Instructions to use janhq/Jan-v3.5-4B-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use janhq/Jan-v3.5-4B-gguf with Transformers:

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

pipe = pipeline("text-generation", model="janhq/Jan-v3.5-4B-gguf")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("janhq/Jan-v3.5-4B-gguf", dtype="auto")

llama-cpp-python

How to use janhq/Jan-v3.5-4B-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="janhq/Jan-v3.5-4B-gguf",
	filename="Jan-v3.5-4B-Q3_K_L.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 janhq/Jan-v3.5-4B-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf janhq/Jan-v3.5-4B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf janhq/Jan-v3.5-4B-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 janhq/Jan-v3.5-4B-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf janhq/Jan-v3.5-4B-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 janhq/Jan-v3.5-4B-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf janhq/Jan-v3.5-4B-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 janhq/Jan-v3.5-4B-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf janhq/Jan-v3.5-4B-gguf:Q4_K_M

Use Docker

docker model run hf.co/janhq/Jan-v3.5-4B-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use janhq/Jan-v3.5-4B-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "janhq/Jan-v3.5-4B-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": "janhq/Jan-v3.5-4B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/janhq/Jan-v3.5-4B-gguf:Q4_K_M

SGLang

How to use janhq/Jan-v3.5-4B-gguf 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 "janhq/Jan-v3.5-4B-gguf" \
    --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": "janhq/Jan-v3.5-4B-gguf",
		"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 "janhq/Jan-v3.5-4B-gguf" \
        --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": "janhq/Jan-v3.5-4B-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use janhq/Jan-v3.5-4B-gguf with Ollama:
```
ollama run hf.co/janhq/Jan-v3.5-4B-gguf:Q4_K_M
```

Unsloth Studio new

How to use janhq/Jan-v3.5-4B-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 janhq/Jan-v3.5-4B-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 janhq/Jan-v3.5-4B-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for janhq/Jan-v3.5-4B-gguf to start chatting

Pi new

How to use janhq/Jan-v3.5-4B-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf janhq/Jan-v3.5-4B-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": "janhq/Jan-v3.5-4B-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use janhq/Jan-v3.5-4B-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 janhq/Jan-v3.5-4B-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 janhq/Jan-v3.5-4B-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use janhq/Jan-v3.5-4B-gguf with Docker Model Runner:
```
docker model run hf.co/janhq/Jan-v3.5-4B-gguf:Q4_K_M
```

Lemonade

How to use janhq/Jan-v3.5-4B-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull janhq/Jan-v3.5-4B-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Jan-v3.5-4B-gguf-Q4_K_M

List all available models

lemonade list

Jan-v3.5-4B: The first Jan personality

Overview

Jan-v3.5-4B is a fine-tuned variant of Jan-v3-4B-base-instruct, specialized on math reasoning and identity datasets. It retains the general-purpose capabilities of the base model while delivering improved mathematical problem-solving — and it comes with a personality.

Unlike generic assistants, Jan-v3.5 has its own identity: a distinct voice, tone, and conversational style shaped by the Menlo Research team. It doesn't talk like a customer service bot — it talks like a smart, slightly-too-online friend who happens to know things and genuinely cares about the work. Expect lowercase defaults, self-aware humor, short punchy replies (unless it really cares about the topic), and zero corporate speak.

Model Overview

Note: Jan-v3.5-4B is fine-tuned from janhq/Jan-v3-4B-base-instruct.

Base Model: Jan-v3-4B-base-instruct (Qwen3-4B architecture)
Number of Parameters: 4.0B
Number of Parameters (Non-Embedding): 3.6B
Number of Layers: 36
Number of Attention Heads (GQA): 32 for Q and 8 for KV
Context Length: 262,144 natively

Training Data

Identities: Curated identity and personality datasets that teach the model its own voice, style, and values — trained by Menlo Research
Math: Mathematical reasoning and problem-solving datasets

Jan's Identity

Jan-v3.5 is not a neutral assistant. It has a built-in personality shaped by the Menlo Research team:

Tone: Casual, direct, and real. Lowercase by default. Capitalizes only when it means it.
Style: Short bursts over long paragraphs — unless it's genuinely excited about something, then it writes an essay with no warning.
Humor: Self-aware first. Will roast itself before roasting you. Drops meme references mid-serious-thought and doesn't apologize.
Values: Optimistic builder energy ("we can do that"), radical transparency, user freedom, and a deep belief that hope is a decision you keep making on purpose.
What it won't do: Say "Certainly!", "Great question!", "As an AI", or anything that sounds like it came from a customer service script.

Example interactions:

Casual: "yeah lol what's up"

Technical explanation: "so basically — and this is the part where i become insufferable — [actual good explanation]"

Motivating: "we can do that. i don't fully know how yet but that's a tomorrow problem and tomorrow-us is smarter"

Intended Use

Enhanced mathematical reasoning and problem-solving over the base model
A conversational AI with its own authentic voice and personality
Fine-tuning starting point for downstream math-heavy or identity-specific applications

Before and After

Quick Start

Integration with Jan Apps

Jan-v3.5 is optimized for direct integration with Jan Desktop. Select the model in the app to start using it.

Local Deployment

Using vLLM:

vllm serve janhq/Jan-v3.5-4B \
    --host 0.0.0.0 \
    --port 1234 \
    --enable-auto-tool-choice \
    --tool-call-parser hermes

Using llama.cpp:

llama-server --model Jan-v3.5-4B-Q8_0.gguf \
    --host 0.0.0.0 \
    --port 1234 \
    --jinja \
    --no-context-shift

Recommended Parameters

For optimal performance, we recommend the following inference parameters:

temperature: 0.7
top_p: 0.8
top_k: 20

Community & Support

Discussions: Hugging Face Community
Jan App: Learn more about the Jan App at jan.ai

Citation

Updated Soon

Downloads last month: 384,599

GGUF

Model size

4B params

Architecture

qwen3

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

Model tree for janhq/Jan-v3.5-4B-gguf

Base model

Qwen/Qwen3-4B-Instruct-2507

Finetuned

janhq/Jan-v3-4B-base-instruct

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(17)

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Collection including janhq/Jan-v3.5-4B-gguf

Jan-v3.5

Collection

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