Instructions to use SZLHOLDINGS/chaski-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SZLHOLDINGS/chaski-r2 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-0.8B")
model = PeftModel.from_pretrained(base_model, "SZLHOLDINGS/chaski-r2")

Transformers

How to use SZLHOLDINGS/chaski-r2 with Transformers:

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

pipe = pipeline("text-generation", model="SZLHOLDINGS/chaski-r2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SZLHOLDINGS/chaski-r2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SZLHOLDINGS/chaski-r2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SZLHOLDINGS/chaski-r2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SZLHOLDINGS/chaski-r2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SZLHOLDINGS/chaski-r2

SGLang

How to use SZLHOLDINGS/chaski-r2 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 "SZLHOLDINGS/chaski-r2" \
    --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": "SZLHOLDINGS/chaski-r2",
		"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 "SZLHOLDINGS/chaski-r2" \
        --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": "SZLHOLDINGS/chaski-r2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use SZLHOLDINGS/chaski-r2 with Docker Model Runner:
```
docker model run hf.co/SZLHOLDINGS/chaski-r2
```

betterwithage commited on about 22 hours ago

Commit

ee1243a

verified ·

1 Parent(s): 5d87d0f

Upload frontier_gpu_summary.json with huggingface_hub

Browse files

Files changed (1) hide show

frontier_gpu_summary.json +17 -0

frontier_gpu_summary.json ADDED Viewed

	@@ -0,0 +1,17 @@

+{
+  "kind": "szl.blackwell-frontier-gpu/v1",
+  "ra_v3": {
+    "label": "MEASURED",
+    "score": "11/12"
+  },
+  "chaski_r2": {
+    "label": "MEASURED",
+    "json_draft": "5/5",
+    "refuse": "6/6"
+  },
+  "unsloth": "2026.7.2 bf16 LoRA Qwen3.5-0.8B",
+  "newest_not_trained": "Qwen3.8-27B / GRPO-4B need more than 8GB; not this GPU",
+  "publication_eligible": false,
+  "lambda": "Conjecture 1",
+  "computed_at": "2026-08-29T14:14:23.953291+00:00"
+}