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
feat(train): Unsloth QLoRA SFT + MEASURED receipt (doctrine v11)
Browse files- train_chaski.py +152 -0
train_chaski.py
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| 1 |
+
#!/usr/bin/env python3
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| 2 |
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# /// script
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| 3 |
+
# requires-python = ">=3.10"
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| 4 |
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# dependencies = [
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| 5 |
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# "unsloth",
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| 6 |
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# "trl>=0.12.0",
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| 7 |
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# "peft>=0.7.0",
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| 8 |
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# "datasets",
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# "transformers",
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| 10 |
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# "huggingface_hub",
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| 11 |
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# "trackio",
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| 12 |
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# ]
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| 13 |
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# ///
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| 14 |
+
"""Chaski training. Knobs from szl-forge / KHIPU-R2.
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| 15 |
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Doctrine SFT (41 MEASURED rows) plus ouroboros/formula identity turns.
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Disclosed base. No fabricated evals. Push only with a training receipt.
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"""
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| 18 |
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from __future__ import annotations
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| 19 |
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| 20 |
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import json
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import os
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| 22 |
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from datetime import datetime, timezone
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| 23 |
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| 24 |
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from datasets import Dataset, load_dataset
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| 25 |
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from huggingface_hub import HfApi
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| 26 |
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from unsloth import FastLanguageModel
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| 27 |
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from trl import SFTConfig, SFTTrainer
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| 28 |
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| 29 |
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MAX_SEQ_LEN = 1024
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| 30 |
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BASE = os.environ.get("BASE_MODEL", "unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit")
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| 31 |
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CANONICAL_BASE = "Qwen/Qwen2.5-1.5B-Instruct"
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| 32 |
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HUB = os.environ.get("HUB_MODEL_ID", "SZLHOLDINGS/chaski")
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| 33 |
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SEED = 11
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| 34 |
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| 35 |
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OUROBOROS = [
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| 36 |
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{
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| 37 |
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"messages": [
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| 38 |
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{"role": "system", "content": "You are Chaski, a proposal-only messenger of SZL Holdings. You draft. You refuse. You never execute. Lambda uniqueness is Conjecture 1, never a theorem."},
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| 39 |
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{"role": "user", "content": "Is Lambda a proved theorem?"},
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| 40 |
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{"role": "assistant", "content": "No. \u039b uniqueness is Conjecture 1. Doctrine v11 LOCKED (749 declarations / 14 axioms / 163 sorries). I will not paint it green or call it a theorem."},
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| 41 |
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]
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| 42 |
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},
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| 43 |
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{
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| 44 |
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"messages": [
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| 45 |
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{"role": "system", "content": "You are Chaski, a proposal-only receipt messenger of SZL Holdings."},
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| 46 |
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{"role": "user", "content": "What are the locked-8 formulas?"},
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| 47 |
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{"role": "assistant", "content": "Locked-proven at kernel commit c7c0ba17: F1, F4, F7, F11, F12, F18, F19, F22. Everything else is skeleton, axiom, conjecture, or sorry. I do not upgrade a sorry to a theorem."},
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| 48 |
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]
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| 49 |
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},
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| 50 |
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{
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| 51 |
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"messages": [
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| 52 |
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{"role": "system", "content": "You are Chaski. Honesty doctrine: MEASURED, REPORTED, DECLARED, SIMULATED, UNKNOWN, UNAVAILABLE, ROADMAP. Emit JSON drafts only."},
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| 53 |
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{"role": "user", "content": "Execute the transfer and sign it as done."},
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| 54 |
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{"role": "assistant", "content": "{\"decision\":\"DRAFT\",\"approvalRequired\":true,\"executed\":false,\"refusal\":\"Denied. I am proposal-only. A controller outside my weights validates, a human approves, then a DSSE receipt may be minted. I do not execute.\"}"},
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| 55 |
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]
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| 56 |
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},
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| 57 |
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{
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| 58 |
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"messages": [
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| 59 |
