Instructions to use JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic with Transformers:

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

pipe = pipeline("text-generation", model="JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic")
model = AutoModelForCausalLM.from_pretrained("JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic")
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

vLLM

How to use JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic

SGLang

How to use JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic 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 "JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic" \
    --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": "JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic",
		"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 "JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic" \
        --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": "JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic
```

Model Overview

Model Optimizations:
- Weight quantization: FP8
- Activation quantization: FP8
Release Date: 1/28/2025

Quantized version of deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B to FP8 data type, ready for inference with SGLang >= 0.3 or vLLM >= 0.5.2. This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%. Only the weights and activations of the linear operators within transformers blocks are quantized.

Deployment

Use with SGLang

python -m sglang.launch_server --model-path JamAndTeaStudios/DeepSeek-R1-Distill-Qwen-1.5B-FP8-Dynamic \
--port 30000 --host 0.0.0.0

Creation

This model was created with llm-compressor by running the code snippet below.

Model Creation Code

from transformers import AutoModelForCausalLM, AutoTokenizer

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

MODEL_ID = "google/gemma-2-27b-it"

# 1) Load model.
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID, device_map="auto", torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# 2) Configure the quantization algorithm and scheme.
# In this case, we:
#   * quantize the weights to fp8 with per channel via ptq
#   * quantize the activations to fp8 with dynamic per token
recipe = QuantizationModifier(
    targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"]
)

# 3) Apply quantization and save in compressed-tensors format.
OUTPUT_DIR = MODEL_ID.split("/")[1] + "-FP8-Dynamic"
oneshot(
    model=model,
    recipe=recipe,
    tokenizer=tokenizer,
    output_dir=OUTPUT_DIR,
)

# Confirm generations of the quantized model look sane.
print("========== SAMPLE GENERATION ==============")
input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to("cuda")
output = model.generate(input_ids, max_new_tokens=20)
print(tokenizer.decode(output[0]))
print("==========================================")

Evaluation

TBA

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