Instructions to use bfuzzy1/TinyGuide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bfuzzy1/TinyGuide with MLX:

# Make sure mlx-lm is installed
# pip install --upgrade mlx-lm

# Generate text with mlx-lm
from mlx_lm import load, generate

model, tokenizer = load("bfuzzy1/TinyGuide")

prompt = "Write a story about Einstein"
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
    messages, add_generation_prompt=True
)

text = generate(model, tokenizer, prompt=prompt, verbose=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings
LM Studio

How to use bfuzzy1/TinyGuide with Pi:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bfuzzy1/TinyGuide"

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "mlx-lm": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "bfuzzy1/TinyGuide"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use bfuzzy1/TinyGuide with Hermes Agent:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bfuzzy1/TinyGuide"

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 bfuzzy1/TinyGuide

Run Hermes

hermes

OpenClaw new

How to use bfuzzy1/TinyGuide with OpenClaw:

Start the MLX server

# Install MLX LM:
uv tool install mlx-lm
# Start a local OpenAI-compatible server:
mlx_lm.server --model "bfuzzy1/TinyGuide"

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 "bfuzzy1/TinyGuide" \
  --custom-provider-id mlx-lm \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

MLX LM

How to use bfuzzy1/TinyGuide with MLX LM:

Generate or start a chat session

# Install MLX LM
uv tool install mlx-lm
# Interactive chat REPL
mlx_lm.chat --model "bfuzzy1/TinyGuide"

Run an OpenAI-compatible server

# Install MLX LM
uv tool install mlx-lm
# Start the server
mlx_lm.server --model "bfuzzy1/TinyGuide"
# Calling the OpenAI-compatible server with curl
curl -X POST "http://localhost:8000/v1/chat/completions" \
   -H "Content-Type: application/json" \
   --data '{
     "model": "bfuzzy1/TinyGuide",
     "messages": [
       {"role": "user", "content": "Hello"}
     ]
   }'

TinyGuide / README.md

bfuzzy1

Update README.md

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	---
	library_name: mlx
	license: apache-2.0
	license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
	base_model: Qwen/Qwen3-0.6B
	pipeline_tag: text-generation
	tags:
	- mlx
	---

	# TinyGuide

	A tiny highly experimental 0.6B coding agent coach that runs on your laptop. TinyGuide sits in Claude
	Code's PreToolUse hook before every `Edit`, `Write`, or `Bash`, it reads the session state and decides whether to say something. Most of the time it stays quiet. When the agent is about to edit a file it never read, re-run a command that already failed, or finalize without testing, it drops one short sentence
	of guidance into the agent's context.

	Fine tuned from Qwen3-0.6B. Small enough to run locally on every tool call.

	This repo holds the fp16 fused weights (bf16, ~1.1 GB) and the Claude Code hook that wires them in.

	## Run it

	Serve with mlx_lm, then point the hook at it (`infer.py` POSTs to `http://127.0.0.1:8080`):

	```bash
	pip install mlx-lm
	mlx_lm.server --model <this-repo-or-local-path> --port 8080
	```

	Direct generation:

	```python
	from mlx_lm import load, generate
	m, tok = load("<this-repo-or-local-path>")
	print(generate(m, tok, prompt, max_tokens=24, temp=0.0))
	```

	## Wire into Claude Code

	Files in `claude-code/`:

	\| File \| Role \|
	\|------\|------\|
	\| `hook_pretooluse.py` \| the PreToolUse hook: rebuilds state from the transcript, asks the server, prints advisory context \|
	\| `infer.py` \| client to the local mlx_lm.server; strips `<think>`, validates the hint \|
	\| `format_prompt.py` \| shared train/inference prompt builder \|
	\| `build_from_raw.py` \| transcript parser + state machine (`iter_pairs`, `_advance`) \|
	\| `clean_output.py` \| `KNOWN_HINTS` + output validation \|
	\| `settings.snippet.json` \| the `PreToolUse` config to merge into `~/.claude/settings.json` \|

	Merge `settings.snippet.json` into `~/.claude/settings.json`, fixing the two absolute paths to your venv python and this folder. The hook fires on `Edit\|Write\|Bash`.

	### Runtime cooldown

	Enforced at runtime in the hook via `/tmp/tinyguide_cooldown.json`:

	- `MIN_CALLS_BETWEEN = 3` tool calls between hints
	- `MAX_PER_SESSION = 5` cap per session

	You can tune these in `hook_pretooluse.py` pacing is a policy.