A corrected version of yunmorning/broken-model.

config.json declares

architectures: ["Qwen3ForCausalLM"]
model_type:    "qwen3"

Every architectural field matches the official Qwen/Qwen3-8B.

The original README.md declared base_model: meta-llama/Meta-Llama-3.1-8B, which does not match any other file in the repository

A /chat/completions request comes in as a structured messages array, but a causal LLM only consumes a single token sequence. The chat template bridges the two by rendering the messages into the role-marked format the model was trained on. For Qwen3 this is <|im_start|>{role}\n{content}<|im_end|>\n, terminated by <|im_start|>assistant\n to cue generation.

The inference engine calls tokenizer.apply_chat_template(messages, …) on every request. With the field absent the call raises and the request fails before any forward pass.

chat_template was the only field missing in tokenizer_config.json (5,374 B vs 9,732 B in the official Qwen3-8B). Its value was copied verbatim from Qwen/Qwen3-8B.

Wrote validate_fix.py and ran it to validate. It loads the patched tokenizer and calls apply_chat_template() on a conversation

The script produced the expected Qwen3 chat format

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
What is 2 + 2?<|im_end|>
<|im_start|>assistant

Changed base_model from meta-llama/Meta-Llama-3.1-8B to Qwen/Qwen3-8B to match the actual architecture declared in config.json and the weight structure in the safetensors files.