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 5 days ago

Commit

549e19f

verified ·

1 Parent(s): d3e2536

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

Browse files

Files changed (1) hide show

training_receipt.json +6 -6

training_receipt.json CHANGED Viewed

@@ -19,13 +19,13 @@
   "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",
@@ -46,5 +46,5 @@
   "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"
 }

   "lr_scheduler_type": "constant_with_warmup",
   "optim": "adamw_8bit",
   "response_only_loss": true,
+  "training_loss": 1.783925924450159,
   "metrics": {
+    "train_runtime": 161.6233,
+    "train_samples_per_second": 0.792,
+    "train_steps_per_second": 0.396,
     "total_flos": 43653087606912.0,
+    "train_loss": 1.783925924450159,
     "epoch": 2.8
   },
   "label": "MEASURED",
   "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": "2026-08-28T17:06:39.320753+00:00"
 }