Instructions to use CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1",
	filename="gguf/llama-3-8b.Q4_K_M.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1: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 CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1: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 CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M

Use Docker

docker model run hf.co/CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M

LM Studio
Jan
Ollama
How to use CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 with Ollama:
```
ollama run hf.co/CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M
```

Unsloth Studio

How to use CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 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 CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 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 CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 to start chatting

Docker Model Runner
How to use CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 with Docker Model Runner:
```
docker model run hf.co/CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M
```

Lemonade

How to use CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3-8B-Quant-Reasoning-v1.1-Q4_K_M

List all available models

lemonade list

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

🌀 CelesteImperia Financial Engine v1.1 (Spiral)

The Quant-Reasoning LoRA for Indian Equities

This is a specialized Low-Rank Adaptation (LoRA) for Llama-3-8B, fine-tuned on a proprietary dataset of quantitative reasoning chains for the Indian stock market (NSE/BSE).

⚠️ IMPORTANT UPDATE (April 2026): > A significantly upgraded version of this model is now available. Indian Finance Quant v2.1 - DeepSeek Reasoning Edition has been released, featuring a "Dual-Brain" architecture capable of synthesizing technical indicators, live market sentiment, and complex regulatory/legal constraints using native <think> reasoning chains. Please migrate to v2.1 for all "Indian Finance Quant" deployments.

🚀 Key Features

Chain-of-Thought Reasoning: This engine uses tags to break down RSI, Support/Resistance, and Momentum before giving a verdict.
NSE/BSE Optimized: Understands the nuances of Indian market sentiment and large-cap stock behavior (e.g., Reliance, HDFC, TCS).
Lightweight & Fast: Optimized via Unsloth for 4-bit inference on consumer GPUs.

📂 Repository Structure

To support various algorithmic trading and scraping pipelines, this repository contains three distinct formats:

/adapter: The raw LoRA adapter weights (.safetensors).
/full_model: The fully merged and sharded model weights (4 parts).
/gguf: A compressed Q4_K_M version optimized for CPU-bound inference (Hugging Face Spaces Free Tier).

💻 Training Infrastructure

The v1.1 weights were trained locally in a dedicated WSL2 environment utilizing a dual-GPU architecture:

Primary Compute: NVIDIA RTX 3090
Secondary Accelerator: NVIDIA RTX A4000

🛠️ Usage & Quick Start

Option A: GPU Inference (Unsloth)

from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained("CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1")

Option B: CPU/Edge Inference (GGUF)

from huggingface_hub import hf_hub_download from llama_cpp import Llama

model_path = hf_hub_download( repo_id="CelesteImperia/Llama-3-8B-Quant-Reasoning-v1.1", filename="gguf/llama-3-8b.Q4_K_M.gguf" ) llm = Llama(model_path=model_path, n_ctx=2048)

📜 Terms of Use & Licensing

Base License: Subject to the Meta Llama-3 Community License.
Financial Data License: Reasoning layers are proprietary to CelesteImperia (CC BY-NC 4.0).
Non-Commercial Restriction: You may not use this model for commercial trading services or paid signal groups without consent.
Authenticity Tracking: All outputs contain visible and invisible steganographic watermarks to identify usage.
Disclaimer: This is an AI research tool. It does not provide financial advice. CelesteImperia is not responsible for financial losses.

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

4-bit

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