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TaimoorSiddiqui/
Hopcoder-Mini-9B

Name: Hopcoder-Mini-9B
Availability: InStock

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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

python

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

python

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))

Sampling: temperature=0.6, top_p=0.95, top_k=20 (Qwen3.5 defaults).

License

Apache 2.0.