Update README.md
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@@ -161,7 +161,7 @@ Below is a summary of the available RunPod Worker images, categorized by image s
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| `MAX_PARALLEL_LOADING_WORKERS` | `None` | `int` |Load model sequentially in multiple batches, to avoid RAM OOM when using tensor parallel and large models. |
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| `BLOCK_SIZE` | `16` | `8`, `16`, `32` |Token block size for contiguous chunks of tokens. |
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| `SWAP_SPACE` | `4` | `int` |CPU swap space size (GiB) per GPU. |
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| `ENFORCE_EAGER` | `0` | boolean as `int` |Always use eager-mode PyTorch. If False(`0`), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility. |
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| `ENFORCE_EAGER` | False | `bool` |Always use eager-mode PyTorch. If False(`0`), will use eager mode and CUDA graph in hybrid for maximal performance and flexibility. |
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| `MAX_SEQ_LEN_TO_CAPTURE` | `8192` | `int` |Maximum context length covered by CUDA graphs. When a sequence has context length larger than this, we fall back to eager mode.|
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| `DISABLE_CUSTOM_ALL_REDUCE` | `0` | `int` |Enables or disables custom all reduce. |
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**Streaming Batch Size Settings**:
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@@ -175,8 +175,8 @@ The way this works is that the first request will have a batch size of `DEFAULT_
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| `OPENAI_RESPONSE_ROLE` | `assistant` | `str` |Role of the LLM's Response in OpenAI Chat Completions. |
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**Serverless Settings**
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| `MAX_CONCURRENCY` | `300` | `int` |Max concurrent requests per worker. vLLM has an internal queue, so you don't have to worry about limiting by VRAM, this is for improving scaling/load balancing efficiency |
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| `DISABLE_LOG_STATS` | `1` | boolean as `int` |Enables or disables vLLM stats logging. |
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| `DISABLE_LOG_REQUESTS` | `1` | boolean as `int` |Enables or disables vLLM request logging. |
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| `DISABLE_LOG_STATS` | False | `bool` |Enables or disables vLLM stats logging. |
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| `DISABLE_LOG_REQUESTS` | False | `bool` |Enables or disables vLLM request logging. |
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> [!TIP]
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> If you are facing issues when using Mixtral 8x7B, Quantized models, or handling unusual models/architectures, try setting `TRUST_REMOTE_CODE` to `1`.
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