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)

# pip install -U transformers accelerate
# 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=256)
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
```

chaski / training_args.json

betterwithage

security(model): replace pickle training arguments with JSON

8964fb9 verified about 1 month ago

Raw History Blame Contribute Delete

2.28 kB

	{
	"arguments_by_text_source": {
	"train_chaski.py": {
	"gradient_accumulation_steps": 2,
	"learning_rate": 0.0002,
	"logging_steps": 1,
	"lr_scheduler_type": "constant_with_warmup",
	"optim": "adamw_8bit",
	"output_dir": "outputs",
	"per_device_train_batch_size": 1,
	"push_to_hub": true,
	"report_to": "none",
	"warmup_steps": 6,
	"weight_decay": 0.01
	},
	"training_receipt.json": {
	"learning_rate": 0.0002,
	"lora_alpha": 32,
	"lora_r": 16,
	"lr_scheduler_type": "constant_with_warmup",
	"max_steps": 64,
	"optim": "adamw_8bit",
	"response_only_loss": true,
	"seed": 11,
	"training_rows": 45,
	"warmup_steps": 6
	}
	},
	"migration_parent_revision": "ca8e0140eb7bd1935db972fb96b3b9c60e6f37a4",
	"purpose": "non-executable record replacing serialized trainer arguments",
	"replaces": {
	"deserialized": false,
	"introduced_by_revision": "152ad88f1b44cadfd88f87e622483d964ea7c5c8",
	"path": "training_args.bin",
	"serialization": "pytorch-zip-pickle",
	"sha256": "9e7849e60a82c69bea76ec2422db2a0d97eadfb4d9a136a02ecdb0727f27a298",
	"size_bytes": 5713
	},
	"repository": "SZLHOLDINGS/chaski",
	"schema": "szl.training-arguments/v1",
	"scope": {
	"argument_completeness": "script-and-receipt-recorded-subset",
	"generated_by_deserializing_pickle": false,
	"inference_required": false,
	"resume_checkpoint": false
	},
	"sources": [
	{
	"path": "README.md",
	"revision": "152ad88f1b44cadfd88f87e622483d964ea7c5c8",
	"sha256": "b4c62c0d1b37c06da5851490917385246b4f2ef6801b4d785d22e7950beb93b0"
	},
	{
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	"revision": "152ad88f1b44cadfd88f87e622483d964ea7c5c8",
	"sha256": "23b15061629938d9d2edb1b584db62d7c9dab97d93d6de63c681d37bb66a96fe"
	},
	{
	"path": "training_receipt.json",
	"revision": "152ad88f1b44cadfd88f87e622483d964ea7c5c8",
	"sha256": "18ee0f228d35b2bf9576043d8007552924fc5c97eccc3cb541710ce86275bfdc"
	}
	],
	"training": {
	"framework_versions": {
	"datasets": "4.3.0",
	"pytorch": "2.11.0",
	"tokenizers": "0.22.2",
	"transformers": "5.5.0",
	"trl": "0.24.0"
	},
	"method": "SFT"
	}
	}