Instructions to use ericflo/Llama-3.2-3B-COTv2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ericflo/Llama-3.2-3B-COTv2 with Transformers:

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

pipe = pipeline("text-generation", model="ericflo/Llama-3.2-3B-COTv2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-3B-COTv2")
model = AutoModelForCausalLM.from_pretrained("ericflo/Llama-3.2-3B-COTv2")
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 ericflo/Llama-3.2-3B-COTv2 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ericflo/Llama-3.2-3B-COTv2",
	filename="Llama-3.2-3B-COTv2-BF16.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ericflo/Llama-3.2-3B-COTv2 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ericflo/Llama-3.2-3B-COTv2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ericflo/Llama-3.2-3B-COTv2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ericflo/Llama-3.2-3B-COTv2:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ericflo/Llama-3.2-3B-COTv2: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 ericflo/Llama-3.2-3B-COTv2:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ericflo/Llama-3.2-3B-COTv2: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 ericflo/Llama-3.2-3B-COTv2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ericflo/Llama-3.2-3B-COTv2:Q4_K_M

Use Docker

docker model run hf.co/ericflo/Llama-3.2-3B-COTv2:Q4_K_M

LM Studio
Jan

vLLM

How to use ericflo/Llama-3.2-3B-COTv2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ericflo/Llama-3.2-3B-COTv2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ericflo/Llama-3.2-3B-COTv2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ericflo/Llama-3.2-3B-COTv2:Q4_K_M

SGLang

How to use ericflo/Llama-3.2-3B-COTv2 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 "ericflo/Llama-3.2-3B-COTv2" \
    --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": "ericflo/Llama-3.2-3B-COTv2",
		"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 "ericflo/Llama-3.2-3B-COTv2" \
        --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": "ericflo/Llama-3.2-3B-COTv2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ericflo/Llama-3.2-3B-COTv2 with Ollama:
```
ollama run hf.co/ericflo/Llama-3.2-3B-COTv2:Q4_K_M
```

Unsloth Studio new

How to use ericflo/Llama-3.2-3B-COTv2 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 ericflo/Llama-3.2-3B-COTv2 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 ericflo/Llama-3.2-3B-COTv2 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ericflo/Llama-3.2-3B-COTv2 to start chatting

Pi new

How to use ericflo/Llama-3.2-3B-COTv2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ericflo/Llama-3.2-3B-COTv2: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": "ericflo/Llama-3.2-3B-COTv2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ericflo/Llama-3.2-3B-COTv2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ericflo/Llama-3.2-3B-COTv2: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 ericflo/Llama-3.2-3B-COTv2:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ericflo/Llama-3.2-3B-COTv2 with Docker Model Runner:
```
docker model run hf.co/ericflo/Llama-3.2-3B-COTv2:Q4_K_M
```

Lemonade

How to use ericflo/Llama-3.2-3B-COTv2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ericflo/Llama-3.2-3B-COTv2:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-3B-COTv2-Q4_K_M

List all available models

lemonade list

Thought-Ranked Llama 3.2 3B v2

There's a newer version (v2.2) of this model: https://huggingface.co/ericflo/Llama-3.2-3B-COTv2.2

What's New in v2?

The biggest improvement in v2 is how the model thinks through problems. Instead of just one level of thoughts, it can now explore up to 6 levels deep, building on its best ideas at each step. Think of it like having a conversation with yourself, where each new thought builds on your previous best insight.

How It Works

Let's look at an example. When asked "What would happen if the moon disappeared?", the model might think:

<thoughts>
<thought>First, I should consider the moon's main effects on Earth</thought>
<thought>The moon controls our tides, so ocean patterns would change dramatically</thought>
<thought>Without the moon's gravitational pull, Earth's rotation would become unstable</thought>
<thought>This would lead to extreme climate changes and disrupted ecosystems</thought>
<thought>The loss of moonlight would affect nocturnal animals and human culture</thought>
<thought>Combining all these effects, we'd see a cascade of environmental changes</thought>
</thoughts>

The disappearance of the moon would have far-reaching consequences for Earth...
[detailed answer follows]

System Messages

The model responds to different types of system prompts. Here are some examples:

Basic prompt:

{"role": "system", "content": "You are a helpful assistant. Think before responding."}

Specific thought count:

{"role": "system", "content": "You are a helpful assistant. Think 3 thoughts before responding."}

Standard helper:

{"role": "system", "content": "You are a helpful assistant."}

About 40% of training examples include system messages, and 75% of those specifically mention thinking. This helps the model learn when and how much to think through problems.

Technical Details

Base Model: Llama 3.2 3B
Training Data: 2,500 carefully selected examples, each with up to 6 levels of thought chains
Thought Selection: At each level, the model generates multiple possible thoughts and picks the best one using an external ranking system

What's It Good For?

This model excels at tasks that benefit from careful thinking:

✅ Breaking down complex problems ✅ Step-by-step math solutions ✅ Detailed analysis of situations ✅ Explaining complicated concepts ✅ Making well-reasoned decisions

Limitations

Can sometimes overthink simple problems
Limited by the capabilities of the base Llama 3.2 3B model
Not suitable for critical decisions without human oversight
May occasionally generate irrelevant thought chains

Example Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ericflo/Llama-3.2-3B-COT-v2")
tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-3B-COT-v2")

messages = [
    {"role": "system", "content": "You are a helpful assistant. Think 3 thoughts before responding."},
    {"role": "user", "content": "How would you teach a child to ride a bike?"}
]

input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt")
output = model.generate(input_ids, temperature=1.0)
response = tokenizer.decode(output[0])

Example output:

<thoughts>
<thought>Safety should be the first priority - helmet and protective gear</thought>
<thought>Starting with balance using training wheels can build confidence</thought>
<thought>Breaking the process into small, manageable steps will help avoid overwhelm</thought>
</thoughts>

Here's how I would teach a child to ride a bike...
[detailed answer follows]

Citation

@misc{thought-ranked-llama-v2,
  title={Thought-Ranked Llama 3.2 v2: Hierarchical Chain-of-Thought Generation},
  author={[Eric Florenzano]},
  year={2024},
  howpublished={\url{https://huggingface.co/ericflo/Llama-3.2-3B-COT-v2}}
}

Acknowledgments

This model builds on the Llama 3.2 3B base model from Meta. Special thanks to the open-source AI community for their contributions to chain-of-thought prompting techniques.

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Tensor type

F32

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