Instructions to use osk-arr00/BigBang-Aquila-35B-Merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use osk-arr00/BigBang-Aquila-35B-Merged with Transformers:

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

pipe = pipeline("image-text-to-text", model="osk-arr00/BigBang-Aquila-35B-Merged")
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("osk-arr00/BigBang-Aquila-35B-Merged")
model = AutoModelForMultimodalLM.from_pretrained("osk-arr00/BigBang-Aquila-35B-Merged", 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

llama.cpp

How to use osk-arr00/BigBang-Aquila-35B-Merged with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf osk-arr00/BigBang-Aquila-35B-Merged
# Run inference directly in the terminal:
llama cli -hf osk-arr00/BigBang-Aquila-35B-Merged

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf osk-arr00/BigBang-Aquila-35B-Merged
# Run inference directly in the terminal:
llama cli -hf osk-arr00/BigBang-Aquila-35B-Merged

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf osk-arr00/BigBang-Aquila-35B-Merged
# Run inference directly in the terminal:
./llama-cli -hf osk-arr00/BigBang-Aquila-35B-Merged

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf osk-arr00/BigBang-Aquila-35B-Merged
# Run inference directly in the terminal:
./build/bin/llama-cli -hf osk-arr00/BigBang-Aquila-35B-Merged

Use Docker

docker model run hf.co/osk-arr00/BigBang-Aquila-35B-Merged

LM Studio
Jan

vLLM

How to use osk-arr00/BigBang-Aquila-35B-Merged with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "osk-arr00/BigBang-Aquila-35B-Merged"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "osk-arr00/BigBang-Aquila-35B-Merged",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/osk-arr00/BigBang-Aquila-35B-Merged

SGLang

How to use osk-arr00/BigBang-Aquila-35B-Merged 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 "osk-arr00/BigBang-Aquila-35B-Merged" \
    --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": "osk-arr00/BigBang-Aquila-35B-Merged",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "osk-arr00/BigBang-Aquila-35B-Merged" \
        --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": "osk-arr00/BigBang-Aquila-35B-Merged",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Ollama
How to use osk-arr00/BigBang-Aquila-35B-Merged with Ollama:
```
ollama run hf.co/osk-arr00/BigBang-Aquila-35B-Merged
```

Unsloth Studio

How to use osk-arr00/BigBang-Aquila-35B-Merged with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for osk-arr00/BigBang-Aquila-35B-Merged to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for osk-arr00/BigBang-Aquila-35B-Merged to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for osk-arr00/BigBang-Aquila-35B-Merged to start chatting

How to use osk-arr00/BigBang-Aquila-35B-Merged with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf osk-arr00/BigBang-Aquila-35B-Merged

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "osk-arr00/BigBang-Aquila-35B-Merged"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use osk-arr00/BigBang-Aquila-35B-Merged with Docker Model Runner:
```
docker model run hf.co/osk-arr00/BigBang-Aquila-35B-Merged
```

Lemonade

How to use osk-arr00/BigBang-Aquila-35B-Merged with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull osk-arr00/BigBang-Aquila-35B-Merged

Run and chat with the model

lemonade run user.BigBang-Aquila-35B-Merged-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use osk-arr00/BigBang-Aquila-35B-Merged with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf osk-arr00/BigBang-Aquila-35B-Merged

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default osk-arr00/BigBang-Aquila-35B-Merged

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use osk-arr00/BigBang-Aquila-35B-Merged with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf osk-arr00/BigBang-Aquila-35B-Merged

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "osk-arr00/BigBang-Aquila-35B-Merged" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

BigBang-Aquila-35B-Merged

DARE-TIES merge of two specialists derived from the same base Qwen/Qwen3.6-35B-A3B.

Merged models

Model	Weight	Specialty
`endless-frontier/BigBang-v1`	0.55	Formal reasoning, math, code, research
`XYZAILab/XYZ-Aquila-mini`	0.45	Web agency (deep search, scraping, tool-calling)

Method

DARE-TIES (Drop And REscale + Trimming, Electing Sign & Disjoint Merge)
Base: Qwen/Qwen3.6-35B-A3B, dtype bfloat16, density 0.5
Mergeability validated: cos(τ_BigBang, τ_Aquila) = 0.057 (nearly orthogonal → both capabilities retained)

Configuration (mergekit)

merge_method: dare_ties
base_model: Qwen/Qwen3.6-35B-A3B
dtype: bfloat16
parameters:
  int8_mask: true
  normalize: true
models:
  - model: endless-frontier/BigBang-v1
    parameters: {weight: 0.55, density: 0.50}
  - model: XYZAILab/XYZ-Aquila-mini
    parameters: {weight: 0.45, density: 0.50}

Evaluation (agentic eval, 9 hard cases)

Domain	Result
BigBang (math/code)	3/3 PASS
Aquila (web agency)	1/3 PASS
Intersection (web+python+reasoning)	2/3 PASS
Total	6/9 PASS

Derivatives

Abliterated: osk-arr00/BigBang-Aquila-35B-Merged-Abliterated
Quantized GGUFs: osk-arr00/BigBang-Aquila-35B-GGUF

Files

model-*.safetensors — merge weights (16 shards, BF16)
BigBang-Aquila-35B-MTP-draft.gguf — generic MTP draft (embedding + output head; the real MTP head was not merged)
eval_report.txt — agentic eval report
abliterate.py, build_calib.py, eval_agent.py, stage2_quantize.sh — pipeline scripts

Notes

Multimodal checkpoint (image-text-to-text). The LLM lives under model.language_model.*.
MTP: this checkpoint does NOT include the real MTP head (mergekit did not merge the 19 MTP tensors). To use MTP, re-insert them from Qwen/Qwen3.6-35B-A3B (see insert_mtp.py).
Requires transformers>=5.12 + kernels flash-linear-attention and causal_conv1d.

Downloads last month: 88

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for osk-arr00/BigBang-Aquila-35B-Merged

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Finetunes

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Quantizations

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