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Organize command-line arguments
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README.md
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README.md
@ -264,8 +264,8 @@ Optionally, you can use the following command-line flags:
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| Flag | Description |
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|--------------------------------------------|-------------|
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| `-h`, `--help` | Show this help message and exit. |
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| `--multi-user` | Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental. |
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| `-h`, `--help` | show this help message and exit |
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| `--multi-user` | Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is likely not safe for sharing publicly. |
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| `--character CHARACTER` | The name of the character to load in chat mode by default. |
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| `--model MODEL` | Name of the model to load by default. |
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| `--lora LORA [LORA ...]` | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
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@ -275,13 +275,13 @@ Optionally, you can use the following command-line flags:
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| `--settings SETTINGS_FILE` | Load the default interface settings from this yaml file. See `settings-template.yaml` for an example. If you create a file called `settings.yaml`, this file will be loaded by default without the need to use the `--settings` flag. |
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| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
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| `--verbose` | Print the prompts to the terminal. |
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| `--chat-buttons` | Show buttons on chat tab instead of hover menu. |
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| `--chat-buttons` | Show buttons on the chat tab instead of a hover menu. |
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#### Model loader
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| Flag | Description |
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|--------------------------------------------|-------------|
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| `--loader LOADER` | Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv, ctransformers |
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| `--loader LOADER` | Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, exllama_hf, exllamav2_hf, exllama, exllamav2, autogptq, gptq-for-llama, llama.cpp, llamacpp_hf, ctransformers, autoawq. |
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#### Accelerate/transformers
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@ -289,17 +289,17 @@ Optionally, you can use the following command-line flags:
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|---------------------------------------------|-------------|
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| `--cpu` | Use the CPU to generate text. Warning: Training on CPU is extremely slow. |
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| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU. |
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| `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maximum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. You can also set values in MiB like `--gpu-memory 3500MiB`. |
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| `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maximum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB. |
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| `--cpu-memory CPU_MEMORY` | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above. |
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| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
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| `--disk-cache-dir DISK_CACHE_DIR` | Directory to save the disk cache to. Defaults to `cache/`. |
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| `--disk-cache-dir DISK_CACHE_DIR` | Directory to save the disk cache to. Defaults to "cache". |
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| `--load-in-8bit` | Load the model with 8-bit precision (using bitsandbytes). |
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| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
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| `--no-cache` | Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
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| `--xformers` | Use xformer's memory efficient attention. This should increase your tokens/s. |
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| `--sdp-attention` | Use torch 2.0's sdp attention. |
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| `--trust-remote-code` | Set trust_remote_code=True while loading a model. Necessary for ChatGLM and Falcon. |
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| `--use_fast` | Set use_fast=True while loading a tokenizer. |
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| `--no-cache` | Set `use_cache` to `False` while generating text. This reduces VRAM usage slightly, but it comes at a performance cost. |
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| `--xformers` | Use xformer's memory efficient attention. This is really old and probably doesn't do anything. |
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| `--sdp-attention` | Use PyTorch 2.0's SDP attention. Same as above. |
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| `--trust-remote-code` | Set `trust_remote_code=True` while loading the model. Necessary for some models. |
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| `--use_fast` | Set `use_fast=True` while loading the tokenizer. |
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#### Accelerate 4-bit
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@ -308,39 +308,34 @@ Optionally, you can use the following command-line flags:
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| Flag | Description |
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|---------------------------------------------|-------------|
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| `--load-in-4bit` | Load the model with 4-bit precision (using bitsandbytes). |
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| `--use_double_quant` | use_double_quant for 4-bit. |
