Instructions to use FoolDev/Janus-35B-HERETIC with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FoolDev/Janus-35B-HERETIC with Transformers:

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

pipe = pipeline("image-text-to-text", model="FoolDev/Janus-35B-HERETIC")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("FoolDev/Janus-35B-HERETIC", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use FoolDev/Janus-35B-HERETIC 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 FoolDev/Janus-35B-HERETIC:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FoolDev/Janus-35B-HERETIC:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf FoolDev/Janus-35B-HERETIC:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf FoolDev/Janus-35B-HERETIC: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 FoolDev/Janus-35B-HERETIC:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf FoolDev/Janus-35B-HERETIC: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 FoolDev/Janus-35B-HERETIC:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf FoolDev/Janus-35B-HERETIC:Q4_K_M

Use Docker

docker model run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M

LM Studio
Jan

vLLM

How to use FoolDev/Janus-35B-HERETIC with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FoolDev/Janus-35B-HERETIC"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FoolDev/Janus-35B-HERETIC",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M

SGLang

How to use FoolDev/Janus-35B-HERETIC 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 "FoolDev/Janus-35B-HERETIC" \
    --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": "FoolDev/Janus-35B-HERETIC",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "FoolDev/Janus-35B-HERETIC" \
        --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": "FoolDev/Janus-35B-HERETIC",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use FoolDev/Janus-35B-HERETIC with Ollama:
```
ollama run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
```
Unsloth Desktop

How to use FoolDev/Janus-35B-HERETIC with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FoolDev/Janus-35B-HERETIC: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": "FoolDev/Janus-35B-HERETIC:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use FoolDev/Janus-35B-HERETIC with Docker Model Runner:
```
docker model run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M
```

Lemonade

How to use FoolDev/Janus-35B-HERETIC with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull FoolDev/Janus-35B-HERETIC:Q4_K_M

Run and chat with the model

lemonade run user.Janus-35B-HERETIC-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use FoolDev/Janus-35B-HERETIC with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FoolDev/Janus-35B-HERETIC: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 FoolDev/Janus-35B-HERETIC:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use FoolDev/Janus-35B-HERETIC with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf FoolDev/Janus-35B-HERETIC: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 "FoolDev/Janus-35B-HERETIC: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"

FoolDev Claude Fable 5 commited on 9 days ago

Commit

834f5fe

1 Parent(s): 98074bd

docs(readme): warn that /v1 can't override num_ctx (OOMs on small hosts)

Browse files

The OpenAI-compatible /v1/chat/completions examples load at the baked
num_ctx 1010000 (v1 has no override knob), which OOMs on <=~48 GB hosts —
the KV cache alone wants ~62 GB. Added a callout beside the examples: use
/api/chat with options.num_ctx, or /save a small-context local tag for
OpenAI clients.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

Files changed (2) hide show

CHANGELOG.md +7 -0
README.md +7 -0

CHANGELOG.md CHANGED Viewed

@@ -65,6 +65,13 @@ track the **tooling and documentation**, not the underlying base model.
   theoretical footprint at the default is recomputed to ~62 GB KV / ~100 GB total.
 ### Fixed
 - **`llama-cpp-python` example matches its CPU-only install.** The
   `examples/README.md` llama-cpp-python quickstart installed the CPU-only wheel
   but then ran with `--gpu-layers 99` — a silent no-op on that build, and a

   theoretical footprint at the default is recomputed to ~62 GB KV / ~100 GB total.
 ### Fixed
+- **OpenAI `/v1` examples now warn about the `num_ctx` OOM on small hosts.**
+  `/v1/chat/completions` has no `num_ctx` knob, so it loads at the baked
+  `num_ctx 1010000` and OOMs on ≤~48 GB hosts (the KV cache alone wants ~62 GB) —
+  but the examples showed `/v1` with no caveat. Added a callout beside the
+  OpenAI-compatible examples: use `/api/chat` with `"options": {"num_ctx": 4096}`,
+  or `/save` a small-context local tag for OpenAI clients (`/v1` can't pass the
+  override inline).
 - **`llama-cpp-python` example matches its CPU-only install.** The
   `examples/README.md` llama-cpp-python quickstart installed the CPU-only wheel
   but then ran with `--gpu-layers 99` — a silent no-op on that build, and a

README.md CHANGED Viewed

@@ -159,6 +159,13 @@ Once the model is loaded (via `ollama run janus`, `lms server`, or `llama-server
 > The examples use `model: "janus"`, the tag from the local build (path B). If you pulled via the TL;DR one-liner instead, use the full tag `hf.co/FoolDev/Janus-35B-HERETIC`, or run `ollama cp hf.co/FoolDev/Janus-35B-HERETIC janus` once to create the short tag.
 #### curl
 ```bash

 > The examples use `model: "janus"`, the tag from the local build (path B). If you pulled via the TL;DR one-liner instead, use the full tag `hf.co/FoolDev/Janus-35B-HERETIC`, or run `ollama cp hf.co/FoolDev/Janus-35B-HERETIC janus` once to create the short tag.
+> **On ≤~48 GB hosts, cap `num_ctx` first.** `/v1/chat/completions` (OpenAI-compat) has
+> no `num_ctx` knob, so it loads at the baked **1,010,000** default and OOMs — the KV
+> cache alone wants ~62 GB (see [Hardware requirements](#hardware-requirements)). Either
+> call `/api/chat` with `"options": {"num_ctx": 4096}`, or bake a small-context tag for
+> OpenAI clients: `ollama run hf.co/FoolDev/Janus-35B-HERETIC:Q4_K_M`, then
+> `/set parameter num_ctx 4096` and `/save janus`, and point clients at `janus`.
 #### curl
 ```bash