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

Libraries

How to use tcclaviger/Laguna-S-2.1-RFA_L with Transformers:

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

pipe = pipeline("text-generation", model="tcclaviger/Laguna-S-2.1-RFA_L", 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("tcclaviger/Laguna-S-2.1-RFA_L", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("tcclaviger/Laguna-S-2.1-RFA_L", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tcclaviger/Laguna-S-2.1-RFA_L with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/tcclaviger/Laguna-S-2.1-RFA_L

SGLang

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

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

🔥 UPDATED — tokenizer refreshed & quantization improved:
layer-0 dense MLP and shared experts now preserved in BF16
Chat Template from: https://huggingface.co/sanjxz/Laguna-S-2.1-Agentic-Chat-Template-Jinja 🔥

Linear-RFA 4-bit quant of poolside/Laguna-S-2.1

This is tcclaviger/Laguna-S-2.1-RFA_L — an RFA 4-bit quantization of Poolside's Laguna-S-2.1. The _L (linear-only) build quantizes the MLP and expert linear layers to 4-bit (IQ4_NL grid, group 16, Hadamard-16 rotation, asymmetric block-float scales) while keeping all attention, the router gate, the lm_head, the layer-0 dense MLP, and the shared experts in bf16. 71.2 GB total, 4.504 bits/weight. All credit for the model to Poolside; this repo only changes the numerics. The full original model card is preserved verbatim below.

Runtime: requires tcclaviger/vllm:latest — an RDNA 4 (gfx12xx) vLLM image and the only build with the RFA kernels; no other vLLM build loads these weights. Not validated on any other hardware at this time.

DFlash speculative decoding works with this RFA checkpoint. Pair it with the Laguna-S-2.1-DFlash draft model exactly as documented in the vLLM section below — the RFA quant does not touch the attention path, so the DFlash draft head runs unchanged.

**DRY has been incorportaed if desired into the docker image as has repetition blocking guard, two layers of protention. See the help for DRY parameters.

Evaluation results (this RFA quant)

Eval Result

Throughput 80.6 tok/s out @ conc 1 (DFlash accept ~2.1); 615.7 tok/s out @ conc 50, ISL 512

WikiText-2 PPL 8.109 ± 0.043 (n_ctx 2048, llama.cpp-compatible)

Loglikelihood acc arc_challenge 0.503 / arc_easy 0.769 / winogrande 0.658 / hellaswag 0.814 (acc_norm)

tool-eval-bench (no-think) 89/100 (full 69 scenarios)

GSM8K / MMLU / IFEval 90% / 60% / 95% (prompt-level)

Long-context code recall 0.980 overall (py 1.00 / js 0.93 / rs 0.99 / cpp 1.00); tool pass 93.5%

Hard Mode agentic (thinking-ON) 73/100 — 10 pass / 2 partial / 3 fail

Test harnesses: vllm bench serve (random dataset, saturation sweep) · llama.cpp-compatible WikiText-2 perplexity · lm-evaluation-harness (loglikelihood, local-completions) · tool-eval-bench v2 (69 scenarios + GSM8K/MMLU/IFEval + 15 Hard Mode scenarios) · codeneedle (positional recall, 4 corpora + tool pass).

Hardware: tcclaviger/vllm:latest, 4× AMD AI PRO R9700 GPUs (TP4), Ryzen 9 9950X, 256 GB DDR5-6000. (Not yet fully tuned for throughput.)

