Instructions to use osmapi/osmQwopus3.6-27B-Fable-Agentic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osmapi/osmQwopus3.6-27B-Fable-Agentic with Transformers:

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

pipe = pipeline("text-generation", model="osmapi/osmQwopus3.6-27B-Fable-Agentic")
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("osmapi/osmQwopus3.6-27B-Fable-Agentic")
model = AutoModelForMultimodalLM.from_pretrained("osmapi/osmQwopus3.6-27B-Fable-Agentic")
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 osmapi/osmQwopus3.6-27B-Fable-Agentic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osmapi/osmQwopus3.6-27B-Fable-Agentic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osmapi/osmQwopus3.6-27B-Fable-Agentic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/osmapi/osmQwopus3.6-27B-Fable-Agentic

SGLang

How to use osmapi/osmQwopus3.6-27B-Fable-Agentic 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 "osmapi/osmQwopus3.6-27B-Fable-Agentic" \
    --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": "osmapi/osmQwopus3.6-27B-Fable-Agentic",
		"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 "osmapi/osmQwopus3.6-27B-Fable-Agentic" \
        --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": "osmapi/osmQwopus3.6-27B-Fable-Agentic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use osmapi/osmQwopus3.6-27B-Fable-Agentic with Docker Model Runner:
```
docker model run hf.co/osmapi/osmQwopus3.6-27B-Fable-Agentic
```

osmQwopus3.6-27B-Fable-Agentic

Agentic - Reasoning - Tool-Calling - Top-Tier Knowledge

Beats its base model on knowledge - and acts like an agent.

osmQwopus3.6-27B-Fable-Agentic is a powerful 27B agentic model that reasons step-by-step and operates tools across multi-turn sessions, while delivering top-tier general knowledge. Fine-tuned from Jackrong/Qwopus3.6-27B-v2.

Benchmarks

Evaluated head-to-head against the base model on identical questions, with an 8192-token budget so step-by-step reasoning is never truncated.

Benchmark	Base	This model
MMLU-Pro (350 Q)	86.86%	88.29%
GSM8K (300 Q)	98.00%	97.33%
GPQA-Diamond (198 Q)	57.58%	57.07%

Knowledge improved, hard reasoning preserved: +1.43 over base on MMLU-Pro, matching it on GSM8K math and on GPQA-Diamond (graduate-level science, ~frontier-class for a 27B) - with agentic tool-calling added on top.

MMLU-Pro by category

Category	Base	This model
Biology	96.0%	94.0%
Business	78.0%	88.0%
Chemistry	84.0%	80.0%
Computer Science	84.0%	84.0%
Health	82.0%	86.0%
Math	94.0%	94.0%
Physics	90.0%	92.0%
Overall	86.86%	88.29%

Highlights

Top-tier knowledge - 88.29% MMLU-Pro, above the base model
Strong math - 97.33% GSM8K
Agentic tool-calling across multi-turn sessions
Step-by-step reasoning built in

Capabilities

Agentic, multi-turn tool-calling
Chain-of-thought reasoning
Top-tier general knowledge and math
Multimodal (vision + text) architecture

Usage (vLLM)

vllm serve osmapi/osmQwopus3.6-27B-Fable-Agentic --trust-remote-code --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder --max-model-len 16384

Tip: this model reasons before answering - give it a generous max_tokens so it can finish its chain of thought.

License

Apache 2.0

Downloads last month: 19

Safetensors

Model size

27B params

Tensor type

BF16

Model tree for osmapi/osmQwopus3.6-27B-Fable-Agentic

Base model

Jackrong/Qwopus3.6-27B-v2

Finetuned

(7)

this model

Finetunes

1 model