Instructions to use nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8", trust_remote_code=True, 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 nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nm-testing/DeepSeek-Coder-V2-Lite-Instruct-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": "nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8

SGLang

How to use nm-testing/DeepSeek-Coder-V2-Lite-Instruct-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 "nm-testing/DeepSeek-Coder-V2-Lite-Instruct-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": "nm-testing/DeepSeek-Coder-V2-Lite-Instruct-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 "nm-testing/DeepSeek-Coder-V2-Lite-Instruct-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": "nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8 with Docker Model Runner:
```
docker model run hf.co/nm-testing/DeepSeek-Coder-V2-Lite-Instruct-FP8
```

DeepSeek-Coder-V2-Lite-Instruct-FP8 / README.md

mgoin

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	---
	base_model:
	- deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
	---

	Created using llm-compressor for use with vLLM:
	```python
	from datasets import load_dataset
	from transformers import AutoModelForCausalLM, AutoTokenizer

	from llmcompressor.modifiers.quantization import QuantizationModifier
	from llmcompressor.transformers import oneshot

	# NOTE: transformers 4.48.0 has an import error with DeepSeek.
	# Please consider either downgrading your transformers version to a
	# previous version or upgrading to a version where this bug is fixed

	# select a Mixture of Experts model for quantization
	MODEL_ID = "deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct"

	model = AutoModelForCausalLM.from_pretrained(
	MODEL_ID, device_map="auto", torch_dtype="auto", trust_remote_code=True
	)
	tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

	# Select calibration dataset.
	# its recommended to use more calibration samples for MoE models so each expert is hit
	DATASET_ID = "HuggingFaceH4/ultrachat_200k"
	DATASET_SPLIT = "train_sft"
	NUM_CALIBRATION_SAMPLES = 2048
	MAX_SEQUENCE_LENGTH = 2048


	# Load dataset and preprocess.
	ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
	ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))


	def preprocess(example):
	return {
	"text": tokenizer.apply_chat_template(
	example["messages"],
	tokenize=False,
	)
	}


	ds = ds.map(preprocess)


	# Tokenize inputs.
	def tokenize(sample):
	return tokenizer(
	sample["text"],
	padding=False,
	max_length=MAX_SEQUENCE_LENGTH,
	truncation=True,
	add_special_tokens=False,
	)


	ds = ds.map(tokenize, remove_columns=ds.column_names)

	# define a llmcompressor recipe for FP8 W8A8 quantization
	# since the MoE gate layers are sensitive to quantization, we add them to the ignore
	# list so they remain at full precision
	recipe = [
	QuantizationModifier(
	targets="Linear",
	scheme="FP8",
	ignore=["lm_head", "re:.*mlp.gate$"],
	),
	]

	SAVE_DIR = MODEL_ID.split("/")[1] + "-FP8"

	oneshot(
	model=model,
	dataset=ds,
	recipe=recipe,
	max_seq_length=MAX_SEQUENCE_LENGTH,
	num_calibration_samples=NUM_CALIBRATION_SAMPLES,
	trust_remote_code_model=True,
	save_compressed=True,
	output_dir=SAVE_DIR,
	)

	print("========== SAMPLE GENERATION ==============")
	SAMPLE_INPUT = ["I love quantization because"]
	tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
	inputs = tokenizer(SAMPLE_INPUT, return_tensors="pt", padding=True).to(model.device)
	output = model.generate(**inputs, max_length=50)
	text_output = tokenizer.batch_decode(output)
	print(text_output)

	```