Eval	Result
Throughput	80.6 tok/s out @ conc 1 (DFlash accept ~2.1); 615.7 tok/s out @ conc 50, ISL 512
WikiText-2 PPL	8.109 ± 0.043 (n_ctx 2048, llama.cpp-compatible)
Loglikelihood acc	arc_challenge 0.503 / arc_easy 0.769 / winogrande 0.658 / hellaswag 0.814 (acc_norm)
tool-eval-bench (no-think)	89/100 (full 69 scenarios)
GSM8K / MMLU / IFEval	90% / 60% / 95% (prompt-level)
Long-context code recall	0.980 overall (py 1.00 / js 0.93 / rs 0.99 / cpp 1.00); tool pass 93.5%
Hard Mode agentic (thinking-ON)	73/100 — 10 pass / 2 partial / 3 fail

Use on OpenRouter · Use on Vercel AI Gateway · Release blog post

Laguna S 2.1

Laguna S 2.1 is a 118B total parameter Mixture-of-Experts model with 8B activated parameters per token, designed for agentic coding and long-horizon work. It sits between Laguna XS 2.1 (33B-A3B) and Laguna M.1 (225B-A23B) in the Laguna series and shares the family recipe: a token-choice router with softplus gating over 256 routed experts plus one shared expert, grouped-query attention, and interleaved full/sliding-window attention.

Highlights

Mixed SWA and global attention layout: 48 layers in a 1:3 global-to-SWA ratio (12 global attention layers, 36 sliding-window layers, window 512), with softplus attention gating and per-layer-type rotary scales
1M context: 1,048,576-token context window
Native reasoning support: interleaved thinking between tool calls, with per-request control via enable_thinking
Speculative decoding: a trained DFlash draft model is available for lower-latency serving
Quantized variants: FP8, NVFP4, INT4 and GGUF
OpenMDW-1.1 license: Use and modify the model and associated materials freely for commercial and non-commercial purposes (learn more about OpenMDW)

Model overview

Number of parameters: 118B total, ~8B activated per token
Layers: 48 (12 global attention, 36 sliding-window attention)
Experts: 256 routed (top-10) plus 1 shared expert
Attention: grouped-query, 8 KV heads, head dim 128; per-head softplus output gating
Sliding window: 512 tokens
Context window: 1,048,576 tokens
Vocabulary: 100,352 tokens (Laguna family tokenizer)
Modality: text-to-text
Reasoning: interleaved thinking with preserved thinking

Benchmark results

benchmarks

Model	Size	Terminal-Bench 2.1	SWE-bench Multilingual	SWE-Bench Pro (Public Dataset)	DeepSWE	SWE Atlas (Codebase QnA)	Toolathlon Verified
Laguna S 2.1	118B-A8B	70.2%	78.5%	59.4%	40.4%	46.2%	49.7%
Tencent Hy3	295B-A21B	71.7%	75.8%	57.9%	-	-	-
Inkling	975B-A41B	63.8%	-	54.3%	-	-	45.5%*
Nemotron 3 Ultra	550B-A55B	56.4%	67.7%	-	-	-	34.3%*
DeepSeek-V4-Pro Max	1.6T-A49B	64.0%*	76.2%	55.4%	9.0%*	27.2%*	55.9%*
Kimi K3	2800B-A50B	88.3%	-	-	69%	-	-
Qwen 3.7 Max	-	74.5%*	78.3%	60.6%	-	-	-
Muse Spark 1.1	-	80%	-	61.5%	53.3%	42.2%*	75.6%
Claude Fable 5	-	88%	-	80.3%	70%	-	-

Benchmarks as of 21 July 2026. Laguna S 2.1 in bold; a dash (-) marks a benchmark a model was not evaluated on. Scores marked * are as reported by third parties: Terminal-Bench 2.1 and DeepSWE via Artificial Analysis, SWE Atlas via Scale AI's official leaderboard, and Toolathlon Verified via its official leaderboard. Full evaluation trajectories: trajectories.poolside.ai.

Usage

Laguna S 2.1 uses the same laguna architecture as Laguna XS 2.1, so the same engine integrations apply (vLLM, SGLang, Transformers, TRT-LLM, llama.cpp). At 118B parameters the BF16 checkpoint needs multiple GPUs (roughly 236GB of weights); quantized variants reduce this substantially.

vLLM

vllm serve \
    --model poolside/Laguna-S-2.1 \
    --tensor-parallel-size 4 \
    --tool-call-parser poolside_v1 \
    --reasoning-parser poolside_v1 \
    --enable-auto-tool-choice \
    --served-model-name laguna \
    --default-chat-template-kwargs '{"enable_thinking": true}'

Optional: speculative decoding with DFlash. Pair with the Laguna S 2.1 DFlash draft model by adding --speculative-config '{"model":"poolside/Laguna-S-2.1-DFlash","num_speculative_tokens":7,"method":"dflash"}'.

