Instructions to use bfuzzy1/TinyGuide with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
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
- Pi
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"} ] }'
File size: 1,798 Bytes
78d2164 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 | """Strict filter on model output. Returns hint str or None."""
KNOWN_HINTS = [
"Read the traceback file before editing.",
"Read the file before editing it.",
"Run the focused failing test now.",
"Do not patch the same hypothesis again.",
"Search the exact symbol from the error.",
"Change strategy; this command already failed.",
"Check the project's package manager first.",
"Use the latest error, not the original one.",
"Verify the change before finalizing.",
]
BANNED = ["<think>", "</think>", "because", "step 1", "first,", "second,", "plan:", "i think"]
import re
_THINK = re.compile(r"<think>.*?</think>", re.S)
def clean_hint(text):
text = _THINK.sub("", text).strip() # drop empty think block
text = text.splitlines()[0].strip() if text.strip() else ""
if text == "NO_HINT":
return None
if any(x in text.lower() for x in BANNED):
return None
if len(text.split()) > 12:
return None
marks = text.count(".") + text.count("!") + text.count("?")
if marks > 1:
return None
if marks == 0:
text += "."
if text not in KNOWN_HINTS:
return None
return text
def demo():
assert clean_hint("NO_HINT") is None
assert clean_hint("Read the file before editing it.") == "Read the file before editing it."
assert clean_hint("Read the file before editing it") == "Read the file before editing it." # adds period
assert clean_hint("First, I think we should because reasons.") is None # banned + not known
assert clean_hint("blah blah not a known hint at all here.") is None
assert clean_hint("This is a very long sentence with way more than twelve words in it indeed yes.") is None
print("clean_output ok")
if __name__ == "__main__":
demo()
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