Instructions to use SZLHOLDINGS/chaski with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
fix: this is a merged finetune, not an adapter — plus unambiguous loading guidance
Browse filesThe card declared base_model_relation: adapter and artifact_class: ADAPTER while also declaring weights: AVAILABLE and shipping a 1.75 GB merged shard; its own originality field already said FINETUNE_DISCLOSED_BASE. Corrected to finetune / MERGED_FINETUNE. Adds a loading section because three loadable artifacts co-exist here: config.json and adapter_config.json both sit at the repo root so a bare from_pretrained is ambiguous with peft installed, the merged shard uses the non-standard filename model.safetensors-00001-of-00001.safetensors which conventional globs miss, and config.json carries a vision_config whose tower loads even for text-only use. No eval, weight or receipt claim was touched; the MEASURED failing gate stands unchanged.
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thumbnail: https://huggingface.co/SZLHOLDINGS/chaski/resolve/main/og-card.png
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license: apache-2.0
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language:
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-
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pipeline_tag: text-generation
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library_name: transformers
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base_model: Qwen/Qwen3.5-0.8B
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base_model_relation:
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tags:
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szl:
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doctrine: v11-LOCKED
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lean:
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lambda:
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evidence_ceiling: 0.97
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artifact_class:
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originality: FINETUNE_DISCLOSED_BASE
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collection:
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jobs: COMPLETED
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job_id: 6a91bf1045686a1580c12105
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job_namespace: SZLHOLDINGS
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evals: MEASURED
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named_n:
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revision: 1c55df8652e9d0f7b84356b1e2d54849165ae884
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date:
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json_draft:
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adversarial_refusal:
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label: MEASURED
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gate: fail
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report: eval_report.json
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report_sha256: 4d057eb9867285e69b00222be110bbb660330a96fe7b284a4d7f488268a13e05
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report_bytes: 3996
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report_commit: db71c243d0176bccff1ff087cd4dd57663bd6502
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method:
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publication_eligible: false
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gpu: UNAVAILABLE
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job_prior_error: 6a91bb7c984507d9db4ea0a4
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job_completed_no_weights: 6a91ba00984507d9db4ea07f
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job_prior_failed:
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---
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# Chaski
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<p align="center">
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thumbnail: https://huggingface.co/SZLHOLDINGS/chaski/resolve/main/og-card.png
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license: apache-2.0
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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base_model: Qwen/Qwen3.5-0.8B
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base_model_relation: finetune
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tags:
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- szl-holdings
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- series-a
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- doctrine-v11
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- governed-ai
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- proposal-only
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szl:
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doctrine: v11-LOCKED
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lean: 749/14/163
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lambda: Conjecture 1 — advisory, never a theorem
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evidence_ceiling: 0.97
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artifact_class: MERGED_FINETUNE
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originality: FINETUNE_DISCLOSED_BASE
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collection: SZL Fall 2026 — Original Cuts
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jobs: COMPLETED
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job_id: 6a91bf1045686a1580c12105
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job_namespace: SZLHOLDINGS
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evals: MEASURED
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named_n:
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revision: 1c55df8652e9d0f7b84356b1e2d54849165ae884
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date: '2026-08-28'
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json_draft: 0/5
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adversarial_refusal: 2/6
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label: MEASURED
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gate: fail
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report: eval_report.json
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report_sha256: 4d057eb9867285e69b00222be110bbb660330a96fe7b284a4d7f488268a13e05
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report_bytes: 3996
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report_commit: db71c243d0176bccff1ff087cd4dd57663bd6502
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method: in-process greedy generate, messages[:-1], transformers 5.16.1 bf16 CPU,
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load_in_4bit=False; live Chaski merged shard 1c55df8; PR 63 gates
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publication_eligible: false
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gpu: UNAVAILABLE
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job_prior_error: 6a91bb7c984507d9db4ea0a4
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job_completed_no_weights: 6a91ba00984507d9db4ea07f
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job_prior_failed:
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- 6a91b8ba984507d9db4ea071
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- 6a91b990984507d9db4ea077
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- 6a91bb7c984507d9db4ea0a4
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---
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> **Metadata correction 2026-08-30.** This repo declared
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> `base_model_relation: adapter` and `artifact_class: ADAPTER` while also declaring
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> `weights: AVAILABLE` and shipping a 1.75 GB merged shard. Relative to
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> `Qwen/Qwen3.5-0.8B` this is a **finetune**, not an adapter — corrected to
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> `base_model_relation: finetune` / `artifact_class: MERGED_FINETUNE`. The card's own
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> `originality: FINETUNE_DISCLOSED_BASE` already said so. No eval, weight or receipt
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> claim is touched; the MEASURED failing gate below stands exactly as it was.
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## Loading this repo unambiguously
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Three loadable artifacts sit in this repository at once, which makes a bare
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`from_pretrained` on the repo id ambiguous. Be explicit about which one you want.
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| Want | Load |
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|---|---|
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| the merged finetune | `AutoModelForCausalLM.from_pretrained("SZLHOLDINGS/chaski")` — resolves the merged shard via `model.safetensors.index.json` |
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| the LoRA adapter on the stock base | base `Qwen/Qwen3.5-0.8B` + `PeftModel.from_pretrained(base, "SZLHOLDINGS/chaski")` |
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| the Unsloth-base adapter | base `unsloth/...-bnb-4bit` + PEFT pointed at the `adapter-unsloth` subfolder |
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Two sharp edges worth knowing before you debug them:
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- **Both `config.json` and `adapter_config.json` live at the repo root.** With `peft`
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installed, some loader paths will follow the adapter rather than the merged weights.
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Pass the path you mean rather than relying on precedence.
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- **The merged shard has a non-standard filename**,
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`model.safetensors-00001-of-00001.safetensors` instead of the conventional
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`model-00001-of-00001.safetensors`. It resolves correctly through the index file, but
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tooling that globs for the conventional pattern will not find it.
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- `config.json` is `Qwen3_5ForConditionalGeneration` and carries a **`vision_config`**;
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visual weights are present in the index. If you only want text, that tower still loads.
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# Chaski
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<p align="center">
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