fix: map quantize_type to valid Transformers.js dtype values

--quantize none now emits dtype: "fp32" in the README instead of
dtype: "none", matching Transformers.js documented values (fp32,
fp16, q8, q4).

finetune/convert_onnx.py

@@ -321,12 +321,18 @@ def upload_to_hub(
         commit_message="Upload ONNX model",
     )
 
+    # Map quantize_type to Transformers.js dtype values
+    dtype_map = {"q4": "q4", "q8": "q8", "fp16": "fp16", "none": "fp32"}
+    tj_dtype = dtype_map.get(quantize_type, "fp32")
+    format_desc = "FP32 (no quantization)" if quantize_type == "none" else f"{quantize_type.upper()} quantization"
+    repo_name = output_repo.split("/")[-1]
+
     readme = f"""---
 base_model: {base_model}
 tags: [onnx, transformers.js, webgpu, query-expansion, qmd]
 library_name: transformers.js
 ---
-# {output_repo.split("/")[-1]}
+# {repo_name}
 
 ONNX conversion of the QMD Query Expansion model for use with
 [Transformers.js](https://huggingface.co/docs/transformers.js) and WebGPU.
@@ -336,7 +342,7 @@ ONNX conversion of the QMD Query Expansion model for use with
 - **SFT:** {sft_model}
 - **GRPO:** {grpo_model}
 - **Task:** Query expansion (lex/vec/hyde format)
-- **Format:** ONNX with {quantize_type.upper()} quantization
+- **Format:** ONNX with {format_desc}
 
 ## Usage with Transformers.js
 
@@ -345,7 +351,7 @@ import {{ AutoTokenizer, AutoModelForCausalLM }} from "@huggingface/transformers
 
 const tokenizer = await AutoTokenizer.from_pretrained("{output_repo}");
 const model = await AutoModelForCausalLM.from_pretrained("{output_repo}", {{
-  dtype: "{quantize_type}",
+  dtype: "{tj_dtype}",
   device: "webgpu",
 }});
 ```