Instructions to use vivek-nalliah/finsight-qwen2.5-14b-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 vivek-nalliah/finsight-qwen2.5-14b-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 vivek-nalliah/finsight-qwen2.5-14b-gguf
# Run inference directly in the terminal:
llama cli -hf vivek-nalliah/finsight-qwen2.5-14b-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vivek-nalliah/finsight-qwen2.5-14b-gguf
# Run inference directly in the terminal:
llama cli -hf vivek-nalliah/finsight-qwen2.5-14b-gguf

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 vivek-nalliah/finsight-qwen2.5-14b-gguf
# Run inference directly in the terminal:
./llama-cli -hf vivek-nalliah/finsight-qwen2.5-14b-gguf

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 vivek-nalliah/finsight-qwen2.5-14b-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vivek-nalliah/finsight-qwen2.5-14b-gguf

Use Docker

docker model run hf.co/vivek-nalliah/finsight-qwen2.5-14b-gguf

LM Studio
Jan

vLLM

How to use vivek-nalliah/finsight-qwen2.5-14b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "vivek-nalliah/finsight-qwen2.5-14b-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "vivek-nalliah/finsight-qwen2.5-14b-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/vivek-nalliah/finsight-qwen2.5-14b-gguf

Ollama
How to use vivek-nalliah/finsight-qwen2.5-14b-gguf with Ollama:
```
ollama run hf.co/vivek-nalliah/finsight-qwen2.5-14b-gguf
```
Unsloth Desktop
Docker Model Runner
How to use vivek-nalliah/finsight-qwen2.5-14b-gguf with Docker Model Runner:
```
docker model run hf.co/vivek-nalliah/finsight-qwen2.5-14b-gguf
```

Lemonade

How to use vivek-nalliah/finsight-qwen2.5-14b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vivek-nalliah/finsight-qwen2.5-14b-gguf

Run and chat with the model

lemonade run user.finsight-qwen2.5-14b-gguf-{{QUANT_TAG}}

List all available models

lemonade list

Atomic Chat

FinSight AI — Fine-tuned Qwen 2.5 14B (Q4_K_M GGUF)

A financial intelligence model fine-tuned on 527 historical market analysis training pairs spanning 2016–2026, covering market movements, economic indicators, geopolitical events, and their outcomes.

Model Details

Property	Value
Base Model	Qwen/Qwen2.5-14B-Instruct
Fine-tuning	LoRA (rank 64, alpha 128) via Unsloth
Training Data	527 weekly market analysis pairs (2016–2026)
Quantization	Q4_K_M via llama.cpp
File Size	~8.4 GB
Context Length	4096 tokens
Format	GGUF (compatible with Ollama, llama.cpp, LM Studio)

Training Data Sources

Yahoo Finance: Daily OHLCV for 25 major assets (S&P 500, NASDAQ, Gold, Oil, BTC, etc.)
FRED: Economic indicators (GDP, CPI, unemployment, fed funds rate, yield curve)
Wikipedia Current Events: 90,745 curated events across 122 months
GDELT 2.0: Global news coverage from 75 weeks

Usage with Ollama

# Download the GGUF file
# Create a Modelfile:
cat > Modelfile << 'EOF'
FROM ./finsight_q4km.gguf
PARAMETER temperature 0.3
PARAMETER num_ctx 4096
SYSTEM "You are FinSight, an expert AI financial analyst..."
EOF

ollama create finsight -f Modelfile
ollama run finsight "What are the market implications of rising inflation?"

Capabilities

Market Analysis: Interprets market movements with historical context
Historical Pattern Matching: References similar past periods when analyzing current events
Trend Prediction: Directional calls with confidence levels for major asset classes
Risk Assessment: Identifies risk factors based on historical parallels

Part of FinSight AI

This model powers FinSight AI, a real-time financial intelligence platform with RAG, vector search (Qdrant), and a live dashboard.

License

Apache 2.0 (same as Qwen 2.5 base model)

Downloads last month: -

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Model tree for vivek-nalliah/finsight-qwen2.5-14b-gguf

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Quantized

(184)

this model