Instructions to use SZLHOLDINGS/chaski-r4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SZLHOLDINGS/chaski-r4 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-r4")

Transformers

How to use SZLHOLDINGS/chaski-r4 with Transformers:

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

pipe = pipeline("text-generation", model="SZLHOLDINGS/chaski-r4")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SZLHOLDINGS/chaski-r4", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use SZLHOLDINGS/chaski-r4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SZLHOLDINGS/chaski-r4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SZLHOLDINGS/chaski-r4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/SZLHOLDINGS/chaski-r4

SGLang

How to use SZLHOLDINGS/chaski-r4 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-r4" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SZLHOLDINGS/chaski-r4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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-r4" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SZLHOLDINGS/chaski-r4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use SZLHOLDINGS/chaski-r4 with Docker Model Runner:
```
docker model run hf.co/SZLHOLDINGS/chaski-r4
```

chaski-r4

Commit History

Oct 13 15 days ago

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docs: publish Chaski-R4 card from szl-forge@87c94cf421ab875afc5a963479c4abedbc6fdb26⁠

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Oct 1 27 days ago

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initial commit

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Commit History

Oct 13 15 days ago

docs: publish Chaski-R4 card from szl-forge@87c94cf421ab875afc5a963479c4abedbc6fdb26⁠

docs: publish Chaski-R4 card from szl-forge@cd66836ac2df83ecbebb101d40047a63ad3de463⁠

Oct 8 20 days ago

docs: publish Chaski-R4 card from szl-forge@156b8e9dc63948d9f079f62c92837acb49983b3b⁠

Oct 4 24 days ago

docs: publish Chaski-R4 card from szl-forge@5daef16de6e0cfb55daf0ff86ba3281bac1fc821⁠

Oct 3 25 days ago

docs: publish Chaski-R4 card from szl-forge@0560b5055f981141d7ef0841298e864a44348714⁠

chaski-r4: reconcile source-bound qualification sidecar (NOT_PROMOTABLE)⁠

Oct 1 27 days ago

docs: publish Chaski-R4 card from szl-forge@614ffdf0d788978da1bdb44daf585fcb642c0271⁠

chaski-r4: adapter bytes + training receipt + receipt C (PUBLIC_EXPERIMENTAL_ARTIFACT; NOT PROMOTABLE)⁠

docs: publish Chaski-R4 card from szl-forge@edfac67c43138c5e8c5ccf90fe007658718569eb⁠

initial commit