Instructions to use vid-GENR/NanoWeb-7B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use vid-GENR/NanoWeb-7B-Instruct 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 vid-GENR/NanoWeb-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vid-GENR/NanoWeb-7B-Instruct:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf vid-GENR/NanoWeb-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf vid-GENR/NanoWeb-7B-Instruct: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 vid-GENR/NanoWeb-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf vid-GENR/NanoWeb-7B-Instruct: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 vid-GENR/NanoWeb-7B-Instruct:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf vid-GENR/NanoWeb-7B-Instruct:Q4_K_M

Use Docker

docker model run hf.co/vid-GENR/NanoWeb-7B-Instruct:Q4_K_M

LM Studio
Jan
Ollama
How to use vid-GENR/NanoWeb-7B-Instruct with Ollama:
```
ollama run hf.co/vid-GENR/NanoWeb-7B-Instruct:Q4_K_M
```
Unsloth Desktop

How to use vid-GENR/NanoWeb-7B-Instruct with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vid-GENR/NanoWeb-7B-Instruct: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": "vid-GENR/NanoWeb-7B-Instruct:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use vid-GENR/NanoWeb-7B-Instruct with Docker Model Runner:
```
docker model run hf.co/vid-GENR/NanoWeb-7B-Instruct:Q4_K_M
```

Lemonade

How to use vid-GENR/NanoWeb-7B-Instruct with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull vid-GENR/NanoWeb-7B-Instruct:Q4_K_M

Run and chat with the model

lemonade run user.NanoWeb-7B-Instruct-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use vid-GENR/NanoWeb-7B-Instruct with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vid-GENR/NanoWeb-7B-Instruct: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 vid-GENR/NanoWeb-7B-Instruct:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use vid-GENR/NanoWeb-7B-Instruct with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf vid-GENR/NanoWeb-7B-Instruct: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 "vid-GENR/NanoWeb-7B-Instruct: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"

NanoWeb-7B-Instruct (Phase 2 - Multi-Domain Distillation)

NanoWeb-7B-Instruct v2 is an autonomous, sovereign, refusal-abliterated browser-agent model fine-tuned on 8,280 multi-domain web flight-recorder trajectories (~31,000 tool calls) from base Qwen/Qwen2.5-7B-Instruct.

Interaction Domains Covered

E-Commerce & Price Intelligence: Product navigation, price comparison, specification extraction, stock alerts.
Academic & Technical Research: ArXiv paper synthesis, GitHub repository inspection, documentation navigation.
Financial & Market Intelligence: Ticker analysis, earnings report lookups, crypto metrics, macroeconomic indicators.
News & Geopolitical Tracking: Breaking news cross-referencing, primary source verification, fact-checking.
Travel, Hospitality & Flight Schedules: Routing queries, hotel amenity comparisons, booking workflow traversal.
Complex Multi-Hop Web Synthesis: Multi-source reconciliation, adversarial error recovery, dynamic DOM element interaction.

Tool Schemas

Pre-tuned with native Hermes function calling on:

fetch_page(url): Stealth headless retrieval with Jev-pruned numbered interactive DOM elements.
read_page(url, chunk_index): Sequential chunked reading for dense textual content.
find_in_page(url, pattern): Regex and keyword search across DOM elements.
browser_action(action, element_id, text, key, value): Interactive element execution (click, type, select, press_enter).
web_search(query): Multi-provider search querying.
finish_research(answer, sources): Final evidence synthesis.

Mechanistic Refusal Abliteration

Refusal representations are mathematically annihilated via in-place orthogonal subspace projection ($W \leftarrow (I - \hat{r}\hat{r}^T)W$) across layers 14, 15, 16, 17, 18, 19, and 20 on both MLP down-projections and self-attention output projections ($P_\perp W_{\text{down}}$ & $P_\perp W_o$), eliminating false refusals on sovereign research, web scraping, and DOM automation.

Quantized GGUF Deliverables (SHA-256 Verified)

nanoweb-7b-v2-instruct-Q4_K_M.gguf: ~4.5 GB (Optimal for consumer GPUs / Ollama / llama.cpp)
nanoweb-7b-v2-instruct-Q8_0.gguf: ~7.5 GB (High-precision near-FP16 fidelity)
Accompanying .sha256 manifest files for cryptographic integrity verification.

Downloads last month: -

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

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Model tree for vid-GENR/NanoWeb-7B-Instruct

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

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