SGLang

python -m sglang.launch_server \
  --model-path poolside/Laguna-S-2.1 \
  --tp-size 4 \
  --reasoning-parser poolside_v1 \
  --tool-call-parser poolside_v1 \
  --trust-remote-code

TRT-LLM

trtllm-serve poolside/Laguna-S-2.1 --trust-remote-code \
    --tool_parser poolside_v1 --reasoning_parser laguna

Note the flag names differ from vLLM's (--tool_parser, and the reasoning parser is laguna, not poolside_v1).

llama.cpp

GGUF conversions are available at poolside/Laguna-S-2.1-GGUF. Serve with poolside's llama.cpp fork, branch laguna, which carries full Laguna support including DFlash speculative decoding. (Base Laguna support is also in upstream review: ggml-org/llama.cpp#25165.)

git clone --branch laguna https://github.com/poolsideai/llama.cpp
cd llama.cpp && cmake -B build && cmake --build build -j

./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf --jinja --port 8000

# with DFlash speculative decoding:
./build/bin/llama-server -m laguna-s-2.1-Q4_K_M.gguf \
  -md laguna-s-2.1-DFlash-BF16.gguf \
  --spec-type draft-dflash --spec-draft-n-max 7 -fa on --jinja --port 8000

Controlling reasoning

Laguna S 2.1 has native reasoning support and works best with preserved thinking: keep reasoning_content from prior assistant messages in the message history. The model will generally reason before calling tools and between tool calls, and may stop reasoning in follow-up steps if prior thinking blocks are dropped.

Thinking is controlled per request via the chat template:

extra_body={"chat_template_kwargs": {"enable_thinking": False}}

or at the server level with --default-chat-template-kwargs '{"enable_thinking": true}'. For agentic coding use cases we recommend enabling thinking and preserving reasoning in the message history.

License

This model is licensed under the OpenMDW-1.1 License.

Intended and Responsible Use

Laguna S 2.1 is designed for software engineering and agentic coding use cases, and you are responsible for confirming that it is appropriate for your intended application. Laguna S 2.1 is subject to the OpenMDW-1.1 License, and should be used consistently with Poolside's Acceptable Use Policy. We advise against circumventing Laguna S 2.1 safety guardrails without implementing substantially equivalent mitigations appropriate for your use case.

Please report security vulnerabilities or safety concerns to security@poolside.ai.

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Safetensors

Model size

61B params

Tensor type

BF16

Model tree for tcclaviger/Laguna-S-2.1-RFA_L

Base model

poolside/Laguna-S-2.1

Quantized

(75)

this model

🔥 UPDATED — tokenizer refreshed & quantization improved:layer-0 dense MLP and shared experts now preserved in BF16 Chat Template from: https://huggingface.co/sanjxz/Laguna-S-2.1-Agentic-Chat-Template-Jinja 🔥

Linear-RFA 4-bit quant of poolside/Laguna-S-2.1

Evaluation results (this RFA quant)

Laguna S 2.1

Highlights

Model overview

Benchmark results

Usage

vLLM

SGLang

TRT-LLM

llama.cpp

Controlling reasoning

License

Intended and Responsible Use

Model tree for tcclaviger/Laguna-S-2.1-RFA_L

🔥 UPDATED — tokenizer refreshed & quantization improved:
layer-0 dense MLP and shared experts now preserved in BF16
Chat Template from: https://huggingface.co/sanjxz/Laguna-S-2.1-Agentic-Chat-Template-Jinja 🔥