Instructions to use Evoke3962/dream-0.6b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Evoke3962/dream-0.6b-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 Evoke3962/dream-0.6b-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Evoke3962/dream-0.6b-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 Evoke3962/dream-0.6b-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf Evoke3962/dream-0.6b-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 Evoke3962/dream-0.6b-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Evoke3962/dream-0.6b-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 Evoke3962/dream-0.6b-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Evoke3962/dream-0.6b-gguf:Q4_K_M
Use Docker
docker model run hf.co/Evoke3962/dream-0.6b-gguf:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Evoke3962/dream-0.6b-gguf with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Evoke3962/dream-0.6b-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": "Evoke3962/dream-0.6b-gguf", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Evoke3962/dream-0.6b-gguf:Q4_K_M
- Ollama
How to use Evoke3962/dream-0.6b-gguf with Ollama:
ollama run hf.co/Evoke3962/dream-0.6b-gguf:Q4_K_M
- Unsloth Desktop
- Pi
How to use Evoke3962/dream-0.6b-gguf with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Evoke3962/dream-0.6b-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": "Evoke3962/dream-0.6b-gguf:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use Evoke3962/dream-0.6b-gguf with Docker Model Runner:
docker model run hf.co/Evoke3962/dream-0.6b-gguf:Q4_K_M
- Lemonade
How to use Evoke3962/dream-0.6b-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Evoke3962/dream-0.6b-gguf:Q4_K_M
Run and chat with the model
lemonade run user.dream-0.6b-gguf-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use Evoke3962/dream-0.6b-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 Evoke3962/dream-0.6b-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 Evoke3962/dream-0.6b-gguf:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use Evoke3962/dream-0.6b-gguf with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf Evoke3962/dream-0.6b-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 "Evoke3962/dream-0.6b-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"
dream-0.6b โ a bedtime storyteller that fits in a browser tab
A 0.6B-parameter model that writes slow, gentle, plotless sleep stories for grown-ups. Built to run fully client-side in the browser (via wllama / WebAssembly) as the story engine behind sleepcast.pro's dream feature โ one ~400MB download, then infinite offline stories with nothing leaving the device.
What it does
Give it a scene ("a greenhouse in soft rain", "a night ferry crossing calm water") and it wanders: soft sensory prose, no plot, no urgency, no cliffhangers โ words designed to be listened to while falling asleep.
Prompt format
This is a completion model (not chat). Use exactly this frame, including the primed opening line โ the model continues from it:
A slow, gentle bedtime story for grown-ups. No plot, no urgency โ soft words that wander until the listener drifts off.
Topic: {your scene}
Story:
Let yourself sink down into the quiet. Wherever you are right now, let it hold you. There's nowhere to be and nothing to do. Just breathe, slow and easy, and let these words carry you somewhere soft and far away.
Recommended sampling: temperature 0.8, top_p 0.95. The full intro prime
matters โ shorter primes let the base model's multilingual pretraining leak
through at the seam.
llama.cpp
llama-completion -m dream-0.6b-Q4_K_M.gguf -p "$(cat prompt.txt)" \
-n 2000 --temp 0.8 --top-p 0.95
LM Studio
Import the GGUF, use completion/playground mode with the frame above.
How it was made
Knowledge distillation from a two-stage local pipeline:
- A Qwen3-4B-Base + QLoRA adapter (trained on sleep-podcast-style narration) generated 120 raw stories across a 30-place ร 8-texture topic grid.
- Gemma rewrote each one clean โ consistent register, fixed intro/outro brackets, no artifacts โ yielding 119 records (~7.2k chars each).
- Qwen3-0.6B-Base was LoRA-fine-tuned (r32, ฮฑ64, 8 epochs, bf16) on the clean set, merged, and quantized to Q4_K_M with llama.cpp.
The entire pipeline ran on a single 8GB consumer GPU.
Files
v2 (current) โ trained on a 300-story, 60-topic variety-first dataset;
real topic-following (v1 mode-collapsed toward one generic story). v2 has
NO fixed intro: the prompt ends bare at Story: and the first few tokens
may stutter โ trim short leading lines. Use repeat penalty ~1.1.
| file | size | use |
|---|---|---|
dream2-0.6b-Q4_K_M.gguf |
~397MB | browser (wllama), llama.cpp, LM Studio |
dream2-1.7b-Q6_K.gguf |
~1.4GB | higher quality; Q4 breaks this one, use Q6 |
dream2-4b-chat-Q8_0.gguf |
~4.3GB | desktop chat: send a topic as the user message |
adapters/dream2-*.lora.gguf |
40-130MB | attach to stock Qwen3 bases via --lora |
4B chat system prompt: "You are a bedtime storyteller for grown-ups. When given a topic, tell a slow, gentle, plotless story in soft wandering words until the listener drifts off. Never break character, never explain, just tell the story."
v1 (legacy)
| file | size | use |
|---|---|---|
dream-0.6b-Q4_K_M.gguf |
~397MB | v1: single-voice, weak topic-following; needs the primed intro (see git history) |
Limitations
- English only; single register (soporific narration). It will not answer questions, follow instructions, or hold a conversation โ by design.
- No chat template training; chat-mode use produces off-register output.
- Small model: occasional soft non-sequiturs. In its intended use (drowsy listening) these read as features.
- Not for factual, medical, or advisory content of any kind.
Lineage & license
Base model Qwen/Qwen3-0.6B-Base (Apache 2.0). Fine-tune and distillation data produced by the sleepcast pipeline.
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