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| `--compute_dtype COMPUTE_DTYPE` | compute dtype for 4-bit. Valid options: bfloat16, float16, float32. |
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| `--quant_type QUANT_TYPE` | quant_type for 4-bit. Valid options: nf4, fp4. |
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| `--use_double_quant` | use_double_quant for 4-bit. |
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#### GGUF (for llama.cpp and ctransformers)
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| Flag | Description |
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|-------------|-------------|
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| `--threads` | Number of threads to use. |
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| `--threads-batch THREADS_BATCH` | Number of threads to use for batches/prompt processing. |
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| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. |
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| `--n-gpu-layers N_GPU_LAYERS` | Number of layers to offload to the GPU. Only works if llama-cpp-python was compiled with BLAS. Set this to 1000000000 to offload all layers to the GPU. |
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| `--n_ctx N_CTX` | Size of the prompt context. |
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#### llama.cpp
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| Flag | Description |
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|---------------|---------------|
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|-------------|-------------|
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| `--n_ctx N_CTX` | Size of the prompt context. |
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| `--threads` | Number of threads to use. |
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| `--threads-batch THREADS_BATCH` | Number of threads to use for batches/prompt processing. |
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| `--mul_mat_q` | Activate new mulmat kernels. |
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| `--tensor_split TENSOR_SPLIT` | Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17 |
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| `--llama_cpp_seed SEED` | Seed for llama-cpp models. Default 0 (random). |
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| `--cache-capacity CACHE_CAPACITY` | Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
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|`--cfg-cache` | llamacpp_HF: Create an additional cache for CFG negative prompts. |
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| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. |
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| `--no-mmap` | Prevent mmap from being used. |
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| `--mlock` | Force the system to keep the model in RAM. |
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| `--numa` | Activate NUMA task allocation for llama.cpp |
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| `--cpu` | Use the CPU version of llama-cpp-python instead of the GPU-accelerated version. |
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| `--n-gpu-layers N_GPU_LAYERS` | Number of layers to offload to the GPU. |
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| `--tensor_split TENSOR_SPLIT` | Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17. |
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| `--llama_cpp_seed SEED` | Seed for llama-cpp models. Default is 0 (random). |
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| `--numa` | Activate NUMA task allocation for llama.cpp. |
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| `--cache-capacity CACHE_CAPACITY` | Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
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#### ctransformers
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#### ExLlama
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| Flag | Description |
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|-------------|-------------|
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| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently gpt2, gptj, gptneox, falcon, llama, mpt, starcoder (gptbigcode), dollyv2, and replit are supported. |
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|------------------|-------------|
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|`--gpu-split` | Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7. |
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|`--max_seq_len MAX_SEQ_LEN` | Maximum sequence length. |
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|`--cfg-cache` | ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama. |
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#### AutoGPTQ
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@ -353,14 +348,6 @@ Optionally, you can use the following command-line flags:
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| `--desc_act` | For models that don't have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig. |
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| `--disable_exllama` | Disable ExLlama kernel, which can improve inference speed on some systems. |
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#### ExLlama
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| Flag | Description |
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|------------------|-------------|
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|`--gpu-split` | Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. `20,7,7` |
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|`--max_seq_len MAX_SEQ_LEN` | Maximum sequence length. |
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|`--cfg-cache` | ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama. |
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#### GPTQ-for-LLaMa
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| Flag | Description |
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@ -370,7 +357,13 @@ Optionally, you can use the following command-line flags:
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| `--groupsize GROUPSIZE` | Group size. |
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| `--pre_layer PRE_LAYER [PRE_LAYER ...]` | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg `--pre_layer 30 60`. |
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| `--checkpoint CHECKPOINT` | The path to the quantized checkpoint file. If not specified, it will be automatically detected. |
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| `--monkey-patch` | Apply the monkey patch for using LoRAs with quantized models.
