Instructions to use john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF",
	filename="Mistral-7B-v0.3-RYS-18-23-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF: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 john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF: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 john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M

Use Docker

docker model run hf.co/john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF with Ollama:
```
ollama run hf.co/john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M
```

Unsloth Studio new

How to use john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF 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 john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF 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 john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF to start chatting

Docker Model Runner
How to use john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF with Docker Model Runner:
```
docker model run hf.co/john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M
```

Lemonade

How to use john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull john-broadway/Mistral-7B-v0.3-RYS-18-23-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-7B-v0.3-RYS-18-23-GGUF-Q4_K_M

List all available models

lemonade list

Mistral-7B-v0.3-RYS-18-23

Mistral-7B-Instruct-v0.3 with layers 18-22 duplicated. The mid-late stack reasoning circuit — the same depth-position as the Llama-3.1-8B reasoning circuit — runs twice on every forward pass.

32 base layers → 37 after duplication. No training, no merging, no weight changes.

Reasoning 41.18% → 58.83% (+17.65). Math 0.534 → 0.5856 (+5.16). EQ 88.52 → 87.19 (−1.33).

Results

Metric	Baseline	RYS (18,23)	Delta
Math	0.534	0.5856	+5.16
EQ	88.52	87.19	−1.33
Reasoning	41.18%	58.83%	+17.65

The mid-baseline reasoning lift. Mistral-7B-v0.3 places its primary reasoning circuit at the same depth-fraction as Llama-3.1-8B (both peak at layers ~18-22 of a 32-layer stack, block-size 4-5). The position is shared across architectures. The magnitude differs: Mistral's weaker reasoning baseline (41.18% vs Llama's 82.35%) gives it more recoverable headroom, and 28 of 66 configurations boost reasoning >5% (vs Llama-3.1-8B's 15 boosters). Position is architecture-determined; magnitude is baseline-determined.

Pick this when you want strong reasoning out of a 7B class model with minimal EQ trade-off.

Usage

llama-server -m Mistral-7B-v0.3-RYS-18-23-Q4_K_M.gguf -ngl 99

Full sweep data

66 configurations tested. (18,23) block-5 is the best-combined pick. Full per-config sweep + cross-architecture analysis: v2 dataset.

Part of the RYS Sovereign Collection v2.

Where this sits in the Sovereign Collection

v1 — Qwen2.5 cross-scale + Qwen3-32B headline crossover. 5 model repos.

v2 — cross-architecture corpus. 21 model variants across 10 architecture families. Inverse correlation (r = −0.726): weak baselines lift more, in their weakest dimension. Mistral's matched-position / different-magnitude relationship with Llama-3.1-8B is one of the cleanest demonstrations of the position-vs-magnitude factorization. 13 deployable RYS-applied weight repos covering every non-zero-lift variant.

Cross-architecture comparator: john-broadway/Llama-3.1-8B-RYS-18-22-GGUF — same circuit-position, stronger baseline, modest lift.

Credit

John Broadway, with collaboration from Claude (Opus 4.6 in April 2026 sweep generation and build pipeline; Opus 4.7 in May 2026 cross-architecture analysis and publication). Original RYS method by David Ng on Qwen2-72B; sweep + probe toolkit by alainnothere.