Instructions to use logic65/whittle-dev with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use logic65/whittle-dev 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 logic65/whittle-dev:Q8_0
# Run inference directly in the terminal:
llama cli -hf logic65/whittle-dev:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf logic65/whittle-dev:Q8_0
# Run inference directly in the terminal:
llama cli -hf logic65/whittle-dev: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 logic65/whittle-dev:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf logic65/whittle-dev: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 logic65/whittle-dev:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf logic65/whittle-dev:Q8_0

Use Docker

docker model run hf.co/logic65/whittle-dev:Q8_0

LM Studio
Jan
Ollama
How to use logic65/whittle-dev with Ollama:
```
ollama run hf.co/logic65/whittle-dev:Q8_0
```
Unsloth Desktop

How to use logic65/whittle-dev with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf logic65/whittle-dev: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": "logic65/whittle-dev:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use logic65/whittle-dev with Docker Model Runner:
```
docker model run hf.co/logic65/whittle-dev:Q8_0
```

Lemonade

How to use logic65/whittle-dev with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull logic65/whittle-dev:Q8_0

Run and chat with the model

lemonade run user.whittle-dev-Q8_0

List all available models

lemonade list

Hermes Agent

How to use logic65/whittle-dev with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf logic65/whittle-dev: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 logic65/whittle-dev:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use logic65/whittle-dev with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf logic65/whittle-dev: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 "logic65/whittle-dev: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"

whittle-dev

☕ Support this work

Whittle is built by one person on a grocery budget and rented GPU hours. If this research is useful to you, or you want to see it finished: ko-fi.com/davida81328. Every hour of GPU time goes straight into the next checkpoint, and every checkpoint, table and log lands in these repos.

Nightly training checkpoints (trainer state: HC/PLE/LoRA/gates) for Whittle-Next runs on Colab. colab1/latest.pt is overwritten as the run progresses; colab1/step<N>.pt are milestones (the earlier note on this card said every 1000 steps; the tree holds step200.pt to step4400.pt, every 200 steps, as the Whittle-Next-27B-A3B card states). Load with the trainer (RESUME_CKPT=), not as a standalone model.

Status

Development artefacts, not a release. These are the run directories referenced by the Whittle-Next-27B-A3B and Whittle-Qwen-3.8-35B-A3B cards: colab1/ is the v4.4 run of Whittle-Next-27B-A3B (its checkpoints and run-end table), and tbl1/, lw2/, lw5/ and agentfix2/ are the checkpoints of the same names described on the Whittle-Qwen-3.8-35B-A3B card. The other directories (colab2/, lw1/, lw3/, lw4/, agentfix/, ai2-archive/) are runs of the same line that no card describes.

What is here

colab1/ — step<N>.pt (22 milestones), latest.pt, ngram_table_final.npy.
colab2/ — step<N>.pt, latest.pt, ngram_table_final.npy, eval/ (CE curve, layer-wise eval logs and JSON, export log, TRAIN.log).
lw1/ — step<N>.pt, latest.pt, ngram_table_final.npy, eval/ (CE curve, layer-wise eval logs and JSON, export log, TRAIN.log).
lw2/, lw3/, lw4/, lw5/ — step<N>.pt, latest.pt, ngram_table.npy, ple_hash.json, TRAIN.log, eval/ (CE curve, layer-wise eval logs and JSON, math60_replies.jsonl + math60_score.txt, export/convert logs) and one Q8_0 GGUF per run: Whittle-Qwen-3.8-35B-A3B-lw2-Q8_0.gguf, …-lw3-…, …-lw4-…, …-lw5-….
tbl1/ — step<N>.pt, latest.pt, ngram_table.npy, ple_hash.json, eval/ (GGUF build and quantisation logs, gguf_sizes.json).
agentfix/ — latest.pt, step200.pt.
agentfix2/ — latest.pt, step300.pt, bf16/ (full weights, 14 safetensors shards + config, tokenizer and chat template) and Whittle-Qwen-3.8-35B-A3B-agentfix2-Q8_0.gguf.
ai2-archive/ — archived trainer states: reader7/, reader9/, reader11/, reader12/ (TRAIN.log, ngram_table.npy, ple_hash.json, and keep_final.pt for all but reader9), v3ref/ (resume_from.pt, ple_hash.json), v4/ (colab_onpolicy_next_step42.pt).

The checkpoints are trainer state, not standalone models: the trainer, exporter and the frozen body they apply to are described on the two model cards above.

Downloads last month: 193

GGUF

Model size

36B params

Architecture

qwen4exp

Hardware compatibility

8-bit

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