Instructions to use prithivMLmods/JEV-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/JEV-9B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-classification", model="prithivMLmods/JEV-9B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/JEV-9B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use prithivMLmods/JEV-9B-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 prithivMLmods/JEV-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/JEV-9B-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 prithivMLmods/JEV-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf prithivMLmods/JEV-9B-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 prithivMLmods/JEV-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf prithivMLmods/JEV-9B-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 prithivMLmods/JEV-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf prithivMLmods/JEV-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/prithivMLmods/JEV-9B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use prithivMLmods/JEV-9B-GGUF with Ollama:
```
ollama run hf.co/prithivMLmods/JEV-9B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use prithivMLmods/JEV-9B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/JEV-9B-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": "prithivMLmods/JEV-9B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use prithivMLmods/JEV-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/JEV-9B-GGUF:Q4_K_M
```

Lemonade

How to use prithivMLmods/JEV-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull prithivMLmods/JEV-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.JEV-9B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use prithivMLmods/JEV-9B-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 prithivMLmods/JEV-9B-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 prithivMLmods/JEV-9B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use prithivMLmods/JEV-9B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf prithivMLmods/JEV-9B-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 "prithivMLmods/JEV-9B-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"

JEV-9B-GGUF

autotrust/JEV-9B is AutoTrust AI's first integrated System 1 + System 2 open model, built on a frozen, bit-identical Qwen3.5-9B backbone using a "Blocks of Experts" recipe: System 2 is ordinary text generation through the untouched base lm_head (70.7% HumanEval pass@1, identical to base Qwen3.5-9B with all 164 completions byte-identical), while System 1 is a small, detachable 40.2M-parameter LoRA plus a 24-slot fp32 decision head that answers typed noul (yes/no), choice (2–16 options), or score (0–5 scale) questions in a single forward pass, distilled from the closed, hosted TypeSafe Jev 1.13's own output distributions via the Apache-2.0 SargeDev/jev-distill-corpus-v3 corpus. On 25,376 Jev-labelled held-out rows, JEV-9B reaches a mean KL divergence of just ≈0.019 nats from the teacher's distributions (essentially indistinguishable at that resolution, including reproducing several of the teacher's known mistakes), 90.2% choice top-1 agreement, 0.994 noul AUROC, and an ECE of 0.0007 with no post-hoc correction needed, while also generalizing to unseen task families (KL 0.234, top-1 91.8% on out-of-distribution Open-Jev rows) and reaching 90–97% of the teacher's accuracy on an independent third-party benchmark with human gold labels. It is dramatically faster than the hosted API — a single decision takes ~90ms median versus 238–301ms for the hosted service, and one B200 GPU sustains ~15x the throughput — and both systems are served from one set of weights via a single vLLM engine, with a request routed to either path per-call; its larger sibling, autotrust/JEV-27B, trades some of this speed for closer teacher fidelity, better OOD transfer, and stronger System 2 generation (78.0% HumanEval). The model, its LoRA adapter, decision head, and training/evaluation reports are all released under Apache-2.0, and AutoTrust AI states it is an independent, unaffiliated reproduction sharing no code or weights with TypeSafe AI.

Model Files

File Name	Quant Type	File Size	File Link	Description
JEV-9B.BF16.gguf	BF16	17.9 GB	Link	Full BF16 weights. Highest quality, largest file size.
JEV-9B.Q3_K_L.gguf	Q3_K_L	4.93 GB	Link	Lower quality but usable, good for low RAM availability.
JEV-9B.Q3_K_M.gguf	Q3_K_M	4.62 GB	Link	Low quality.
JEV-9B.Q4_K_M.gguf	Q4_K_M	5.63 GB	Link	Good quality, default size for most use cases, recommended.
JEV-9B.Q4_K_S.gguf	Q4_K_S	5.35 GB	Link	Slightly lower quality with more space savings, recommended.
JEV-9B.Q5_K_M.gguf	Q5_K_M	6.47 GB	Link	High quality, recommended.
JEV-9B.Q5_K_S.gguf	Q5_K_S	6.31 GB	Link	High quality, recommended.
JEV-9B.Q6_K.gguf	Q6_K	7.36 GB	Link	Very high quality, near perfect, recommended.