Instructions to use ericflo/Llama-3.2-3B-COTv2.2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ericflo/Llama-3.2-3B-COTv2.2 with Transformers:

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

pipe = pipeline("text-generation", model="ericflo/Llama-3.2-3B-COTv2.2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-3B-COTv2.2")
model = AutoModelForCausalLM.from_pretrained("ericflo/Llama-3.2-3B-COTv2.2", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use ericflo/Llama-3.2-3B-COTv2.2 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 ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M

Use Docker

docker model run hf.co/ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use ericflo/Llama-3.2-3B-COTv2.2 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-COTv2.2"
# 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-COTv2.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M

SGLang

How to use ericflo/Llama-3.2-3B-COTv2.2 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-COTv2.2" \
    --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-COTv2.2",
		"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-COTv2.2" \
        --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-COTv2.2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ericflo/Llama-3.2-3B-COTv2.2 with Ollama:
```
ollama run hf.co/ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M
```
Unsloth Desktop

How to use ericflo/Llama-3.2-3B-COTv2.2 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ericflo/Llama-3.2-3B-COTv2.2: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": "ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ericflo/Llama-3.2-3B-COTv2.2 with Docker Model Runner:
```
docker model run hf.co/ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M
```

Lemonade

How to use ericflo/Llama-3.2-3B-COTv2.2 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ericflo/Llama-3.2-3B-COTv2.2:Q4_K_M

Run and chat with the model

lemonade run user.Llama-3.2-3B-COTv2.2-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ericflo/Llama-3.2-3B-COTv2.2 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ericflo/Llama-3.2-3B-COTv2.2: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-COTv2.2:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ericflo/Llama-3.2-3B-COTv2.2 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ericflo/Llama-3.2-3B-COTv2.2: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 "ericflo/Llama-3.2-3B-COTv2.2: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"

Llama-3.2-3B-COTv2.2 / README.md

ericflo

Update README.md

56c1122 verified almost 2 years ago

preview code

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---
license: apache-2.0
base_model:
- meta-llama/Llama-3.2-3B-Instruct
library_name: transformers
datasets:
- ericflo/Llama-3.2-3B-COT
---
# Thought-Ranked Llama 3.2 3B v2.2

## 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:

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

2. Specific thought count:
```
{"role": "system", "content": "You are a helpful assistant. Think 3 thoughts before responding."}
```

3. 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

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ericflo/Llama-3.2-3B-COT-v2.2")
tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-3B-COT-v2.2")
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

```bibtex
@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.

	---
	license: apache-2.0
	base_model:
	- meta-llama/Llama-3.2-3B-Instruct
	library_name: transformers
	datasets:
	- ericflo/Llama-3.2-3B-COT
	---
	# Thought-Ranked Llama 3.2 3B v2.2

	## 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:

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

	2. Specific thought count:
	```
	{"role": "system", "content": "You are a helpful assistant. Think 3 thoughts before responding."}
	```

	3. 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

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("ericflo/Llama-3.2-3B-COT-v2.2")
	tokenizer = AutoTokenizer.from_pretrained("ericflo/Llama-3.2-3B-COT-v2.2")

	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

	```bibtex
	@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.