Instructions to use EvanOLeary/laguna-xs2-dense-k8-cuda-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EvanOLeary/laguna-xs2-dense-k8-cuda-sft with Transformers:

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

pipe = pipeline("text-generation", model="EvanOLeary/laguna-xs2-dense-k8-cuda-sft", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("EvanOLeary/laguna-xs2-dense-k8-cuda-sft", trust_remote_code=True, device_map="auto")

Kernels

How to use EvanOLeary/laguna-xs2-dense-k8-cuda-sft with Kernels:

# !pip install kernels

from kernels import get_kernel

# a version (or an explicit revision) is required; see the "Files and versions" tab for the available ones
kernel = get_kernel("EvanOLeary/laguna-xs2-dense-k8-cuda-sft", version=1)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EvanOLeary/laguna-xs2-dense-k8-cuda-sft with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EvanOLeary/laguna-xs2-dense-k8-cuda-sft"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EvanOLeary/laguna-xs2-dense-k8-cuda-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/EvanOLeary/laguna-xs2-dense-k8-cuda-sft

SGLang

How to use EvanOLeary/laguna-xs2-dense-k8-cuda-sft 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 "EvanOLeary/laguna-xs2-dense-k8-cuda-sft" \
    --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": "EvanOLeary/laguna-xs2-dense-k8-cuda-sft",
		"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 "EvanOLeary/laguna-xs2-dense-k8-cuda-sft" \
        --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": "EvanOLeary/laguna-xs2-dense-k8-cuda-sft",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use EvanOLeary/laguna-xs2-dense-k8-cuda-sft with Docker Model Runner:
```
docker model run hf.co/EvanOLeary/laguna-xs2-dense-k8-cuda-sft
```

EvanOLeary commited on May 30

Commit

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@@ -78,10 +78,20 @@ KernelBook (Triton) 40% · OpenCodeInstruct (Python) 30% · **SakanaAI CUDA-C++
 ### SFT curve (CUDA, 400 steps, CE 0.68→0.32)
 ![sft](https://huggingface.co/EvanOLeary/laguna-xs2-dense-k8-cuda-sft/resolve/main/sft_curve.png)
-## KernelBench-Lite baseline (this SFT model)
-generate → compile (`load_inline`) → correctness vs PyTorch eager → speedup:
-- **ReLU:** compiles + correct ✅, **0.93×** vs eager (best-of-4). It produces real, working CUDA.
-- fast_0/fast_1 to be expanded across the op set; this is the **baseline RFT will improve**.
 ---
 ## Stage 3 — RFT (reserved — running now)

 ### SFT curve (CUDA, 400 steps, CE 0.68→0.32)
 ![sft](https://huggingface.co/EvanOLeary/laguna-xs2-dense-k8-cuda-sft/resolve/main/sft_curve.png)
+## Results — KernelBench-Lite (subprocess-isolated, cross-validated) — VALID
+Reliable on simple elementwise ops; consistent across **three independent harnesses**:
+| Op | pass@4 (best-of-4) | pass@3 (isolated) | speedup vs eager |
+|---|---|---|---|
+| **ReLU** | **3/4 correct** | 2/3 correct | **0.93×** |
+| **Tanh** | **3/4 correct** | 2/3 correct | — |
+| Sigmoid | 0/4 | 0/3 | fails |
+ReLU & Tanh ~70–75% at pass@k (three runs agree). Harder ops fail on **float4-cast bugs**
+(`float4* v = float4* x;` vs `reinterpret_cast<float4*>(x)`) → the RFT compile-reward target.
+**vs teacher:** 11× smaller, 12× less VRAM, **+26% faster decode (32.1 vs 25.4 tok/s)**; neither beats
+PyTorch eager on single elementwise ops (bandwidth-bound — needs fusion). Eval must be process-isolated
+(a bad kernel corrupts the CUDA context). Code/results: [github.com/Tyronita/laguna-dense-cuda-kernels](https://github.com/Tyronita/laguna-dense-cuda-kernels).
 ---
 ## Stage 3 — RFT (reserved — running now)