Instructions to use manvadariya1/Zynthos-1.2B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use manvadariya1/Zynthos-1.2B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="manvadariya1/Zynthos-1.2B-Instruct-GGUF",
	filename="Zynthos-1.2B-Instruct-F16.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 manvadariya1/Zynthos-1.2B-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf manvadariya1/Zynthos-1.2B-Instruct-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 manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf manvadariya1/Zynthos-1.2B-Instruct-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 manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf manvadariya1/Zynthos-1.2B-Instruct-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 manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use manvadariya1/Zynthos-1.2B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "manvadariya1/Zynthos-1.2B-Instruct-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": "manvadariya1/Zynthos-1.2B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M

Ollama
How to use manvadariya1/Zynthos-1.2B-Instruct-GGUF with Ollama:
```
ollama run hf.co/manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use manvadariya1/Zynthos-1.2B-Instruct-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 manvadariya1/Zynthos-1.2B-Instruct-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 manvadariya1/Zynthos-1.2B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for manvadariya1/Zynthos-1.2B-Instruct-GGUF to start chatting

How to use manvadariya1/Zynthos-1.2B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf manvadariya1/Zynthos-1.2B-Instruct-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": "manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use manvadariya1/Zynthos-1.2B-Instruct-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 manvadariya1/Zynthos-1.2B-Instruct-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 manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use manvadariya1/Zynthos-1.2B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use manvadariya1/Zynthos-1.2B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull manvadariya1/Zynthos-1.2B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Zynthos-1.2B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

🌌 Zynthos-1.2B-Instruct: The Edge AI Revolution

Zynthos-1.2B-Instruct represents a monumental paradigm shift in local, on-device intelligence. Moving entirely beyond the scaling limits and massive computational overhead of traditional Transformer models, Zynthos is a high-fidelity deployment lineage built upon Liquid AI’s revolutionary non-transformer sequential architecture (LFM2.5-1.2B-Instruct).

By redefining token processing logic from the ground up, Zynthos delivers unprecedented throughput, sub-millisecond execution loops, and infinitely scalable context efficiency—all within a microscopic hardware footprint.

⚡ The Architectural Shift: Why Zynthos Changes Everything

Conventional small language models choke on memory bottlenecks and computational drain during long agent loops. Zynthos-1.2B-Instruct shatters these constraints, establishing a brand new class of localized ambient intelligence:

Sub-50ms Intelligent Routing: Deployed instantly as a local "fast-lane" intent classifier to orchestrate multi-agent tasks before routing heavier workloads to deep reasoning engines.
Deterministic Structured Extraction: Completely strips away conversational fluff to enforce flawless, schema-compliant JSON outputs and lightning-fast tool calls directly at the edge.
Flawless Infinite Scaling: Leverages underlying non-transformer recurrent dynamics to process complex data arrays with virtually static memory allocations, saving critical hardware battery life.

📊 Quantization & Performance Matrix

⭐ Execution Recommendation

For professional deployments, local workflow automation, and multi-agent system pipelines, Zynthos-1.2B-Instruct-F16.gguf is the highly recommended variant. It preserves 100% of the raw, uncompressed model tensors, guaranteeing maximum semantic reasoning, perfect tool-calling accuracy, and zero quantization loss.

File Artifact	Precision Bit-Weight	File Size	Memory Footprint	Deployment Classification
`Zynthos-1.2B-Instruct-F16.gguf`	Full FP16 Master	~2.4 GB	8 GB RAM	🏆 Recommended Tier: Maximum Precision & Uncompromised Routing
`Zynthos-1.2B-Instruct-Q8_0.gguf`	8-bit Standard	~1.2 GB	4 GB RAM	Balanced Tier: Premium RAG parsing & local document scanning
`Zynthos-1.2B-Instruct-Q4_K_M.gguf`	4-bit Medium	~750 MB	2 GB RAM	Ultra-Fast Tier: Extreme edge execution & restricted mobile hardware

🛠️ High-Speed Integration Blueprint

1. Instant Desktop Setup (LM Studio / Ollama)

Navigate to the Files and versions tab and download the recommended Zynthos-1.2B-Instruct-F16.gguf file.
Drop the file directory path straight into your local workspace.
Select the model within your UI, maximize your GPU Offload toggles, and experience localized generation speeds that feel instantaneous.

2. Enterprise Workflow Orchestration (`llama-cpp-python`)

Build local agent loops, background intent filters, or rapid JSON parsers with this streamlined script:

from llama_cpp import Llama

# Instantiate the recommended uncompressed master file for flawless execution
llm = Llama(
    model_path="./Zynthos-1.2B-Instruct-F16.gguf",
    n_ctx=4096,
    n_gpu_layers=-1 # Completely offload all layer calculations to your hardware GPU
)

# Optimized syntax structure for Instruct execution
prompt = "<|im_start|>user\nAnalyze this payload and return only the target intent key: [JSON], [SQL], or [TEXT]. Payload: 'SELECT * FROM infrastructure_metrics WHERE cpu > 90;'<|im_end|>\n<|im_start|>assistant\n"

output = llm(prompt, max_tokens=16, stop=["<|im_end|>"])
print(f"⚡ Routed Intent: {output['choices'][0]['text'].strip()}")

Downloads last month: 168

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for manvadariya1/Zynthos-1.2B-Instruct-GGUF

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(59)

this model