Instructions to use TaimoorSiddiqui/Hopcoder-Mini-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TaimoorSiddiqui/Hopcoder-Mini-9B with Transformers:

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

pipe = pipeline("text-generation", model="TaimoorSiddiqui/Hopcoder-Mini-9B")
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("TaimoorSiddiqui/Hopcoder-Mini-9B")
model = AutoModelForMultimodalLM.from_pretrained("TaimoorSiddiqui/Hopcoder-Mini-9B")
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 TaimoorSiddiqui/Hopcoder-Mini-9B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TaimoorSiddiqui/Hopcoder-Mini-9B

SGLang

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

Docker Model Runner
How to use TaimoorSiddiqui/Hopcoder-Mini-9B with Docker Model Runner:
```
docker model run hf.co/TaimoorSiddiqui/Hopcoder-Mini-9B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Hopcoder-Mini-9B

Hopcoder-Mini-9B is a compact 9B-parameter reasoning model with a 1,048,576-token context window (YaRN rope-scaling enabled by default), native function calling, and strong chain-of-thought performance.

Highlights

1M-token context out of the box via YaRN.
Native Qwen3.5-style function calling — no wrapper needed.
Self-corrects with tools — emits source-cited, factually grounded answers when given a Python executor and web search.
Built on a Qwen3.5-9B base (via empero-ai/Qwythos-9B-Claude-Mythos-5-1M), full-parameter fine-tuned on high-quality reasoning traces.

Architecture

Field	Value
Architecture	Qwen3_5ForConditionalGeneration
Model type	qwen3_5 (text + vision)
Parameters	~9B
Hidden size	4096
Layers	32 (hybrid linear / full attention)
Attention heads	16
KV heads	4
Vocab size	248,320
Max context	1,048,576 tokens
Precision	bfloat16

Requirements

transformers >= 5.12.1 (required for qwen3_5 model type)
torch >= 2.1
trust_remote_code=True when loading

Usage

Text-only input

import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model = AutoModelForImageTextToText.from_pretrained(
    "TaimoorSiddiqui/Hopcoder-Mini-9B",
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(
    "TaimoorSiddiqui/Hopcoder-Mini-9B",
    trust_remote_code=True,
)

messages = [
    {"role": "user", "content": "What is 2+2?"},
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(out[0], skip_special_tokens=True))

Vision input

from transformers import AutoModelForImageTextToText, AutoProcessor
from PIL import Image

model = AutoModelForImageTextToText.from_pretrained(
    "TaimoorSiddiqui/Hopcoder-Mini-9B",
    dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)
processor = AutoProcessor.from_pretrained(
    "TaimoorSiddiqui/Hopcoder-Mini-9B",
    trust_remote_code=True,
)

image = Image.open("example.jpg")
messages = [
    {"role": "user", "content": [
        {"type": "image", "image": image},
        {"type": "text", "text": "Describe this image."},
    ]},
]
text = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(text=text, images=image, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(out[0], skip_special_tokens=True))