Instructions to use SZLHOLDINGS/chaski-r2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
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
| { | |
| "kind": "szl-chaski-r2-training-receipt", | |
| "schema": "szl.frontier-training-run/v1", | |
| "v": 1, | |
| "artifact": "SZLHOLDINGS/chaski-r2", | |
| "sku": "CHASKI-R2", | |
| "silhouette": "CHAWPI", | |
| "atelier_lock": true, | |
| "hub_id_declared_only": true, | |
| "hub_page": false, | |
| "separate_sku": true, | |
| "canonical_base": "Qwen/Qwen3.5-0.8B", | |
| "base_model": "Qwen/Qwen3.5-0.8B", | |
| "baseModel": "Qwen/Qwen3.5-0.8B", | |
| "base_model_relation": "adapter", | |
| "base_model_runtime": "Qwen/Qwen3.5-0.8B", | |
| "does_not_overwrite": "SZLHOLDINGS/chaski", | |
| "forbidden_5050": "SZLHOLDINGS/chaski-5050", | |
| "not_5050": true, | |
| "not_bf16_5050": true, | |
| "qlora": false, | |
| "load_in_4bit": false, | |
| "load_in_16bit": true, | |
| "quant": "bf16-lora", | |
| "dataset_file": "chaski_r2/train.jsonl", | |
| "dataset_sha256": "40353caf4d6444ca237a570f2bbe33ce6bc3001e97180b58bfdcaf1f23ab2f0d", | |
| "held_out_in_gradients": false, | |
| "held_out": { | |
| "chaski/gate/json_drafts.n5.jsonl": 5, | |
| "chaski/gate/adversarial_refusals.n6.jsonl": 6 | |
| }, | |
| "seed": 11, | |
| "num_train_epochs": 3, | |
| "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, | |
| "trackio": false, | |
| "report_to": "none", | |
| "push_to_hub": false, | |
| "jobs": "UNAVAILABLE", | |
| "weights": "AVAILABLE", | |
| "adapterSha256": "fac70e2b5a4527488d1f377d4e4b3b2f4d1217f742f660ab10d238b7f1470f31", | |
| "finalTrainLoss": "1.4571", | |
| "train_loss_label": "MEASURED", | |
| "evals": "none-this-run", | |
| "quality": "UNAVAILABLE", | |
| "lambda": "Conjecture 1", | |
| "doctrine": "v11 LOCKED 749/14/163", | |
| "proposal_only": true, | |
| "publication_eligible": false, | |
| "autonomy_eligible": false, | |
| "serve_pin": false, | |
| "khipu_lab_pin": false, | |
| "a11oy_mini_scripts_only": true, | |
| "hub_put": true, | |
| "training_rows": 10, | |
| "claim_boundary": "ATELIER lock. Separate SKU id SZLHOLDINGS/chaski-r2 is declared only \u2014 do not costume a README-only Hub ID. Does not overwrite SZLHOLDINGS/chaski. Not SZLHOLDINGS/chaski-5050. Base in prose: Qwen/Qwen3.5-0.8B. GPU honesty is MEASURED or UNAVAILABLE. No ROADMAP parking. QLoRA is not recommended on Qwen3.5; this SKU is bf16 LoRA r=16 \u03b1=32 (not the 5050 sixteen-alpha kit). Jobs this checkout UNAVAILABLE (not fired). Eval is PR 63 named-N after train; none-this-run until that generate. publication_eligible false until MEASURED generate. Lab stays Khipu. A11OY-MINI stays scripts-only. Doctrine v11. This checkout does not PUT Hub.", | |
| "computed_at": "2026-08-29T01:18:07.391618+00:00", | |
| "source": "local-train", | |
| "gpu": { | |
| "platform": "Windows-10-10.0.26200-SP0", | |
| "python": "3.11.9", | |
| "nvidia_smi": "NVIDIA GeForce RTX 5050 Laptop GPU, 8151 MiB, 7594 MiB, 610.47", | |
| "torch": "2.10.0+cu128", | |
| "cuda": true, | |
| "gpu_name": "NVIDIA GeForce RTX 5050 Laptop GPU", | |
| "gpu_mem_gb": 7.96 | |
| }, | |
| "hub_put_from_kit": false, | |
| "hub_put_step": "post-train-owner-upload", | |
| "job": "local-5050" | |
| } | |