v0.45.0

See https://github.com/quic/ai-hub-models/releases/v0.45.0 for changelog.

README.md

@@ -31,18 +31,6 @@ This model is an implementation of Qwen2.5-1.5B-Instruct found [here](https://gi
   - Input sequence length for Prompt Processor: 128
   - Context length: 4096
   - Precision: w4 + w8 (few layers) with fp16 activations
-  - Num of key-value heads: 4
-  - Information about the model parts: Prompt Processor and Token Generator are split into 6 parts each. Each corresponding Prompt Processor and Token Generator part share weights.
-  - Prompt processor input (part1): 128 tokens
-  - Prompt processor output (part1): Embeddings output
-  - Prompt processor input (other parts): 128 tokens + KVCache initialized with pad token
-  - Prompt processor output (other parts): 128 output tokens + KVCache for token generator
-  - Token generator input (part1): 128 tokens
-  - Token generator output (part1): Embeddings output
-  - Token generator input (other parts): 1 input token + past KVCache
-  - Token generator output (other parts): 1 output token + KVCache for next iteration
-  - Use: Initiate conversation with prompt-processor and then token generator for subsequent iterations.
-  - Minimum QNN SDK version required: 2.27.7
   - Supported languages: Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
   - TTFT: Time To First Token is the time it takes to generate the first response token. This is expressed as a range because it varies based on the length of the prompt. The lower bound is for a short prompt (up to 128 tokens, i.e., one iteration of the prompt processor) and the upper bound is for a prompt using the full context length (4096 tokens).
   - Response Rate: Rate of response generation after the first response token.