Instructions to use yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 with Transformers:

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

pipe = pipeline("text-generation", model="yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2")
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 AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2")
model = AutoModelForMultimodalLM.from_pretrained("yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2

SGLang

How to use yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 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 "yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2" \
    --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": "yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2",
		"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 "yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2" \
        --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": "yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2 with Docker Model Runner:
```
docker model run hf.co/yuxinlu1/gemma-4-12B-agentic-fable5-composer2.5-v2-3.5x-tau2
```

sold out

by deleted - opened 8 days ago

Discussion

deleted

8 days ago

yuxinlu1: "I'm now working with a top-tier AI lab to give back to the open-source community. 🤝 Many of you have already noticed the side effects in v1 and v2 — and honestly they come down to just two things: (1) not enough compute, and (2) one person with limited expertise behind the whole thing. This collaboration solves both of those completely. And the benchmarks you care about will absolutely be addressed — the things I simply couldn't fully pull off before because of time and compute limits. The people working on this with me are PhDs from top universities, with seriously strong papers and citation records. Just think about that for a second: the people who actually build large models are now contributing to the open-source community together with me — that is genuinely wild. 🤯 We're in active discussions right now, and the project is still in the R&D phase, so I can't share specifics yet — but the moment I have news, you'll be the first to know. 🚀"

pack it up boys, he got bought out

the "Mythos/Fable" name washing worked out well for him

this is why we are seeing alot of these mythos finetunes popping up everywhere on the trending page

yuxinlu1: "I can't share specifics yet"

that's just the start, soon he won't even share a single model

I guess you just need deceptive marketing and benchmaxxing a certain niche benchmark (in this case tau2, whatever that is) to be bought out by a "top-tier AI lab" and "PhDs from top universities with SERIOUSLY STRONG papers and citation records"

this also goes to show the level of intelligence of the people who label themselves top-tier AI labs and PhDs from top universities, with seriously strong papers and citation records.

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