Instructions to use interpolators/FableOpus-9B-Delta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use interpolators/FableOpus-9B-Delta with Transformers:

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

pipe = pipeline("text-generation", model="interpolators/FableOpus-9B-Delta")
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("interpolators/FableOpus-9B-Delta")
model = AutoModelForMultimodalLM.from_pretrained("interpolators/FableOpus-9B-Delta")
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 interpolators/FableOpus-9B-Delta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "interpolators/FableOpus-9B-Delta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "interpolators/FableOpus-9B-Delta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/interpolators/FableOpus-9B-Delta

SGLang

How to use interpolators/FableOpus-9B-Delta 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 "interpolators/FableOpus-9B-Delta" \
    --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": "interpolators/FableOpus-9B-Delta",
		"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 "interpolators/FableOpus-9B-Delta" \
        --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": "interpolators/FableOpus-9B-Delta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use interpolators/FableOpus-9B-Delta with Docker Model Runner:
```
docker model run hf.co/interpolators/FableOpus-9B-Delta
```

suayptalha commited on 13 days ago

Commit

d638efc

verified ·

1 Parent(s): d65c969

Shorten model name and update README

Browse files

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -19,7 +19,7 @@ base_model:
 - Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
 ---
-# Qwable Opus 9B Reasoning Delta bf16
 Opus-forward delta merge anchored to Qwen3.5-9B, keeping Fable tool-use behavior while biasing toward the high-download Opus reasoning family.
@@ -53,7 +53,7 @@ General chat, code assistance, tool-use style prompting, and reasoning-heavy exp
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
-model_id = "interpolators/Qwable-Opus-9B-Reasoning-Delta-bf16"
 tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
 messages = [{"role": "user", "content": "Write a concise plan for building a small agentic coding benchmark."}]

 - Jackrong/Qwen3.5-9B-Claude-4.6-Opus-Reasoning-Distilled-v2
 ---
+# FableOpus 9B Delta bf16
 Opus-forward delta merge anchored to Qwen3.5-9B, keeping Fable tool-use behavior while biasing toward the high-download Opus reasoning family.
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
+model_id = "interpolators/FableOpus-9B-Delta-bf16"
 tok = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto", trust_remote_code=True)
 messages = [{"role": "user", "content": "Write a concise plan for building a small agentic coding benchmark."}]