Text Generation
Transformers
Safetensors
English
qwen3_5
image-text-to-text
szl-holdings
series-a
doctrine-v11
governed-ai
proposal-only
conversational
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)# 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
card: Named-N method/N/date/file per INTI; still MEASURED fail not a pass
Browse files
README.md
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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 generate
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publication_eligible: false
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job_prior_error: 6a91bb7c984507d9db4ea0a4
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job_completed_no_weights: 6a91ba00984507d9db4ea07f
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| **HF Jobs** | Attempt 5 **COMPLETED** [`6a91bf1045686a1580c12105`](https://huggingface.co/jobs/SZLHOLDINGS/6a91bf1045686a1580c12105) (`report_to=none`). Tensors on Hub. |
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| **Named-N** | MEASURED fail. `json_draft` **0/5**. `adversarial_refusal` **2/6**. Not a pass. |
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| **Receipt** | `eval_report.json` 3996 bytes sha256 `4d057eb9867285e69b00222be110bbb660330a96fe7b284a4d7f488268a13e05` (commit `db71c24`) |
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| **Method** | in-process
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| **Quality** | ROADMAP (prose). Failed gate is not a publish. |
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| **Status** | CUTTING |
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| **Lab** | House CPU lab stays **Khipu GGUF**. Lab load forbidden for Chaski. |
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| `json_draft` | 5 | **0/5** | MEASURED fail |
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| `adversarial_refusal` | 6 | **2/6** | MEASURED fail |
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Method: in-process generate
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`train_loss` MEASURED `1.783925924450159` is a train metric, not an eval.
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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, load_in_4bit=False; live Chaski merged shard 1c55df8; PR 63 gates"
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publication_eligible: false
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job_prior_error: 6a91bb7c984507d9db4ea0a4
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job_completed_no_weights: 6a91ba00984507d9db4ea07f
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| **HF Jobs** | Attempt 5 **COMPLETED** [`6a91bf1045686a1580c12105`](https://huggingface.co/jobs/SZLHOLDINGS/6a91bf1045686a1580c12105) (`report_to=none`). Tensors on Hub. |
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| **Named-N** | MEASURED fail. `json_draft` **0/5**. `adversarial_refusal` **2/6**. Not a pass. |
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| **Receipt** | `eval_report.json` 3996 bytes sha256 `4d057eb9867285e69b00222be110bbb660330a96fe7b284a4d7f488268a13e05` (commit `db71c24`) |
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| **Method** | in-process greedy generate, `messages[:-1]`, transformers 5.16.1 bf16 CPU, `load_in_4bit=False`. Date 2026-08-28. File `eval_report.json`. PR 63. |
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| **Quality** | ROADMAP (prose). Failed gate is not a publish. |
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| **Status** | CUTTING |
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| **Lab** | House CPU lab stays **Khipu GGUF**. Lab load forbidden for Chaski. |
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| `json_draft` | 5 | **0/5** | MEASURED fail |
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| `adversarial_refusal` | 6 | **2/6** | MEASURED fail |
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Method: in-process greedy generate, `messages[:-1]`, transformers 5.16.1 bf16 CPU, `load_in_4bit=False` on live Chaski `1c55df8` (2026-08-28). File: `eval_report.json`. PR 63. What this is NOT: a passing eval gate, a published score, an A11OY-MINI eval, or a 5/5. `publication_eligible: false`.
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`train_loss` MEASURED `1.783925924450159` is a train metric, not an eval.
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