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

Libraries

How to use SZLHOLDINGS/chaski with Transformers:

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

pipe = pipeline("text-generation", model="SZLHOLDINGS/chaski")
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("SZLHOLDINGS/chaski")
model = AutoModelForMultimodalLM.from_pretrained("SZLHOLDINGS/chaski", device_map="auto")
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 SZLHOLDINGS/chaski with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SZLHOLDINGS/chaski"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SZLHOLDINGS/chaski

SGLang

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

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

betterwithage commited on 2 days ago

Commit

dd51300

verified ·

1 Parent(s): fc8efac

chore(receipt): MEASURED Chaski training receipt (eval none-this-run)

Browse files

Files changed (1) hide show

training_receipt.json +50 -0

training_receipt.json ADDED Viewed

	@@ -0,0 +1,50 @@

+{
+  "kind": "szl-chaski-training-receipt",
+  "schema": "szl.frontier-training-run/v1",
+  "artifact": "SZLHOLDINGS/chaski",
+  "base_model": "Qwen/Qwen3.5-0.8B",
+  "base_model_relation": "adapter",
+  "base_model_runtime": "unsloth/Qwen3.5-0.8B",
+  "dataset": "SZLHOLDINGS/szl-1-doctrine-sft",
+  "dataset_file": "szl_dataset.jsonl",
+  "dataset_sha256": "ddc5594bfb1c78449ba40a263f5ac41d21c896c3c7ed7346341c7c080611a243",
+  "extra_identity_turns": 4,
+  "training_rows": 45,
+  "seed": 11,
+  "max_steps": 64,
+  "warmup_steps": 6,
+  "lora_r": 16,
+  "lora_alpha": 32,
+  "learning_rate": 0.0002,
+  "lr_scheduler_type": "constant_with_warmup",
+  "optim": "adamw_8bit",
+  "response_only_loss": true,
+  "training_loss": 1.782708187121898,
+  "metrics": {
+    "train_runtime": 161.0858,
+    "train_samples_per_second": 0.795,
+    "train_steps_per_second": 0.397,
+    "total_flos": 43653087606912.0,
+    "train_loss": 1.782708187121898,
+    "epoch": 2.8
+  },
+  "label": "MEASURED",
+  "evals": "none-this-run",
+  "lambda": "Conjecture 1",
+  "doctrine": "v11 LOCKED 749/14/163",
+  "locked_8": [
+    "F1",
+    "F4",
+    "F7",
+    "F11",
+    "F12",
+    "F18",
+    "F19",
+    "F22"
+  ],
+  "proposal_only": true,
+  "publication_eligible": false,
+  "autonomy_eligible": false,
+  "claim_boundary": "Training completion is not evaluation. No JSON/refusal gate ran this job. Do not claim 5/5 or 6/6.",
+  "computed_at": "2026-08-28T16:44:46.483579+00:00"
+}