release-v1.1.2

• Fix inference error in chatglm3 model
• Fix inference issue with embedding input
• Support exporting llm model in MiniCPMV

release-v1.1.1

• Fixed the inference error in the minicpm3 mode
• Fixed the runtime error in rkllm_server_demo.
• Added the rkllm-toolkit installation package for Python 3.10.
• Supported gguf model conversion when tie_word_embeddings is set to true.

release-v1.1.0

• Added support for grouped quantization (w4a16 group sizes of 32/64/128, w8a8 group sizes of 128/256/512).
• Added gdq algorithm to improve 4-bit quantization accuracy.
• Added hybrid quantization algorithm, supporting a combination of grouped and non-grouped quantization based on specified ratios.
• Added support for Llama3, Gemma2, and Minicpm3 models.
• Added support for gguf model conversion (currently supports q4_0 and fp16 only).
• Added support for LoRa models.
• Added storage and loading of prompt cache
• Added PC-side emulation accuracy testing and inference interface support for rkllm-toolkit.
• Fixed catastrophic forgetting issue when the token count exceeds max_context.
• Optimized prefill speed.
• Optimized generate speed.
• Optimized model initialization time
• Added support for four input interfaces: prompt, embedding, token, and multimodal.

release-v1.0.1

• Optimize model conversion memory occupation
• Optimize inference memory occupation
• Increase prefill speed
• Reduce initialization time
• Improve quantization accuracy
• Add support for Gemma, ChatGLM3, MiniCPM, InternLM2, and Phi-3
• Add Server invocation
• Add inference interruption interface
• Add logprob and token_id to the return value