num_cpu_blocks: int, watermark: float = 0.01, sliding_window: Optional[int] = None, ) -> None: self.block_size = block_size self.num_total_gpu_blocks = num_gpu_blocks self.num_total_cpu_blocks = num_cpu_blocks self.block_sliding_window = None if sliding_window is not None: asser...
huggingface/swift-transformers Step 2: Download the converted Core ML models from this Hugging Face repo Step 3: Run inference using Swift: swift run transformers "Best recommendations for a place to visit in Paris in August 2024:" --max-length 200 Mistral7B-CoreML/StatefulMistralInstruc...
completion_tokens int 回答tokens数 total_tokens int tokens总数 注意 :同步模式和流式模式,响应参数返回不同,详细内容参考示例描述。 同步模式下,响应参数为以上字段的完整json包。 流式模式下,各字段的响应参数为 data: {响应参数}。 请求示例(单轮) 以访问凭证access_token鉴权方式为例,说明如何调用API,示例如...
Mistral 7B v0.2是基础模型,并不适合直接使用推理使用,推荐使用其instruct版本 qucik start with raw_weights, hackathon 下载原始模型权重文件并运行 #download the model$wget -c https://models.mistralcdn.com/mistral-7b-v0-2/Mistral-7B-v0.2-Instruct.tar$md5sum Mistral-7B-v0.2-Instruct.tar#解压, 得到...
Mixtral 8x7B 是SMOE 的模型，在大多数基准测试中优于或等价于Llama 2 70 B, GPT3.5，且推理速度...
12.8日更新,先跑几个热门的,欢迎大家来比较,如果没有特别指明,都是跑int4 量化:1. mistral large 123b,惨烈5tokens每秒,只能算能跑2.mistral 8x22b,140b,激活参数47b,这是我最喜欢的模型,可惜没更新,17tokens每秒3.mistal 8x7b,47b, 激活参数大概14b,45tokens每秒4. llama3.3 70b,最新的,10tokens每秒,跟...
huggingface/swift-transformers Step 2: Download the converted Core ML models from this Hugging Face repo Step 3: Run inference using Swift: swift run transformers "Best recommendations for a place to visit in Paris in August 2024:" --max-length 200 Mistral7B-CoreML/StatefulMistralInstructInt4....
save("StatefulMistral7BInstructInt4.mlpackage")There’s a final step after conversion and quantization are done. We need to include a piece of additional metadata that indicates the model identifier we used (mistralai/Mistral-7B-Instruct-v0.3). The Swift code will use this to download the ...
格瑞图:GPTs-0014-知识库-10-部署 OpenChat 7B 模型 格瑞图:GPTs-0015-知识库-11-部署 OpenChat-UI 格瑞图:GPTs-0016-知识库-12-text-generation-webui 部署 格瑞图:GPTs-0017-语音助手-01-AI-voice-chat 部署 格瑞图:GPTs-0018-知识库-13-部署 LangChain-ChatChat-01 格瑞图:GPTs-0019-知识库-14-...
huggingface/swift-transformers Step 2: Download the converted Core ML models from this Hugging Face repo Step 3: Run inference using Swift: swift run transformers "Best recommendations for a place to visit in Paris in August 2024:" --max-length 200 Mistral7B-CoreML/StatefulMistralInstruct...