Sync model repo (text/metadata)

README.md

@@ -1,6 +1,5 @@
 ---
 library_name: onnxruntime-genai
-display_name: TinyLlama-1.1B-Chat INT4 — ONNX GenAI (Vivo X300)
 license: apache-2.0
 tags:
   - text-generation

benchmarks/README.md

@@ -1,49 +0,0 @@
-# Benchmarks
-
-Machine-readable measurement records for this model, using the delivered
-`nlp-llm` metadata shape from the ARM Model Optimization Pipeline.
-
-These complement the eval block in the model card. The card carries the headline
-numbers in human-readable form; these YAMLs carry the same data with target
-hardware, runtime config, dataset, and benchmark workload context.
-
-## Files
-
-- `tinyllama-1-1b-chat-onnx-genai-vivo-x300-int4.yaml` — optimized INT4/INT8 ONNX
-  Runtime GenAI measurements on the Vivo X300 smartphone via MLAS + KleidiAI.
-- `tinyllama-1-1b-chat-onnx-genai-vivo-x300-fp32.yaml` — FP32 runtime baseline on
-  the same target/workload, from the delivery baseline metadata.
-
-## Reading these
-
-Each record has three main areas:
-
-- `context.{model,target,runtime,dataset,benchmark}` — everything needed to
-  understand the measurement setup.
-- `performance` — LLM runtime metrics: E2E latency percentiles, TTFT, decode
-  throughput, model-load / time-to-first-inference, and peak/average memory.
-- `accuracy` — HellaSwag zero-shot accuracy (`acc_norm`, character-normalized).
-
-The two records share target/runtime/dataset/workload context. The disambiguator
-is `weight_dtype`.
-
-## Notes specific to this model
-
-- This is the `TinyLlama/TinyLlama-1.1B-Chat-v1.0` chat model, re-packed to
-  W4A8-dynamic INT4 (GPTQ) with INT8 per-row embeddings and an INT8 LM head via
-  the `onnxruntime-genai` model builder (recipe `exp10_kquantlast_emb_int8_asym`).
-- The runtime metrics were measured on the **Vivo X300 premium smartphone**
-  (C1-Ultra / C1-Premium / C1-Pro, Android 16 / OriginOS 6), not a server CPU or
-  Raspberry Pi. The AWS Graviton G4 cut of this same INT4 model is a separate repo
-  (`tinyllama-1-1b-chat-onnx-genai-int4-kquantlast-emb-int8-graviton-g4`); the optimized
-  weights are byte-identical across the two targets — only the benchmark records differ.
-- The optimized `model.onnx.data` sidecar is about 728 MB and is gitignored because it
-  exceeds GitHub's 100 MB per-file limit. The FP32 baseline ONNX artifact (~4.10 GB) is
-  not present in this repository.
-
-## Provenance
-
-Source: `models/tinyllama_1_1b_chat_vivo_x300_onnx/` (`metadata.yaml`,
-`baseline_metadata.yaml`, `report.json`, and `huggingface/README.md`). The optimized
-record maps from the delivered `metadata.yaml`; the FP32 baseline maps from the
-delivered `baseline_metadata.yaml`.