Instructions to use SargeDev/Solar-JEV-27B-R32-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 SargeDev/Solar-JEV-27B-R32-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 SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SargeDev/Solar-JEV-27B-R32-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 SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SargeDev/Solar-JEV-27B-R32-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 SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M

Use Docker

docker model run hf.co/SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use SargeDev/Solar-JEV-27B-R32-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SargeDev/Solar-JEV-27B-R32-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": "SargeDev/Solar-JEV-27B-R32-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M

Ollama
How to use SargeDev/Solar-JEV-27B-R32-GGUF with Ollama:
```
ollama run hf.co/SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
```
Unsloth Desktop

How to use SargeDev/Solar-JEV-27B-R32-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M

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": "SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use SargeDev/Solar-JEV-27B-R32-GGUF with Docker Model Runner:
```
docker model run hf.co/SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M
```

Lemonade

How to use SargeDev/Solar-JEV-27B-R32-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Solar-JEV-27B-R32-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use SargeDev/Solar-JEV-27B-R32-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 SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M

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 SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use SargeDev/Solar-JEV-27B-R32-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M

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 "SargeDev/Solar-JEV-27B-R32-GGUF:Q4_K_M" \
  --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"

Solar-JEV-27B-R32-GGUF

GGUF ladder for the r32-only build — the one with the decision readout trained INTO the model (lm_head LoRA), and the best-measured build of the Solar JEV family.

These are the same weights as SargeDev/Solar-JEV-27B-R32 (bf16) and SargeDev/JEV-27B-Uncensored; this repo is the quantized form.

Sizes

quant	size	decode tok/s (prompt/tokens)
IQ4_XS	14.35 GiB	963.27 / 13.93
Q2_K	10.11 GiB	753.44 / 18.29
Q3_K_L	13.55 GiB	798.24 / 14.01
Q3_K_M	12.56 GiB	821.83 / 14.99
Q3_K_S	11.40 GiB	805.38 / 16.28
Q4_K_M	15.65 GiB	859.45 / 12.52
Q4_K_S	14.73 GiB	907.39 / 13.32
Q5_K_M	18.18 GiB	829.93 / 11.19
Q5_K_S	17.66 GiB	851.30 / 11.54
Q6_K	20.88 GiB	726.09 / 9.79
Q8_0	27.04 GiB	808.14 / 8.16

MTP/nextn drafter tensors are preserved in every quant, so speculative decoding works in llama.cpp.

Which one to pick

you want	repo
the best decision model, quantized	this repo
the best decision model, bf16	`SargeDev/Solar-JEV-27B-R32`
the r32+solar merge (r32 + our solar adapter)	`SargeDev/Solar-Decision-JEV-27B`
the solar-only build (our adapter alone)	`SargeDev/Solar-JEV-27B`
the vision build	`SargeDev/Solar-Decision-JEV-27B-VL`

The decision-gate numbers for all of them are on the bf16 cards, measured on the same 27,695 rows with the same harness and the same head + calibration.

Quick start

llama-server -m Solar-JEV-27B-R32.Q4_K_M.gguf -ngl 99 --jinja \
  --chat-template-kwargs '{"enable_thinking": false}'

Typed decisions: use the bf16 repo for those

The typed-decision path needs the model's own lm_head logprobs over the 24 verbalizer token ids, which works in vLLM (the bf16 repo, verified end-to-end). The llama.cpp server in this build does not return token logprobs - we measured /v1/completions, the native /completion endpoint, and echo: true, and each returns a payload with no logprobs field. So read typed decisions from the bf16 repo, and use this ladder for fast everyday chat.

Downloads last month: 695

GGUF

Model size

27B params

Architecture

qwen35

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for SargeDev/Solar-JEV-27B-R32-GGUF

Base model

Qwen/Qwen3.8-27B

Finetuned

huihui-ai/Huihui-Qwen3.8-27B-abliterated

Finetuned

SargeDev/Solar-JEV-27B-R32

Quantized

(1)

this model

SargeDev
/

Solar-JEV-27B-R32-GGUF

Solar-JEV-27B-R32-GGUF

Sizes

Which one to pick

Quick start

Typed decisions: use the bf16 repo for those

Model tree for SargeDev/Solar-JEV-27B-R32-GGUF

Dataset used to train SargeDev/Solar-JEV-27B-R32-GGUF