Instructions to use Soofi-Project/Soofi-S-Instruct-Preview-FP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Soofi-Project/Soofi-S-Instruct-Preview-FP8 with Transformers:

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

pipe = pipeline("text-generation", model="Soofi-Project/Soofi-S-Instruct-Preview-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("Soofi-Project/Soofi-S-Instruct-Preview-FP8", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Soofi-Project/Soofi-S-Instruct-Preview-FP8", trust_remote_code=True)
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 Soofi-Project/Soofi-S-Instruct-Preview-FP8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Soofi-Project/Soofi-S-Instruct-Preview-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": "Soofi-Project/Soofi-S-Instruct-Preview-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Soofi-Project/Soofi-S-Instruct-Preview-FP8

SGLang

How to use Soofi-Project/Soofi-S-Instruct-Preview-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 "Soofi-Project/Soofi-S-Instruct-Preview-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": "Soofi-Project/Soofi-S-Instruct-Preview-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 "Soofi-Project/Soofi-S-Instruct-Preview-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": "Soofi-Project/Soofi-S-Instruct-Preview-FP8",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Soofi-Project/Soofi-S-Instruct-Preview-FP8 with Docker Model Runner:
```
docker model run hf.co/Soofi-Project/Soofi-S-Instruct-Preview-FP8
```

Soofi-S-Instruct-Preview-FP8 / README.md

danielporta

Update model card

90ed419 verified 7 days ago

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	---
	base_model: Soofi-Project/Soofi-S-Instruct-Preview
	license: other
	language:
	- en
	- de
	- es
	- fr
	- it
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- fp8
	- compressed-tensors
	- mamba-2
	- moe
	- soofi
	- vllm
	- quantized
	- preview
	quantized_by: Soofi-Project
	---

	# Soofi-S-Instruct-Preview-FP8

	> ⚠️ Preview / internal checkpoint. Weights and metadata may still change.

	FP8 (W8A8 dynamic) quantization of
	[Soofi-Project/Soofi-S-Instruct-Preview](https://huggingface.co/Soofi-Project/Soofi-S-Instruct-Preview)
	for space-efficient serving with vLLM.

	Quantized from bf16 safetensors with
	[`llm-compressor`](https://github.com/vllm-project/llm-compressor) to the
	`compressed-tensors` FP8 format, which vLLM loads natively at roughly **half the
	weight memory** of the bf16 checkpoint.

	> Architecture support: SOOFI-S is a custom hybrid Mamba-2/MoE model and
	> ships with its own modeling code (`trust_remote_code`). FP8 serving requires a
	> vLLM build that understands this architecture — verify against the actual
	> checkpoint before relying on this artifact.

	## Quantization details

	\| Property \| Value \|
	\|---\|---\|
	\| Scheme \| FP8_DYNAMIC (W8A8) \|
	\| Weights \| FP8 E4M3, per-channel static scales \|
	\| Activations \| FP8 E4M3, per-token dynamic (quantized at runtime) \|
	\| Calibration \| none (data-free) \|
	\| Kept in full precision \| `lm_head`, Mamba-2 `in_proj`/`out_proj` \|

	> Why dynamic / data-free? Dynamic per-token activation scales need no
	> calibration dataset and are robust for MoE, where a single static activation
	> scale across experts is a poor fit. The MoE router is not an nn.Linear, so
	> it stays full precision automatically; the Mamba-2 `in_proj`/`out_proj` (the
	> recurrent SSM path) are kept bf16 as the most quantization-sensitive layers.
	>
	> *Size scales with the total* 30B parameters** (not the 3.5B active), so the
	> FP8 weights are ~half the bf16 size minus the few full-precision tensors above.

	## Usage with vLLM

	```bash
	# OpenAI-compatible server
	vllm serve Soofi-Project/Soofi-S-Instruct-Preview-FP8 --trust-remote-code

	# query it
	curl http://localhost:8000/v1/chat/completions \
	-H "Content-Type: application/json" \
	-d '{"model": "Soofi-Project/Soofi-S-Instruct-Preview-FP8",
	"messages": [{"role": "user", "content": "Explain AI sovereignty in one sentence."}]}'
	```

	```python
	from vllm import LLM, SamplingParams

	llm = LLM(model="Soofi-Project/Soofi-S-Instruct-Preview-FP8", trust_remote_code=True)
	out = llm.chat([{"role": "user", "content": "Explain AI sovereignty in one sentence."}],
	SamplingParams(temperature=0.6, top_p=0.95))
	print(out[0].outputs[0].text)
	```

	> `--trust-remote-code` is required: the custom hybrid Mamba-2/MoE modeling
	> code travels with the checkpoint. FP8 needs a GPU with hardware FP8 support
	> (NVIDIA Hopper/Ada/Blackwell — e.g. H100, L40S, RTX 4090) for the fast path;
	> on older GPUs vLLM falls back to a slower Marlin kernel.
	>
	> The chat template is applied by vLLM's chat endpoint, so the model's native
	> identity and tool-calling format work out of the box — no manual template.

	## Architecture note

	This is a hybrid Mixture-of-Experts model designed from scratch: 23
	Mamba-2/MoE layers + 6 attention layers, 128 routing experts + 1 shared expert
	per MoE layer, 6 experts active per token (30B total / 3.5B active). FP8 is
	applied to the Linear layers (attention/MoE expert projections); the SSM
	(Mamba-2) recurrent parameters and the router stay in higher precision. A recent
	version of vLLM is recommended.

	## Related models

	- Base (bf16): [Soofi-Project/Soofi-S-Instruct-Preview](https://huggingface.co/Soofi-Project/Soofi-S-Instruct-Preview)
	- GGUF (llama.cpp/Ollama): [Soofi-Project/Soofi-S-Instruct-Preview-GGUF](https://huggingface.co/Soofi-Project/Soofi-S-Instruct-Preview-GGUF)

	## License & provenance

	Released under a custom license ("Other"), following the base model
	[Soofi-Project/Soofi-S-Instruct-Preview](https://huggingface.co/Soofi-Project/Soofi-S-Instruct-Preview).
	TODO: mirror the full license text once the base model card defines it.