Instructions to use debackerl/KAT-Coder-V2.5-Dev-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use debackerl/KAT-Coder-V2.5-Dev-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="debackerl/KAT-Coder-V2.5-Dev-FP8")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("debackerl/KAT-Coder-V2.5-Dev-FP8")
model = AutoModelForCausalLM.from_pretrained("debackerl/KAT-Coder-V2.5-Dev-FP8", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use debackerl/KAT-Coder-V2.5-Dev-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "debackerl/KAT-Coder-V2.5-Dev-FP8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "debackerl/KAT-Coder-V2.5-Dev-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/debackerl/KAT-Coder-V2.5-Dev-FP8

SGLang

How to use debackerl/KAT-Coder-V2.5-Dev-FP8 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 "debackerl/KAT-Coder-V2.5-Dev-FP8" \
    --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": "debackerl/KAT-Coder-V2.5-Dev-FP8",
		"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 "debackerl/KAT-Coder-V2.5-Dev-FP8" \
        --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": "debackerl/KAT-Coder-V2.5-Dev-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use debackerl/KAT-Coder-V2.5-Dev-FP8 with Docker Model Runner:
```
docker model run hf.co/debackerl/KAT-Coder-V2.5-Dev-FP8
```

Quantization of KAT-Coder-V2.5-Dev to FP8 Dynamic

Quantized using llm-compressor.

recipe = QuantizationModifier(
    targets="Linear",
    scheme="FP8_DYNAMIC",
     ignore=[
        "re:.*lm_head",
        "re:model.visual.*",
        "re:.*mlp.gate$",
        "re:.*embed_tokens$",
        "re:.*shared_expert_gate$",
      ],
)

SGLang

To run using SGLang:

sglang serve --model-path debackerl/KAT-Coder-V2.5-Dev-FP8 --port 8000 --mem-fraction-static 0.9 --context-length 262144 --reasoning-parser qwen3 --tool-call-parser qwen3_coder

Downloads last month: 3

Safetensors

Model size

35B params

Tensor type

BF16

F8_E4M3

Model tree for debackerl/KAT-Coder-V2.5-Dev-FP8

Base model

Kwaipilot/KAT-Coder-V2.5-Dev

Quantized

(34)

this model