---
language:
- en
license: mit
library_name: transformers
tags:
- text-generation
- transformers
- safetensors
- qwen3
- chat
- conversational
- text-generation-inference
- compressed-tensors
pipeline_tag: text-generation
---

# Model Overview

* **Model Optimizations:**
  * **Weight quantization:** FP8
  * **Activation quantization:** FP8
* **Release Date:** 1/29/2025

Quantized version of deepseek-ai/DeepSeek-R1-0528-Qwen3-8B 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

```bash
python -m sglang.launch_server --model-path JamAndTeaStudios/DeepSeek-R1-0528-Qwen3-8B-FP8-Dynamic \
--port 30000 --host 0.0.0.0
```

### Use with vLLM

```bash
python -m vllm.entrypoints.openai.api_server --model JamAndTeaStudios/DeepSeek-R1-0528-Qwen3-8B-FP8-Dynamic \
--port 8000 --host 0.0.0.0
```

## Creation

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

<details>
<summary>Model Creation Code</summary>

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

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

MODEL_ID = "deepseek-ai/DeepSeek-R1-0528-Qwen3-8B"

# 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("==========================================")
```

</details>

## Evaluation

TBA

## Base Model

This model is a quantized version of [deepseek-ai/DeepSeek-R1-0528-Qwen3-8B](https://huggingface.co/deepseek-ai/DeepSeek-R1-0528-Qwen3-8B).

## Usage

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load the quantized model
model_id = "JamAndTeaStudios/DeepSeek-R1-0528-Qwen3-8B-FP8-Dynamic"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.float16,
    device_map="auto"
)

# Example usage
messages = [
    {"role": "user", "content": "What is the capital of France?"}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=100,
        temperature=0.7,
        do_sample=True
    )

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)
```

## License

This model is released under the MIT License.