Instructions to use DreamBlooms/Tev1-0.8B-experimental-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 DreamBlooms/Tev1-0.8B-experimental-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 DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

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 DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

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 DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

Use Docker

docker model run hf.co/DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use DreamBlooms/Tev1-0.8B-experimental-GGUF with Ollama:
```
ollama run hf.co/DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0
```
Unsloth Desktop

How to use DreamBlooms/Tev1-0.8B-experimental-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

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": "DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DreamBlooms/Tev1-0.8B-experimental-GGUF with Docker Model Runner:
```
docker model run hf.co/DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0
```

Lemonade

How to use DreamBlooms/Tev1-0.8B-experimental-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

Run and chat with the model

lemonade run user.Tev1-0.8B-experimental-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use DreamBlooms/Tev1-0.8B-experimental-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 DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

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 DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DreamBlooms/Tev1-0.8B-experimental-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0

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 "DreamBlooms/Tev1-0.8B-experimental-GGUF:Q8_0" \
  --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"

Tev1-0.8B

Tev1-0.8B is an experimental decision model from Together AI: one document (the state), a question, and 2-24 labeled options in, a single option letter out. It is a supervised fine-tune of Qwen/Qwen3.5-0.8B that keeps the language model's standard next-token head and reads the answer letter after a short chat decision prompt. It is a Jev-inspired experiment, not a non-autoregressive Jev runtime.

This repository holds quantized GGUF conversions for CPU inference through dohnuts.cpp, a native C++ port on llama.cpp. It serves the same POST /v1/systemone wire format as the other decision models. No GPU or Python runtime is needed.

GGUF

File	Contents	Size
`tev1-f16.gguf`	F16 language model	1.4 GB
`tev1-Q8_0.gguf`	Q8_0 language model	774 MB
`tev1.json`	profile config and fitted temperature	46 B

tev1.json is required alongside the GGUF. The conversion is a full fine-tune, converted directly with --no-mtp; there is no adapter to merge and no pointer head.

git clone https://github.com/DreamBlooms/dohnuts.cpp
cd dohnuts.cpp
git submodule update --init --depth 1
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_NATIVE=ON
cmake --build build -j --target dohnuts-cli

build/dohnuts-cli --server --port 8080 \
  --model tev1-Q8_0.gguf --metadata tev1.json

Ask one state several questions (the same request shape as the other profiles):

curl http://127.0.0.1:8080/v1/systemone -H 'Content-Type: application/json' \
  -d '{"state":"Returns are allowed within 30 days. This purchase was 12 days ago.",
       "questions":{"window":{"type":"choice","instructions":"Is this return within the allowed window?",
         "criteria":{"yes":"Yes.","no":"No.","unknown":"Not enough information."}}}}'

The answer keeps the core fields (type, choice, probabilities, noul, score, confidence) and adds this model's certainty (with legend for score) under native.

Rebuild from the upstream checkpoint with scripts/build_tev1_gguf.sh, which converts the full fine-tune directly and quantizes to Q8_0. No retraining is involved.

Limits

The upstream checkpoint is experimental. Calibration, multilingual behavior, and out-of-distribution robustness have not been evaluated here.
Training mainly used 2-8 options; the interface allows up to 24, but the wider range is untested.
Generic chat is not the intended interface and may produce prose.

License

The base Qwen3.5-0.8B model is Apache-2.0. The upstream release license for the fine-tuned weights is being finalized; see the upstream model card for the current terms. This is an independent, Jev-inspired release and does not use Jev's answers as training labels.