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Fox-AI-by-teolm30/
fox1.2

Name: fox1.2
Availability: InStock

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Fox1.2 OpenClaw - 100% OpenClaw Tool Supported

A fine-tuned Qwen2.5-0.5B model optimized for OpenClaw agent tool execution.

Model Details

Base: Qwen2.5-0.5B-Instruct
Parameters: ~494M (compressed, efficient)
Context Length: 32,768 tokens
Size: ~994MB (F16)

Capabilities - All OpenClaw Tools Supported ✅

Core Tools

exec: Shell command execution (ls, cd, git, docker, npm, pip, python, node, etc.)
read: Read file contents (path required)
write: Write content to file (path, content required)
edit: Edit file by replacing exact text (path, oldText, newText required)

Process Management

process: Background session management (list, poll, write, send-keys, submit, paste, kill)

Web Operations

web_search: DuckDuckGo search (query, count, region, safeSearch)
web_fetch: Fetch and extract web content (url, extractMode, maxChars)

OpenClaw Management

session_status: Get session information
sessions_list: List active sessions (activeMinutes, kinds, limit, messageLimit)
sessions_history: Get conversation history (sessionKey, includeTools, limit)
sessions_send: Send messages between sessions (sessionKey/label, message)
sessions_spawn: Spawn sub-agents or ACP sessions (task, runtime, mode, etc.)
sessions_yield: End current turn
subagents: Manage spawned sub-agents (list, kill, steer)

Cron Jobs

cron: Job management (status, list, add, update, remove, run, runs, wake)

Memory

memory_search: Search memory files (query, maxResults, minScore)
memory_get: Retrieve memory content (path, from, lines)

Additional

image: Image analysis (image/images, prompt)
weather: Weather information (location)

Tool Call Format

When you need to execute a tool, respond with JSON in this format:

{"action": "tool_name", "param1": "value1", "param2": "value2"}

Examples

json

{"action": "exec", "command": "ls -la"}
{"action": "read", "path": "/home/user/README.md"}
{"action": "write", "path": "/home/user/test.txt", "content": "Hello World"}
{"action": "web_search", "query": "python tutorials"}
{"action": "weather", "location": "Athens"}
{"action": "cron", "action": "list"}
{"action": "session_status"}

Usage

Ollama

bash

# Create the model
ollama create fox1.2-openclaw -f Modelfile

# Run it
ollama run fox1.2-openclaw "list files in current directory"

Python/Transformers

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("teolm30/fox1.2-openclaw")
tokenizer = AutoTokenizer.from_pretrained("teolm30/fox1.2-openclaw")

# Generate tool call
inputs = tokenizer("List all files", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))

HuggingFace CLI

bash

# Download
huggingface-cli download teolm30/fox1.2-openclaw

# Or use curl
curl -L -o fox1.2-openclaw.gguf https://huggingface.co/teolm30/fox1.2-opencloak/resolve/main/fox1.2-openclaw.gguf

Why This Model?

Compact: 494M params - runs on consumer hardware (6GB VRAM)
Fast: Optimized for local inference
100% Tool Support: All OpenClaw agent tools supported
Smart Tool Selection: Knows when to use each tool appropriately
OpenClaw Native: Built specifically for OpenClaw integration

Training

Trained on 200+ examples covering all OpenClaw tool patterns:

File operations (read, write, edit)
Shell commands (exec)
Web operations (search, fetch)
Session management
Cron jobs
Memory operations
And more...

Optimized for tool call generation and execution in agent workflows.

OpenClaw Configuration

Add to your models.json: