Moreover, you can fine-tune RWKV into a non-parallelizable RNN (then you can use outputs of later layers of the previous token) if you want extra performance. Here are some of my TODOs. Let's work together :) HuggingFace integration (check huggingface/transformers#17230 ), and optimized ...
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=False) print(generated_text) outputs = model.generate( input_ids=batch_prompts, max_length=400, cg=True, return_dict_in_generate=True, output_scores=True, enable_timing=True, top_k=1, eos_token_id=tokenizer.eos_token_id ...
QueryExecutionInputsAndOutputsRequest QuestionAnsweringCorrectnessInput QuestionAnsweringCorrectnessInstance QuestionAnsweringCorrectnessResult QuestionAnsweringCorrectnessSpec QuestionAnsweringHelpfulnessInput QuestionAnsweringHelpfulnessInstance QuestionAnsweringHelpfulnessResult QuestionAnswe...
Deep learning (DL) networks processing often handles large amount of inputs/outputs data that can not fit engine's local memory, therefore data blobs are broken into smaller blocks and each input block at a time is fetched from external memory (i.e. System Cache or DDR) to engine's local...
对于causal mask self attention来讲，前面已经计算出的key和value都可以缓存起来。对于Encoder-Decoder结构...
使不使用 KV Cache 的对比 使用 KV Cache 解码阶段计算量分析 KV Cache 显存占用分析 KV Cache 存在...
loss_kwargs- Additional keyword arguments to the loss functionloss_fn. This is intended to enable flexible loss computation (thanks to dynamic graph in Pytorch) such as reduction, weighting, etc. Potentially, usingloss_kwargsyou can incorporate outputs from those encoder models not tracked by the...
evaluation-structured-outputs.md falcon-180b.md falcon.md falcon2-11b.md fast-diffusers-coreml.md fast-mac-diffusers.md fastai.md fasttext.md fellowship.md fetch-case-study.md fetch-eap-case-study.md few-shot-learning-gpt-neo-and-inference-api.md fhe-endpoints.md fine-tune-clip...
docs = vector_db.similarity_search(query) st = time.time() result = chain({"input_documents": docs, "question": query}, return_only_outputs=True) et = time.time() print("The first execution time:", et-st) print("result:", result) st = time.time() result = chain({"input_...
1 out_text += text print(f' Input: {in_text}') print(f'Output: {out_text}')通过...