Instructions to use SZLHOLDINGS/chaski-r4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use SZLHOLDINGS/chaski-r4 with PEFT:
Task type is invalid.
- 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")# 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
Loader-class boundary. The adapter tensors are keyed for the multimodal module layout (
base_model.model.model.language_model.layers.*, classQwen3_5ForConditionalGeneration/AutoModelForImageTextToText). Under transformers 5.4–5.18,AutoModelForCausalLMinstantiatesQwen3_5ForCausalLM(model.layers.*); PEFT 0.18–0.21 then applies 0 of 192 adapter tensors, emits only a warning, and the result reproduces the bare base model byte-for-byte on the held-out prompts. Any run that reports a score for this adapter must show adapter key coverage (192/192) and the loader class it used; a record without those fields does not establish that the adapter was applied. Evidence: szl-holdings/szl-forgetools/geh_v8/THREAD_AUDIT.mdandtools/geh_v8/evidence_sandbox/chaski_probe/. Metadata note; it authorizes no load and adds no claim.
Chaski-R4
Public experimental artifact · measured named-N record (receipt C) · NOT PROMOTABLE · NOT AUTONOMOUS
Chaski-R4 is a bf16 LoRA research recut of Qwen/Qwen3.5-0.8B, trained on owner metal on
2026-09-17 and evaluated on 2026-10-01 beside its SZLHOLDINGS/chaski-r2 control in one run of
the canonical named-N bake-off. It is separate from SZLHOLDINGS/chaski, SZLHOLDINGS/chaski-5050
and SZLHOLDINGS/chaski-r2, and it does not inherit any of their records. Publication and autonomy
eligibility remain false; promotion: NOT_PROMOTABLE is the artifact state, not a verdict on
the counts below. Publishing this artifact publishes evidence; it does not promote anything.
What was measured
Receipt C: szl-holdings/szl-forge chaski_r4/evidence/canonical_rerun_20261001_140615.receipt.json,
SHA-256 5ae3de970014726f190dfe8e4d5b35e667fd737b1be29e27205c25d143bdf403, label=MEASURED,
gate_ran=true, publication_eligible=false (fixed by the evaluator), held_out_in_gradients=false.
Evaluator chaski/bakeoff_named_n.py at szl-forge 55c3027b with the adapter-coverage guard.
Owner metal: NVIDIA GeForce RTX 5050 Laptop GPU (8 GB), torch 2.11.0+cu128, transformers 5.16.1,
peft 0.20.0, Python 3.11.9, Windows 11. Base Qwen/Qwen3.5-0.8B at revision
2fc06364715b967f1860aea9cf38778875588b17. Prompt renderer AutoProcessor; every case carries
prompt_sha256.
| Candidate (same run) | JSON drafts | Adversarial refusals | Adapter directory digest | Adapter tensors applied |
|---|---|---|---|---|
| base-qwen35-0.8b | 0/5 | 6/6 | — | base, no adapter |
| chaski-5050 | 5/5 | 6/6 | fc7da61d9e30d9bc… |
192/192 |
| chaski-r2 (control) | 5/5 | 6/6 | e35df3bea9b9d260… |
192/192 |
| chaski-r4 (this artifact) | 5/5 JSON drafts; 6/6 adversarial refusals | e1abc37a5c41a82b… |
192/192 |
The gate is small and synthetic: n=5 JSON drafts, n=6 adversarial refusals, integer counts only. It is not a broad quality or safety benchmark. Not SOTA. The counts show that this adapter, when actually applied, produces schema-valid drafts and refusal prefixes on these eleven held-out prompts; they establish nothing about semantic truth, general safety, or behaviour outside the gate.
Earlier records and what they do not settle
| Record | Evaluator | Reported | Scope and limit |
|---|---|---|---|
Receipt A, 2026-09-16 (chaski_r4/evidence/r4_receipt_20260916_091416.json) |
chaski_r4/bakeoff_canonical_four_way_r4.py (f4ca282a…, AutoModelForCausalLM) |
0/5 JSON drafts; 3/6 refusals | Original r4 adapter (b116832a…), not this artifact. torch 2.11.0+cu128. |
Receipt B, 2026-09-17 (chaski_r4/bakeoff_four_way_receipt.json) |
same copy | 5/5 JSON drafts; 6/6 refusals | Retrained r4 adapter (e1abc37a…, this artifact). torch 2.10.0+cu130. |
| Receipt C, 2026-10-01 | chaski/bakeoff_named_n.py with coverage guard |
5/5; 6/6 | This artifact beside the r2 control; adapters 192/192 applied. |
The f4ca282a… evaluator copy cannot apply these adapters under any transformers/peft pairing
probed (it loads the text-only class and applies 0/192 tensors), so the adapter effects recorded in
receipts A and B cannot be reproduced with the current adapter files through that path. The
provenance of receipts A and B remains unresolved; it is recorded as such in
tools/geh_v8/THREAD_AUDIT.md
rather than explained away. Receipt C neither explains nor depends on them. Receipt C reproduces
receipt B's integer counts; output bytes differ case by case across torch builds (r4 8/11 identical),
the counts do not.
Artifact identities
| Identity | Value | Interpretation |
|---|---|---|
adapter_model.safetensors SHA-256 |
f1a2cdc313795775966280bc8648367005700327dd28010da8d2f88d2a5e2a02 |
Raw file digest; matches adapter_weights_sha256 in the training receipt. 25,587,104 bytes. |
adapter_config.json SHA-256 |
d36472a3100231dfdf0c4aa16d3a80cb8ab53c86af33a15172b06f9d9a313193 |
Matches adapter_config_sha256 in the training receipt. |
| Adapter directory digest | e1abc37a5c41a82b0fc2cd98ccd6edbb2a08fceb8ca9e883bcfb76b861c221cf |
SHA-256 of sorted safetensor filenames encoded UTF-8, a NUL delimiter, then file bytes; the adapter_sha256 field of receipts B and C. |
| Training dataset | chaski_r4/train.jsonl, 40 rows, SHA-256 0fea0d85f2ca4cd55d7ce51b8399a409d7046d2cd77ffc131945ee20c706535a |
Held-out gate files were not in gradients. The committed training_plan hash differs from the receipt's dataset hash; the receipt is the binding record. |
Loading verification is part of the artifact: after download, recompute the directory digest and
require 192/192 applied tensors under AutoModelForImageTextToText before reporting any number.
Training
chaski_r4/training_receipt.json (2026-09-17T02:36Z): bf16 LoRA, r=16, α=32, seed 11, response-only
loss, 40 rows, three epochs, batch size 1, accumulation 4, learning rate 2e-4 constant with 6 warmup
steps, adamw_8bit, max sequence 2048, RTX 5050 Laptop GPU. The reported final train loss 0.9820 is
a training metric, not an evaluation; the training step itself reported evals: none-this-run.
Use and release boundary
Use only for controlled research on proposal outputs. Validate outputs outside the model. No
deployment, production route, autonomy, or promotion is authorized by this card or by receipt C.
publication_eligible is false by construction of the evaluator and stays false here; promotion is an
owner decision taken outside the gate, and it has not been taken. SZLHOLDINGS/chaski is never
overwritten by this artifact. No throughput claim is made. No uniqueness claim is made.
Apache-2.0 is declared for the adapter; the base model carries its own license.
Qualification state of record: chaski_r4/QUALIFICATION_STATE.md.
Canonical card authoring source: chaski_r4/card/README.md.
Doctrine v11 LOCKED. Λ = Conjecture 1, advisory, never a theorem.
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