Instructions to use notshekhar/markdown-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use notshekhar/markdown-1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="notshekhar/markdown-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("notshekhar/markdown-1")
model = AutoModelForCausalLM.from_pretrained("notshekhar/markdown-1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use notshekhar/markdown-1 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="notshekhar/markdown-1",
	filename="markdown-1-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use notshekhar/markdown-1 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 notshekhar/markdown-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf notshekhar/markdown-1:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf notshekhar/markdown-1:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf notshekhar/markdown-1: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 notshekhar/markdown-1:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf notshekhar/markdown-1: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 notshekhar/markdown-1:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf notshekhar/markdown-1:Q4_K_M

Use Docker

docker model run hf.co/notshekhar/markdown-1:Q4_K_M

LM Studio
Jan

vLLM

How to use notshekhar/markdown-1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "notshekhar/markdown-1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "notshekhar/markdown-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/notshekhar/markdown-1:Q4_K_M

SGLang

How to use notshekhar/markdown-1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "notshekhar/markdown-1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "notshekhar/markdown-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "notshekhar/markdown-1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "notshekhar/markdown-1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use notshekhar/markdown-1 with Ollama:
```
ollama run hf.co/notshekhar/markdown-1:Q4_K_M
```

Unsloth Studio

How to use notshekhar/markdown-1 with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for notshekhar/markdown-1 to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for notshekhar/markdown-1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for notshekhar/markdown-1 to start chatting

How to use notshekhar/markdown-1 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf notshekhar/markdown-1:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "notshekhar/markdown-1:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use notshekhar/markdown-1 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf notshekhar/markdown-1: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 notshekhar/markdown-1:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use notshekhar/markdown-1 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf notshekhar/markdown-1: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 "notshekhar/markdown-1: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"

Docker Model Runner
How to use notshekhar/markdown-1 with Docker Model Runner:
```
docker model run hf.co/notshekhar/markdown-1:Q4_K_M
```

Lemonade

How to use notshekhar/markdown-1 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull notshekhar/markdown-1:Q4_K_M

Run and chat with the model

lemonade run user.markdown-1-Q4_K_M

List all available models

lemonade list

markdown-1 / README.md

notshekhar

Add model card with llama.cpp/Ollama usage

321e137 verified 12 days ago

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metadata

base_model: WeiboAI/VibeThinker-3B
library_name: transformers
tags:
  - gguf
  - llama.cpp
  - ollama
  - tool-calling
  - qwen2
  - vibethinker
pipeline_tag: text-generation

markdown-1

VibeThinker-3B fine-tuned (LoRA, merged) for tool calling + long agent traces.

This repo contains the merged fp16 weights plus ready-to-run GGUF quants for llama.cpp / Ollama / LM Studio.

File	Size	Use
`markdown-1-Q4_K_M.gguf`	~1.9 GB	smaller / faster, great default
`markdown-1-Q8_0.gguf`	~3.3 GB	higher fidelity
`model-*.safetensors`	~6.2 GB	merged fp16 (vLLM / transformers)

LoRA adapter only: notshekhar/vibethinker-finetuned-tool.

Run with llama.cpp

llama-cli -hf notshekhar/markdown-1:Q4_K_M -p "Hello"
# or local:
llama-cli -m markdown-1-Q4_K_M.gguf -p "Hello"

Run with Ollama

# Modelfile
printf 'FROM ./markdown-1-Q4_K_M.gguf\n' > Modelfile
ollama create markdown-1 -f Modelfile
ollama run markdown-1

Base reasoning model uses <think> traces and ChatML (<|im_start|>) with tool-calling via <tool_call> / <tool_response> blocks (see chat_template.jinja).