0
top_p	0.95
top_k	20

Allocate enough output length for research and agent tasks. Financial conclusions, valuation assumptions, and investment decisions still require professional review.

What the model is built for

The producer evaluates the model on FinFIRST, FinSearchComp Verified, FinCRAFT, Finance Agent, APEX-Agents, SpreadsheetBench, and tau3-Banking. These benchmarks cover source-grounded retrieval, investment research, long-horizon execution, valuation modeling, spreadsheet operations, and banking workflows.

See the official model card for the producer's benchmark results, deployment guidance, and limitations.

How these were made

Start from inclusionAI/Ling-3.0-flash-Fin, the original BF16 checkpoint.
Convert the checkpoint directly to GGUF.
Build a per-tensor importance matrix over the public Atomic Chat calibration corpora.
Quantize the shipped builds and validate them against the BF16 reference.

The raw evaluation logs currently available for the uploaded builds are included in the logs/ directory of this repository.

License

Released by InclusionAI under the MIT License. Quantized by Atomic Chat.

Downloads last month: 179

GGUF

Model size

127B params

Architecture

bailingmoe3

Hardware compatibility

6-bit

8-bit

16-bit

Model tree for AtomicChat/Ling-3.0-flash-Fin-GGUF

Base model

inclusionAI/Ling-3.0-flash

Finetuned

inclusionAI/Ling-3.0-flash-Fin

Quantized

(3)

this model