RedHatAI/SmolLM3-3B-FP8-dynamic

Model Overview

• —Model Architecture: SmolLM3-3B
• —Input: Text
• —Output: Text
• —Model Optimizations:
• —Weight quantization: FP8
• —Activation quantization: FP8
• —Release Date: 07/28/2025
• —Version: 1.0
• —License(s): Apache-2.0
• —Model Developers: RedHat (Neural Magic)

Model Optimizations

This model was obtained by quantizing activation and weights of SmolLM3-3B to FP8 data type. This optimization reduces the number of bits used to represent weights and activations from 16 to 8, reducing GPU memory requirements (by approximately 50%) and increasing matrix-multiply compute throughput (by approximately 2x). Weight quantization also reduces disk size requirements by approximately 50%.

Only weights and activations of the linear operators within transformers blocks are quantized. Weights are quantized with a symmetric static per-channel scheme, whereas activations are quantized with a symmetric dynamic per-token scheme. The llm-compressor library is used for quantization.

Deployment

This model can be deployed efficiently using the vLLM backend, as shown in the example below.

from vllm import LLM, SamplingParams
from transformers import AutoTokenizer

model_id = "RedHatAI/SmolLM3-3B-FP8-dynamic"
number_gpus = 1

sampling_params = SamplingParams(temperature=0.7, top_p=0.8, max_tokens=256)

tokenizer = AutoTokenizer.from_pretrained(model_id)

messages = [
    {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
    {"role": "user", "content": "Who are you?"},
]

prompts = tokenizer.apply_chat_template(messages, tokenize=False)

llm = LLM(model=model_id, tensor_parallel_size=number_gpus)

outputs = llm.generate(prompts, sampling_params)

generated_text = outputs[0].outputs[0].text
print(generated_text)

vLLM aslo supports OpenAI-compatible serving. See the documentation for more details.

Creation

<details> <summary>Creation details</summary> This model was created with llm-compressor by running the code snippet below.

  from transformers import AutoModelForCausalLM, AutoTokenizer
  from llmcompressor.modifiers.quantization import QuantizationModifier
  from llmcompressor.transformers import oneshot
  
  # Load model
  model_stub = "HuggingFaceTB/SmolLM3-3B"
  model_name = model_stub.split("/")[-1]
  
  tokenizer = AutoTokenizer.from_pretrained(model_stub)
  
  model = AutoModelForCausalLM.from_pretrained(
      model_stub,
      device_map="auto",
      torch_dtype="auto",
  )
  
  # Configure the quantization algorithm and scheme
  recipe = QuantizationModifier(
      targets="Linear",
      scheme="FP8_dynamic",
      ignore=["lm_head"],
  )
  
  # Apply quantization
  oneshot(
      model=model,
      recipe=recipe,
  )
  
  # Save to disk in compressed-tensors format
  save_path = model_name + "-FP8-dynamic"
  model.save_pretrained(save_path)
  tokenizer.save_pretrained(save_path)
  print(f"Model and tokenizer saved to: {save_path}")

</details>

Evaluation

This model was evaluated on the well-known reasoning tasks: AIME24, Math-500, and GPQA-Diamond. In all cases, model outputs were generated with the vLLM engine, and evals are collected through LightEval library.

<details> <summary>Evaluation details</summary>

    export VLLM_WORKER_MULTIPROC_METHOD=spawn
    export MODEL="RedHatAI/SmolLM3-3B-FP8-dynamic"
    export MODEL_ARGS="model_name=$MODEL,dtype=auto,max_model_length=65536,gpu_memory_utilization=0.9,tensor_parallel_size=1,add_special_tokens=False,generation_parameters={max_new_tokens:32768,temperature:0.6,top_p:0.95}"

    export TASK=aime24 # {aime24, math_500, gpqa:diamond}

    lighteval vllm $MODEL_ARGS "lighteval|${TASK}|0|0" \
        --use-chat-template \
        --output-dir out_dir

</details>

Accuracy

<table> <tr> <th>Category </th> <th>Benchmark </th> <th>HuggingFaceTB/SmolLM3-3B </th> <th>RedHatAI/SmolLM3-3B-FP8-dynamic<br>(this model) </th> <th>Recovery </th> </tr> <tr> <td rowspan="8" ><strong>Reasoning</strong> </td> <td>AIME24 (pass@1:64) </td> <td>45.31 </td> <td>47.50 </td> <td>104.83% </td> </tr> <tr> <td>MATH-500 (pass@1:4) </td> <td>89.30 </td> <td>88.30 </td> <td>98.88% </td> </tr> <tr> <td>GPQA-Diamond (pass@1:8) </td> <td>41.22 </td> <td>40.91 </td> <td>99.25% </td> </tr> <tr> <td><strong>Average</strong> </td> <td><strong>58.61</strong> </td> <td><strong>58.90</strong> </td> <td><strong>100.5%</strong> </td> </tr> <tr> </table>