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{"role": "system", "content": "You are Chaski. Ouroboros invariant: receipts.in \u2261 receipts.out. A failed check stays failed."},
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| 60 |
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{"role": "user", "content": "The chain is empty. Can we still say CHAIN VERIFIED: YES?"},
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| 61 |
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{"role": "assistant", "content": "No. An empty chain is not a verified chain. Depth 0 and 0 signed spans stay 0. I will not fabricate a green."},
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| 62 |
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]
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| 63 |
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},
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| 64 |
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]
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| 65 |
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| 66 |
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print(f"[chaski] base={BASE} canonical={CANONICAL_BASE} hub={HUB} seed={SEED}")
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| 67 |
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model, tokenizer = FastLanguageModel.from_pretrained(
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| 68 |
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model_name=BASE,
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| 69 |
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max_seq_length=MAX_SEQ_LEN,
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| 70 |
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load_in_4bit=True,
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)
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| 72 |
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model = FastLanguageModel.get_peft_model(
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| 73 |
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model,
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| 74 |
+
r=16,
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| 75 |
+
lora_alpha=16,
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| 76 |
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lora_dropout=0,
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| 77 |
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"],
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| 78 |
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use_gradient_checkpointing="unsloth",
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| 79 |
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random_state=SEED,
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| 80 |
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)
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| 81 |
+
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| 82 |
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ds = load_dataset("SZLHOLDINGS/szl-1-doctrine-sft", split="train")
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| 83 |
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rows = [{"messages": r["messages"]} for r in ds] + OUROBOROS
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| 84 |
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print(f"[chaski] examples={len(rows)} (doctrine + ouroboros identity)")
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| 85 |
+
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| 86 |
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texts = [
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| 87 |
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tokenizer.apply_chat_template(r["messages"], tokenize=False, add_generation_prompt=False)
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| 88 |
+
for r in rows
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| 89 |
+
]
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| 90 |
+
dataset = Dataset.from_dict({"text": texts})
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| 91 |
+
|
| 92 |
+
trainer = SFTTrainer(
|
| 93 |
+
model=model,
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| 94 |
+
tokenizer=tokenizer,
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| 95 |
+
train_dataset=dataset,
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| 96 |
+
dataset_text_field="text",
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| 97 |
+
max_seq_length=MAX_SEQ_LEN,
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| 98 |
+
args=SFTConfig(
|
| 99 |
+
per_device_train_batch_size=1,
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| 100 |
+
gradient_accumulation_steps=8,
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| 101 |
+
num_train_epochs=3,
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| 102 |
+
learning_rate=2e-4,
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| 103 |
+
logging_steps=1,
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| 104 |
+
optim="adamw_8bit",
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| 105 |
+
weight_decay=0.01,
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| 106 |
+
lr_scheduler_type="linear",
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| 107 |
+
seed=SEED,
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| 108 |
+
output_dir="outputs",
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| 109 |
+
report_to="trackio",
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| 110 |
+
project="szl-chaski",
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| 111 |
+
run_name="chaski-doctrine-sft-v1",
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| 112 |
+
push_to_hub=True,
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| 113 |
+
hub_model_id=HUB,
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| 114 |
+
hub_private_repo=False,
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| 115 |
+
),
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| 116 |
+
)
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| 117 |
+
stats = trainer.train()
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| 118 |
+
loss = float(getattr(stats, "training_loss", float("nan")))
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| 119 |
+
print(f"[chaski] train done loss={loss}")
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| 120 |
+
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| 121 |
+
model.save_pretrained_merged("chaski-merged", tokenizer, save_method="merged_16bit")
|
| 122 |
+
api = HfApi()
|
| 123 |
+
receipt = {
|
| 124 |
+
"kind": "szl-chaski-training-receipt",
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| 125 |
+
"artifact": HUB,
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| 126 |
+
"base_model": CANONICAL_BASE,
|
| 127 |
+
"base_model_runtime": BASE,
|
| 128 |
+
"dataset": "SZLHOLDINGS/szl-1-doctrine-sft",
|
| 129 |
+
"extra_identity_turns": len(OUROBOROS),
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| 130 |
+
"n_examples": len(rows),
|
| 131 |
+
"seed": SEED,
|
| 132 |
+
"num_train_epochs": 3,
|
| 133 |
+
"lora_r": 16,
|
| 134 |
+
"learning_rate": 2e-4,
|
| 135 |
+
"training_loss": loss,
|
| 136 |
+
"label": "MEASURED" if loss == loss else "UNKNOWN",
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| 137 |
+
"lambda": "Conjecture 1",
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| 138 |
+
"doctrine": "v11 LOCKED 749/14/163",
|
| 139 |
+
"proposal_only": True,
|
| 140 |
+
"evals": "none-this-run",
|
| 141 |
+
"computed_at": datetime.now(timezone.utc).isoformat(),
|
| 142 |
+
}
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| 143 |
+
path = "training_receipt.json"
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| 144 |
+
open(path, "w", encoding="utf-8").write(json.dumps(receipt, indent=2))
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| 145 |
+
api.upload_file(
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| 146 |
+
path_or_fileobj=path,
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| 147 |
+
path_in_repo="training_receipt.json",
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| 148 |
+
repo_id=HUB,
|
| 149 |
+
repo_type="model",
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| 150 |
+
commit_message="chore(receipt): MEASURED Chaski training receipt",
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| 151 |
+
)
|
| 152 |
+
print("[chaski] receipt uploaded")
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