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
```

KAT-Coder-V2.5-Dev-FP8 / README.md

debackerl

Remove instructions to run the model until it's confirmed

0817fab verified 4 days ago

preview code

Raw

History Blame Contribute Delete

602 Bytes

	---
	base_model: Kwaipilot/KAT-Coder-V2.5-Dev
	base_model_relation: quantized
	license: apache-2.0
	pipeline_tag: text-generation
	library_name: transformers
	language:
	- en
	- zh
	tags:
	- code
	- agent
	- agentic-coding
	- moe
	- coding
	- quantized
	---

	# 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$",
	],
	)
	```