Instructions to use Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16 with Transformers:

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

pipe = pipeline("text-generation", model="Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16")
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("Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16")
model = AutoModelForMultimodalLM.from_pretrained("Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16", 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

vLLM

How to use Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16

SGLang

How to use Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16 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 "Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16" \
    --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": "Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16",
		"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 "Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16" \
        --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": "Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16 with Docker Model Runner:
```
docker model run hf.co/Ttimms/KAT-Coder-V2.5-Dev-REAP-50-bf16
```

KAT-Coder-V2.5-Dev · REAP-50 (pruned bf16)

The 50%-REAP-pruned Kwaipilot/KAT-Coder-V2.5-Dev in bf16 — the source checkpoint behind the NVFP4 and GGUF releases. Provided so others can produce their own quants (AWQ, EXL2, MLX, custom GGUF, …).

256 → 128 experts via REAP (Router-weighted Expert Activation Pruning), with a router-renormalization fix over the survivors.
qwen3_5_moe hybrid (Gated-DeltaNet + attention + MoE). No MTP head (mtp_num_hidden_layers: 0); reload-verified with a real forward pass.
Vision tower not stripped in this checkpoint — use Qwen3_5MoeForCausalLM for text-only.

Base-model quality (measured on the NVFP4A16 quant of this checkpoint, greedy, instruct): HumanEval+ ~90%, MBPP+ ~90%. These are single greedy draws; the accuracy harness has a measured run-to-run spread of 1.85–4.27 pp per task (2026-09-05), so read small differences as unresolved.

Architecture

graph TD
    Base["Kwaipilot/KAT-Coder-V2.5-Dev<br/>Qwen3.5 MoE - 256 experts - ~69 GB bf16"]
    subgraph Build ["Build - RTX 5070 Ti, SM120"]
        REAP["REAP expert prune 50%  256 -> 128 experts + router-renorm fix"]
        Strip["strip vision tower + 333 untrained tensors"]
        Quant["NVFP4A16 quantize (weight-only, data-free, 82 s)"]
    end
    subgraph HF ["Published formats"]
        A16["REAP-50-NVFP4A16 - 12.45 GiB (default, vLLM)"]
        W4A4["REAP-50-NVFP4-W4A4 (native FP4)"]
        GPTQ["REAP-50-NVFP4A16-GPTQ (null result, kept for transparency)"]
        GGUF["REAP-50-GGUF (Q4_K_M / Q5_K_M / Q6_K / Q8_0)"]
        BF16["REAP-50-bf16 (pruned source)"]
    end
    Bench["A16 single draws - HumanEval+ 89.0% - MBPP+ 90.5% - SWE-bench Verified 52.0%"]
    Base --> REAP --> Strip --> Quant --> A16
    Strip --> BF16
    BF16 -. re-quant .-> W4A4
    BF16 -. re-quant .-> GPTQ
    BF16 -. convert .-> GGUF
    A16 --> Bench