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| `--monkey-patch` | Apply the monkey patch for using LoRAs with quantized models. |
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#### ctransformers
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| Flag | Description |
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|-------------|-------------|
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| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently gpt2, gptj, gptneox, falcon, llama, mpt, starcoder (gptbigcode), dollyv2, and replit are supported. |
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#### DeepSpeed
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@ -391,21 +384,21 @@ Optionally, you can use the following command-line flags:
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| Flag | Description |
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|------------------|-------------|
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| `--alpha_value ALPHA_VALUE` | Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both. |
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| `--rope_freq_base ROPE_FREQ_BASE` | If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63). |
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| `--compress_pos_emb COMPRESS_POS_EMB` | Positional embeddings compression factor. Should be set to (context length) / (model's original context length). Equal to 1/rope_freq_scale. |
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| `--alpha_value ALPHA_VALUE` | Positional embeddings alpha factor for NTK RoPE scaling. Use either this or `compress_pos_emb`, not both. |
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| `--rope_freq_base ROPE_FREQ_BASE` | If greater than 0, will be used instead of alpha_value. Those two are related by `rope_freq_base = 10000 * alpha_value ^ (64 / 63)`. |
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| `--compress_pos_emb COMPRESS_POS_EMB` | Positional embeddings compression factor. Should be set to `(context length) / (model's original context length)`. Equal to `1/rope_freq_scale`. |
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#### Gradio
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| Flag | Description |
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|---------------------------------------|-------------|
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| `--listen` | Make the web UI reachable from your local network. |
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| `--listen-host LISTEN_HOST` | The hostname that the server will use. |
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| `--listen-port LISTEN_PORT` | The listening port that the server will use. |
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| `--listen-host LISTEN_HOST` | The hostname that the server will use. |
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| `--share` | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
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| `--auto-launch` | Open the web UI in the default browser upon launch. |
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| `--gradio-auth USER:PWD` | set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3" |
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| `--gradio-auth-path GRADIO_AUTH_PATH` | Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" |
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| `--gradio-auth USER:PWD` | Set Gradio authentication password in the format "username:password". Multiple credentials can also be supplied with "u1:p1,u2:p2,u3:p3". |
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| `--gradio-auth-path GRADIO_AUTH_PATH` | Set the Gradio authentication file path. The file should contain one or more user:password pairs in the same format as above. |
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| `--ssl-keyfile SSL_KEYFILE` | The path to the SSL certificate key file. |
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| `--ssl-certfile SSL_CERTFILE` | The path to the SSL certificate cert file. |
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@ -10,7 +10,7 @@ from modules.logging_colors import logger
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# Model variables
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model = None
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tokenizer = None
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model_name = "None"
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model_name = 'None'
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is_seq2seq = False
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model_dirty_from_training = False
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lora_names = []
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@ -59,94 +59,79 @@ settings = {
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'default_extensions': ['gallery'],
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}
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def str2bool(v):
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if isinstance(v, bool):
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return v
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if v.lower() in ('yes', 'true', 't', 'y', '1'):
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return True
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elif v.lower() in ('no', 'false', 'f', 'n', '0'):
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return False
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else:
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raise argparse.ArgumentTypeError('Boolean value expected.')
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parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
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# Basic settings
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parser.add_argument('--notebook', action='store_true', help='DEPRECATED')
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parser.add_argument('--chat', action='store_true', help='DEPRECATED')
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parser.add_argument('--multi-user', action='store_true', help='Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental.')
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parser.add_argument('--multi-user', action='store_true', help='Multi-user mode. Chat histories are not saved or automatically loaded. Warning: this is likely not safe for sharing publicly.')
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parser.add_argument('--character', type=str, help='The name of the character to load in chat mode by default.')
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parser.add_argument('--model', type=str, help='Name of the model to load by default.')
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parser.add_argument('--lora', type=str, nargs="+", help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.')
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parser.add_argument("--model-dir", type=str, default='models/', help="Path to directory with all the models")
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parser.add_argument("--lora-dir", type=str, default='loras/', help="Path to directory with all the loras")
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parser.add_argument('--lora', type=str, nargs='+', help='The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces.')
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parser.add_argument('--model-dir', type=str, default='models/', help='Path to directory with all the models.')
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parser.add_argument('--lora-dir', type=str, default='loras/', help='Path to directory with all the loras.')
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parser.add_argument('--model-menu', action='store_true', help='Show a model menu in the terminal when the web UI is first launched.')
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parser.add_argument('--no-stream', action='store_true', help='DEPRECATED')
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parser.add_argument('--settings', type=str, help='Load the default interface settings from this yaml file. See settings-template.yaml for an example. If you create a file called settings.yaml, this file will be loaded by default without the need to use the --settings flag.')
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parser.add_argument('--extensions', type=str, nargs="+", help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
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parser.add_argument('--extensions', type=str, nargs='+', help='The list of extensions to load. If you want to load more than one extension, write the names separated by spaces.')
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parser.add_argument('--verbose', action='store_true', help='Print the prompts to the terminal.')
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parser.add_argument('--chat-buttons', action='store_true', help='Show buttons on chat tab instead of hover menu.')
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parser.add_argument('--chat-buttons', action='store_true', help='Show buttons on the chat tab instead of a hover menu.')
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# Model loader
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parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv')
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parser.add_argument('--loader', type=str, help='Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, exllama_hf, exllamav2_hf, exllama, exllamav2, autogptq, gptq-for-llama, llama.cpp, llamacpp_hf, ctransformers, autoawq.')
