Instructions to use tinyopsec/llama3.2-1b-CoT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use tinyopsec/llama3.2-1b-CoT-GGUF 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 tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/llama3.2-1b-CoT-GGUF: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 tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tinyopsec/llama3.2-1b-CoT-GGUF: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 tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tinyopsec/llama3.2-1b-CoT-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/llama3.2-1b-CoT-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tinyopsec/llama3.2-1b-CoT-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M

Ollama
How to use tinyopsec/llama3.2-1b-CoT-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M
```
Unsloth Desktop

How to use tinyopsec/llama3.2-1b-CoT-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/llama3.2-1b-CoT-GGUF: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": "tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tinyopsec/llama3.2-1b-CoT-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/llama3.2-1b-CoT-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.llama3.2-1b-CoT-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/llama3.2-1b-CoT-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/llama3.2-1b-CoT-GGUF: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 tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/llama3.2-1b-CoT-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/llama3.2-1b-CoT-GGUF: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 "tinyopsec/llama3.2-1b-CoT-GGUF: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"

llama3.2-1b-CoT GGUF

GGUF quantizations of harshwardhanjadhav/llama3.2-1b-CoT.

Fine-tuned version of unsloth/Llama-3.2-1B-Instruct on 10,000 samples from the open-thoughts/OpenThoughts-114k dataset.
Trained to produce structured Chain-of-Thought reasoning traces using <|begin_of_thought|> and <|begin_of_solution|> blocks before delivering final answers.
Specializes in multi-step mathematical and analytical problem solving.

Quantization Table

File	Bits	Size	Use Case
`model_f16.gguf`	16	~2.5 GB	Maximum quality, reference
`model_q8_0.gguf`	8	~1.3 GB	Best quality / size tradeoff
`model_q6_k.gguf`	6	~1.0 GB	High quality
`model_q5_k_m.gguf`	5	~0.9 GB	Recommended
`model_q5_k_s.gguf`	5	~0.85 GB	Slightly smaller Q5
`model_q4_k_m.gguf`	4	~0.75 GB	Good balance
`model_q4_k_s.gguf`	4	~0.70 GB	Smaller Q4
`model_q3_k_l.gguf`	3	~0.60 GB	Low RAM, large variant
`model_q3_k_m.gguf`	3	~0.57 GB	Low RAM
`model_q3_k_s.gguf`	3	~0.53 GB	Minimum RAM Q3
`model_q2_k.gguf`	2	~0.45 GB	Extreme compression

VRAM Requirements

Quant	Min VRAM
F16	4 GB
Q8_0	2 GB
Q4_K_M	1.5 GB
Q2_K	1 GB

System Prompt (required for CoT)

Your role as an assistant involves thoroughly exploring questions through a systematic long thinking process before providing the final precise and accurate solutions.
This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process.
Please structure your response into two main sections: Thought and Solution.
In the Thought section, detail your reasoning process using the specified format: <|begin_of_thought|> {thought with steps separated with '\n\n'} <|end_of_thought|>
In the Solution section, present the final solution formatted as: <|begin_of_solution|> {final formatted, precise, and clear solution} <|end_of_solution|>

Recommended Inference Parameters

Parameter	Value	Reason
`temperature`	0.1	Stable CoT traces
`repetition_penalty`	1.15	Prevents infinite reasoning loops
`top_p`	0.9	Filters low-probability tokens
`max_new_tokens`	4096	Accommodates long reasoning chains

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf \
  --temp 0.1 \
  --repeat-penalty 1.15 \
  --top-p 0.9 \
  -n 4096 \
  -p "A store offers a 20% discount on a $150 coat, then takes 10% more off. What is the final price?"

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=8192)
output = llm(
    "Solve step by step: 2x + 5 = 13",
    max_tokens=4096,
    temperature=0.1,
    repeat_penalty=1.15,
    top_p=0.9,
)
print(output["choices"][0]["text"])

LM Studio

Search tinyopsec/llama3.2-1b-CoT-GGUF in the model browser.

Ollama

ollama run hf.co/tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M

Original Model

harshwardhanjadhav/llama3.2-1b-CoT

Downloads last month: 1,485

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for tinyopsec/llama3.2-1b-CoT-GGUF

Base model

meta-llama/Llama-3.2-1B-Instruct

Finetuned

unsloth/Llama-3.2-1B-Instruct

Finetuned

harshwardhanjadhav/llama3.2-1b-CoT

Quantized

(1)

this model