Instructions to use AxionML/Laguna-S-2.1-NVFP4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use AxionML/Laguna-S-2.1-NVFP4 with Transformers:

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

pipe = pipeline("text-generation", model="AxionML/Laguna-S-2.1-NVFP4", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("AxionML/Laguna-S-2.1-NVFP4", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("AxionML/Laguna-S-2.1-NVFP4", trust_remote_code=True, device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use AxionML/Laguna-S-2.1-NVFP4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "AxionML/Laguna-S-2.1-NVFP4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "AxionML/Laguna-S-2.1-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/AxionML/Laguna-S-2.1-NVFP4

SGLang

How to use AxionML/Laguna-S-2.1-NVFP4 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 "AxionML/Laguna-S-2.1-NVFP4" \
    --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": "AxionML/Laguna-S-2.1-NVFP4",
		"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 "AxionML/Laguna-S-2.1-NVFP4" \
        --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": "AxionML/Laguna-S-2.1-NVFP4",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use AxionML/Laguna-S-2.1-NVFP4 with Docker Model Runner:
```
docker model run hf.co/AxionML/Laguna-S-2.1-NVFP4
```

AxionML Laguna-S-2.1-NVFP4

Mirrored by AxionML for open-source serving and deployment use cases. Part of AxionML's effort to provide ready-to-serve quantized models for the community.

Quantized by Poolside. The weights in this repository are an unmodified copy of poolside/Laguna-S-2.1-NVFP4 (revision 826aacdf6d8b2699d4e367def6f17c83b06044c2). All credit for the quantization belongs to Poolside.

This is an NVFP4-quantized version of poolside/Laguna-S-2.1 (117.6B total parameters, 8.5B activated), quantized with NVIDIA Model Optimizer.

About NVFP4 quantization: NVFP4 on Blackwell couples a compact E2M1 FP4 codebook with blockwise FP8 (E4M3) scaling over 16-element micro-blocks, so that 4-bit stored values remain numerically useful for neural-network computation. The E2M1 codebook provides a small, nonuniform set of representable magnitudes up to ±6 and relies on saturating behavior rather than IEEE NaN/Inf encodings to maximize usable range per bit. Using an FP8 block scale (rather than power-of-two-only E8M0) enables fractional scales and error-minimizing scale selection. On Blackwell Tensor Cores, native FP4 multipliers exploit E2M1 simplicity while higher-precision FP32 accumulation protects dot-product accuracy.

Ready for commercial and non-commercial use under OpenMDW-1.1 (included as LICENSE.md), consistent with Poolside's Acceptable Use Policy.

Model Summary


Architecture	MoE; 48 layers (12 global attention + 36 sliding-window, window 512), softplus-gated attention
Total Parameters	117.6B
Activated Parameters	8.5B
Experts	256 + 1 shared
Context Length	1,048,576 tokens
Checkpoint Size	~100 GB

Evaluation Results

Benchmark	Laguna S 2.1
Terminal-Bench 2.1	70.2
SWE-bench Multilingual	78.5
SWE-Bench Pro (Public)	59.4
DeepSWE	40.4
SWE Atlas (Codebase QnA)	46.2
Toolathlon Verified	49.7

Scores are from the Laguna S 2.1 model card.

Quantization Details

Quantization format: NVFP4 (compressed-tensors nvfp4-pack-quantized, group size 16) on routed-expert gate_proj / up_proj / down_proj; Poolside's official NVFP4 release, calibrated at the 1M-context configuration
KV cache: FP8

Usage

Deploy with SGLang

# Poolside notes NVFP4 does not currently run correctly on SGLang; use vLLM below.

Deploy with vLLM

vllm serve AxionML/Laguna-S-2.1-NVFP4 \
    --enable-auto-tool-choice \
    --tool-call-parser poolside_v1 \
    --reasoning-parser poolside_v1 \
    --max-model-len 262144

Requires vLLM ≥ 0.25.0; quantization is detected automatically. Optional DFlash speculative decoding: add --speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash-NVFP4","num_speculative_tokens":7,"method":"dflash"}' --max-num-seqs 32. Serve with the sampling defaults in generation_config.json.

Limitations

The base model was trained on data that may contain toxic language and societal biases. The quantized model inherits these limitations. It may generate inaccurate, biased, or offensive content. Please refer to the original model card and the upstream quantized model card for full details.

Credits

Base model: poolside/Laguna-S-2.1
Quantization: poolside/Laguna-S-2.1-NVFP4 by Poolside
Mirror: AxionML

Downloads last month: 588

Safetensors

Model size

118B params

Tensor type

BF16

Model tree for AxionML/Laguna-S-2.1-NVFP4

Base model

poolside/Laguna-S-2.1

Quantized

(96)

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