Instructions to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT",
	filename="SOLARized-GraniStral-14B_1902_YeAM-HCT_Q4.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT 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 srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32
# Run inference directly in the terminal:
llama cli -hf srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32
# Run inference directly in the terminal:
llama cli -hf srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

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 srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32
# Run inference directly in the terminal:
./llama-cli -hf srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

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 srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32
# Run inference directly in the terminal:
./build/bin/llama-cli -hf srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

Use Docker

docker model run hf.co/srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

LM Studio
Jan

vLLM

How to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

Ollama
How to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT with Ollama:
```
ollama run hf.co/srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32
```

Unsloth Studio

How to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT 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 srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT 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 srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT to start chatting

How to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

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": "srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

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 srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT with Docker Model Runner:
```
docker model run hf.co/srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32
```

Lemonade

How to use srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT:F32

Run and chat with the model

lemonade run user.GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT-F32

List all available models

lemonade list

GGUF — SOLARized-GraniStral-14B_1902_YeAM-HCT (Beta)

WARNING! llama-cpp-python (0.3.16) DO NOT SUPPORT mistral3 arch! Use this: https://github.com/ggml-org/llama.cpp

This repository contains GGUF-only artifacts for convenience (search / indexing / quick downloads).

Main model repo (HF checkpoint + configs + tokenizer + templates + full docs):
https://huggingface.co/srs6901/SOLARized-GraniStral-14B_1902_YeAM-HCT

If you need the original weights, tokenizer files, chat templates, or anything beyond GGUF inference — use the main repo above.

Quant	File	Link
Q4_K	SOLARized-GraniStral-14B_1902_YeAM-HCT_Q4.gguf	download
Q5_K	SOLARized-GraniStral-14B_1902_YeAM-HCT_Q6.gguf	download
Q6_K	SOLARized-GraniStral-14B_1902_YeAM-HCT_Q8.gguf	download
FP32	mmproj-SOLARized-GraniStral-14B_1902_YeAM-HCT_F32.gguf	download

RU
EN
License

RU

Это репозиторий только с GGUF для удобства поиска/индексации и быстрого скачивания.

Основной репозиторий модели (полный HF чекпоинт + конфиги + токенизатор + шаблоны + документация):
https://huggingface.co/srs6901/SOLARized-GraniStral-14B_1902_YeAM-HCT

Что это за модель (коротко)

SOLARized-GraniStral-14B_1902_YeAM-HCT — экспериментальный beta-мердж на базе Ministral-3-14B-Instruct-2512 (text+vision) с примесью SOLAR и IBM Granite.

Что лежит в этом репозитории

*.gguf: готовые GGUF-кванты для llama.cpp/совместимых рантаймов.

GGUF / llama.cpp

Если модель начинает печатать literal [/INST], это обычно проблема метаданных токенизатора (pretok/token types) — см. заметки в основном репозитории модели.
Для мультимодальности в llama.cpp обычно нужен GGUF модели плюс отдельный mmproj GGUF (projector) — см. основной репозиторий.

Важно: мультимодальность llama.cpp для Pixtral/Mistral3 сейчас активно меняется; качество понимания изображений может быть некорректным даже если HF/Transformers работает правильно.

EN

This is a GGUF-only repository for convenience (search / indexing / quick downloads).

Main model repo (full HF checkpoint + configs + tokenizer + templates + docs):
https://huggingface.co/srs6901/SOLARized-GraniStral-14B_1902_YeAM-HCT

What this model is (short)

SOLARized-GraniStral-14B_1902_YeAM-HCT is an experimental beta merge built on top of Ministral-3-14B-Instruct-2512 (text+vision), with donor influence from SOLAR and IBM Granite.

Files in this repo

*.gguf: ready-to-use GGUF quants for llama.cpp and compatible runtimes.

GGUF / llama.cpp notes

If you see literal service tokens like [/INST] in output, it is almost always a tokenizer metadata issue (token types / pretok). See the main repo for the exact intended configuration.
For multimodal usage in llama.cpp, expect a model GGUF plus a separate mmproj GGUF (projector). See the main repo.

Important: llama.cpp multimodal support for Pixtral/Mistral3 is under heavy development. In practice, image understanding quality may be incorrect even when HF/Transformers works correctly.

License

Apache-2.0. Base model licenses apply for the corresponding upstream artifacts.

Downloads last month: 31

GGUF

Model size

14B params

Architecture

mistral3

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

Collection including srs6901/GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT

GGUF's

Collection

GGUF Collection for convenience • 4 items • Updated Feb 22

srs6901
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GGUF-SOLARized-GraniStral-14B_1902_YeAM-HCT