Task	Score	Metric
IFEval (Instruction Following)	60.63%	prompt_level_strict_acc
BBH (Chain of Thought)	77.65%	exact_match
MATH (Hard)	20.77%	exact_match
GPQA (Science Q&A)	33.31%	acc_norm
MUSR (Multi-step Reasoning)	40.87%	acc_norm
MMLU-Pro	49.90%	exact_match

Average Score: 47.19%

Evaluated using lm-evaluation-harness v0.4.x

Model Details

Base Model: Steelskull/MSM-MS-Cydrion-22B
Quantization: FP8_DYNAMIC (W8A8)
Format: compressed-tensors (SafeTensors)
Memory: ~50% of original BF16 size
Quality: <1-2% degradation on benchmarks (typical)

Quick Start

vLLM (Recommended)

bash

pip install vllm

# Serve the model
vllm serve REPO_ID \
  --max-model-len 32768 \
  --gpu-memory-utilization 0.95

# Python API
from vllm import LLM
llm = LLM(model="REPO_ID")
outputs = llm.generate("Hello, how are you?")
print(outputs[0].outputs[0].text)

Transformers

python

from transformers import AutoTokenizer, AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    "REPO_ID",
    device_map="auto",
    torch_dtype="auto"
)
tokenizer = AutoTokenizer.from_pretrained("REPO_ID")

messages = [{'role': 'user', 'content': 'Hello!'}]
inputs = tokenizer.apply_chat_template(messages, return_tensors='pt').to(model.device)
outputs = model.generate(inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0]))

Quantization Details

This model was quantized using:

Tool: llmcompressor
Method: FP8_DYNAMIC (Round-to-Nearest)
Targets: All Linear layers except lm_head
Scheme: W8A8 (8-bit weights and activations)

Performance

Memory Usage

Original BF16: ~2× size of FP8
FP8 Quantized: ~50% of original
Savings: ~50% VRAM reduction

Inference Speed

Expect 1.3-1.8× faster inference vs BF16
2× higher throughput (more KV cache available)

Use Cases

Perfect for:

✅ Production inference on limited VRAM
✅ Running larger models on single GPU
✅ Cost-effective API serving
✅ High-throughput applications
✅ Extended context lengths (more KV cache)

Hardware Requirements

Minimum VRAM (approximate):

70B model: ~40 GB (RTX A6000, A100 40GB)
123B model: ~70 GB (A100 80GB, H100, H200)

Recommended:

H100/H200 for best performance
vLLM for optimized serving
Enable FP8 KV cache for extended context

Important Notes

⚠️ Quantization Trade-offs:

Slight quality degradation (typically <1-2%)
Not suitable for fine-tuning (inference only)
Best with vLLM (has FP8 kernel optimizations)

✅ Best Practices:

Use --kv-cache-dtype fp8 for longer contexts
Set --gpu-memory-utilization 0.90-0.95
Add --enforce-eager if you encounter compilation issues

Citation

If you use this model, please cite:

bibtex

@misc{model_name-fp8,
  author = {author},
  title = {model_name FP8 Dynamic Quantization},
  year = {2025},
  publisher = {HuggingFace},
  url = {https://huggingface.co/repo_id}
}

License

Inherits license from base model: Steelskull/MSM-MS-Cydrion-22B

Acknowledgments

Base model by Steelskull
Quantization via llmcompressor
Serving optimized for vLLM

Want more FP8 models? Check out my other quantizations!

sh0ck0r/MSM-MS-Cydrion-22B-FP8-Dynamic

MSM-MS-Cydrion-22B - FP8 Dynamic Quantization

Open LLM Leaderboard v2 Results