Instructions to use Flexan/HumanPet-X2.2-1.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Flexan/HumanPet-X2.2-1.7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Flexan/HumanPet-X2.2-1.7B-GGUF",
	filename="HumanPet-X2.2-1.7B.Q2_K.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 Flexan/HumanPet-X2.2-1.7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Flexan/HumanPet-X2.2-1.7B-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 Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Flexan/HumanPet-X2.2-1.7B-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 Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Flexan/HumanPet-X2.2-1.7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Flexan/HumanPet-X2.2-1.7B-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": "Flexan/HumanPet-X2.2-1.7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M

Ollama
How to use Flexan/HumanPet-X2.2-1.7B-GGUF with Ollama:
```
ollama run hf.co/Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M
```

Unsloth Studio

How to use Flexan/HumanPet-X2.2-1.7B-GGUF 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 Flexan/HumanPet-X2.2-1.7B-GGUF 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 Flexan/HumanPet-X2.2-1.7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Flexan/HumanPet-X2.2-1.7B-GGUF to start chatting

How to use Flexan/HumanPet-X2.2-1.7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Flexan/HumanPet-X2.2-1.7B-GGUF: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": "Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Flexan/HumanPet-X2.2-1.7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Flexan/HumanPet-X2.2-1.7B-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 Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Flexan/HumanPet-X2.2-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M
```

Lemonade

How to use Flexan/HumanPet-X2.2-1.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Flexan/HumanPet-X2.2-1.7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.HumanPet-X2.2-1.7B-GGUF-Q4_K_M

List all available models

lemonade list

GGUF Files for HumanPet-X2.2-1.7B

These are the GGUF files for Flexan/HumanPet-X2.2-1.7B.

Note: this model has only been quantized to Q2_K, Q4_K_M, and Q8_0. Other quantizations may become available later.

Downloads

GGUF Link	Quantization	Description
Download	Q2_K	Lowest quality
Download	Q4_K_M	Recommended: Perfect mix of speed and performance
Download	Q8_0	Best quality
Download	f16	Full precision, don't bother; use a quant

HumanPet X2.2 1.7B

Description

HumanPet X2.2 1.7B is an instruct LLM consisting of 1.7B parameters trained to talk in a human conversational manner. It does not support reasoning nor tool-calling (although the base model does).
The model was LoRA fine-tuned with Qwen/Qwen3-1.7B as base model.

The HumanPet series is part of an experiment. Do not expect consistent releases.

Explanation of the experiment
Progress on the experiment
Findings for HumanPet X2.2 1.7B

Note: the model files are not released yet. Read section 'Stages' in the experiment explanation.

Note: this model is a re-train of the previous model. Read why the model was re-trained here: Findings for HumanPet X2.1 1.7B

Chat Format

HumanPet X2.2 1.7B uses the ChatML format, e.g.:

<|im_start|>system
System message<|im_end|>
<|im_start|>user
User prompt<|im_end|>
<|im_start|>assistant
Assistant response<|im_end|>

Usage

This model is not trained on system prompts. Therefore, it is recommended to not send any system messages. This includes tools (which this model also was not trained on).

The assistant response has the following format:

<|im_start|>assistant
<think>

</think>

What happened?

<emote>:^</emote><|im_end|>

Note that the <think>...</think> tags are always empty, as this model was not trained on reasoning data. The <emote>...</emote> tags contain a text emoji that the model thinks fits most with the response. The emojis it may output are:

0^0
u~u
u.u
:^
:3
x3
0w0
u-u
:P
>.<
>-<
owo
U.U
:]
^-^
>:3
:0
:O
0~0
o^o
O^O
@~@
@.@
o~o
T^T
>~<
>.0
o.o
>^<
0.0
0-0
-w-
-.-
T-T
T.T
T~T
:<
u^u

Note that the emojis are escaped because of the XML tags (e.g. ">.<" is outputted by the model as ">.<"). If the model does not adhere to the list of emojis, please let us know in the community tab.

Datasets

ConvLab/dailydialog 4.3k chats
Only the conversations from the "Relationship" domain have been used.
- Private NLP processing
  NLP processing has been applied to the text to modify the dataset to be sillier.

Downloads last month: 134

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

2-bit

4-bit

8-bit

16-bit

Model tree for Flexan/HumanPet-X2.2-1.7B-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

Flexan/HumanPet-X2.2-1.7B

Quantized

(3)

this model

Flexan
/

HumanPet-X2.2-1.7B-GGUF