Instructions to use ProCreations/grug-35b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ProCreations/grug-35b with Transformers:

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

pipe = pipeline("text-generation", model="ProCreations/grug-35b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ProCreations/grug-35b")
model = AutoModelForMultimodalLM.from_pretrained("ProCreations/grug-35b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ProCreations/grug-35b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ProCreations/grug-35b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ProCreations/grug-35b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ProCreations/grug-35b

SGLang

How to use ProCreations/grug-35b 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 "ProCreations/grug-35b" \
    --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": "ProCreations/grug-35b",
		"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 "ProCreations/grug-35b" \
        --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": "ProCreations/grug-35b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ProCreations/grug-35b with Docker Model Runner:
```
docker model run hf.co/ProCreations/grug-35b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

grug-35b

grug brain now live in weight. no hidden style stick needed.

July 15, 2026 weight replacement: same public repo, rebuilt weights. Old main preserved on pre-intrinsic-style-fix-2026-07-14. New main makes Grug reasoning intrinsic under ordinary chat and tool-enabled agent prompts.

grug honest rebuild

Earlier main branch could look strongly Grug in ordinary chat because a bundled conditional template injected a style instruction when tools were absent. Tool-enabled agent clients such as OpenCode bypassed that branch, exposing too much normal planner English. This rebuild fixes weight behavior instead of hiding it with prompt steering.

started from previous merged ProCreations/grug-35b checkpoint
normal BF16 LoRA, not QLoRA
multi-pass intrinsic reasoning correction used neutral English agent scaffolds; the final policy repair supervised only the closing-think boundary and exact native tool call from non-held-out real transitions, then retained its delta only on the four dedicated reasoning/tool boundary vocabulary rows
trained under normal English coding-agent scaffolds and native tool schemas
original deepreinforce-ai/Ornith-1.0-35B chat template restored without any Grug/style instruction
intrinsic-style passes adapted the upper shared transformer path; the final tool-boundary repair calibrated only four dedicated boundary rows of the output projection; routed MoE experts remained frozen
rank 16, alpha 32, LR 1e-05
final continuation-adapter delta scale 48.0, retained on 4 native-tool vocabulary rows
final boundary repair: 1,400 non-held-out examples, 160 optimizer steps

unprompted gate

No evaluation request says Grug, requests telegraphic prose, or supplies a style prompt. The suite includes neutral chat, long normal-English agent systems, native tools, and the three exact failure shapes reported after the previous release.

intrinsic measure	result
reasoning present	100.0%
reasoning present with tools	100.0%
dialect-clean	100.0%
function-word ratio	4.15%
complex reasoning mean / median words	55.08 / 43.0
complex reasoning maximum words	127
repetitive complex traces	0

capability retention and tradeoff

Same greedy local harness as prior release: HumanEval 164, MBPP first 100 sanitized test, 18-action card, and 119-action broad tool holdout.

grug family benchmark

Same prompts, parser, runtime, decoding, and limits for both birds. All numbers are percent; bold marks the best result in each column. Ties make both rocks bold.

model	HumanEval	MBPP	card valid	card strict	card right	broad valid	broad strict	broad right
Grug v2 9B	82.9	77.0	100.0	100.0	100.0	100.0	100.0	94.1
Grug 35B	80.5	88.0	94.4	88.9	94.4	100.0	100.0	95.0

grug honest: intrinsic style correction is not free. HumanEval moves down four solved problems (82.9% to 80.5%); MBPP and tool selection improve. Previous weights remain on pre-intrinsic-style-fix-2026-07-14 for anyone preferring that tradeoff.

test	old main %	rebuilt main %
HumanEval pass@1	82.9	80.5
MBPP pass@1	87.0	88.0
card valid / strict / right	88.9 / 88.9 / 88.9	94.4 / 88.9 / 94.4
broad valid / strict / right	100.0 / 100.0 / 94.1	100.0 / 100.0 / 95.0

valid means parser found an offered tool. strict requires exact schema and required arguments. right requires the expected next tool.

Reasoning remains <think>...</think>. Native XML tool shape remains compatible with recent Transformers, vLLM, llama.cpp, and OpenAI-compatible agent clients. Popular rocks: ProCreations/grug-35b-gguf.

Downloads last month: 418

Safetensors

Model size

35B params

Tensor type

BF16

Model tree for ProCreations/grug-35b

Base model

deepreinforce-ai/Ornith-1.0-35B

Finetuned

(13)

this model

Quantizations

1 model

ProCreations
/

grug-35b