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# Accelerate/transformers
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parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text. Warning: Training on CPU is extremely slow.')
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parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
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parser.add_argument('--gpu-memory', type=str, nargs="+", help='Maximum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.')
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parser.add_argument('--gpu-memory', type=str, nargs='+', help='Maximum GPU memory in GiB to be allocated per GPU. Example: --gpu-memory 10 for a single GPU, --gpu-memory 10 5 for two GPUs. You can also set values in MiB like --gpu-memory 3500MiB.')
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parser.add_argument('--cpu-memory', type=str, help='Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.')
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parser.add_argument('--disk', action='store_true', help='If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk.')
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parser.add_argument('--disk-cache-dir', type=str, default="cache", help='Directory to save the disk cache to. Defaults to "cache".')
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parser.add_argument('--disk-cache-dir', type=str, default='cache', help='Directory to save the disk cache to. Defaults to "cache".')
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parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision (using bitsandbytes).')
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parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
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parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces the VRAM usage a bit at a performance cost.')
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parser.add_argument('--xformers', action='store_true', help="Use xformer's memory efficient attention. This should increase your tokens/s.")
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parser.add_argument('--sdp-attention', action='store_true', help="Use torch 2.0's sdp attention.")
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parser.add_argument('--trust-remote-code', action='store_true', help="Set trust_remote_code=True while loading a model. Necessary for ChatGLM and Falcon.")
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parser.add_argument('--use_fast', action='store_true', help="Set use_fast=True while loading a tokenizer.")
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parser.add_argument('--no-cache', action='store_true', help='Set use_cache to False while generating text. This reduces VRAM usage slightly, but it comes at a performance cost.')
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parser.add_argument('--xformers', action='store_true', help='Use xformer\'s memory efficient attention. This is really old and probably doesn\'t do anything.')
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parser.add_argument('--sdp-attention', action='store_true', help='Use PyTorch 2.0\'s SDP attention. Same as above.')
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parser.add_argument('--trust-remote-code', action='store_true', help='Set trust_remote_code=True while loading the model. Necessary for some models.')
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parser.add_argument('--use_fast', action='store_true', help='Set use_fast=True while loading the tokenizer.')
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# Accelerate 4-bit
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parser.add_argument('--load-in-4bit', action='store_true', help='Load the model with 4-bit precision (using bitsandbytes).')
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parser.add_argument('--compute_dtype', type=str, default="float16", help="compute dtype for 4-bit. Valid options: bfloat16, float16, float32.")
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parser.add_argument('--quant_type', type=str, default="nf4", help='quant_type for 4-bit. Valid options: nf4, fp4.')
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parser.add_argument('--use_double_quant', action='store_true', help='use_double_quant for 4-bit.')
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parser.add_argument('--compute_dtype', type=str, default='float16', help='compute dtype for 4-bit. Valid options: bfloat16, float16, float32.')
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parser.add_argument('--quant_type', type=str, default='nf4', help='quant_type for 4-bit. Valid options: nf4, fp4.')
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# llama.cpp
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parser.add_argument('--n_ctx', type=int, default=2048, help='Size of the prompt context.')
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parser.add_argument('--threads', type=int, default=0, help='Number of threads to use.')
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parser.add_argument('--threads-batch', type=int, default=0, help='Number of threads to use for batches/prompt processing.')
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parser.add_argument('--mul_mat_q', action='store_true', help='Activate new mulmat kernels.')
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parser.add_argument('--n_batch', type=int, default=512, help='Maximum number of prompt tokens to batch together when calling llama_eval.')
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parser.add_argument('--no-mmap', action='store_true', help='Prevent mmap from being used.')
|
||||
parser.add_argument('--mlock', action='store_true', help='Force the system to keep the model in RAM.')
|
||||
parser.add_argument('--mul_mat_q', action='store_true', help='Activate new mulmat kernels.')
|
||||
parser.add_argument('--cache-capacity', type=str, help='Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.')
|
||||
parser.add_argument('--n-gpu-layers', type=int, default=0, help='Number of layers to offload to the GPU.')
|
||||
parser.add_argument('--tensor_split', type=str, default=None, help="Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17")
|
||||
parser.add_argument('--n_ctx', type=int, default=2048, help='Size of the prompt context.')
|
||||
parser.add_argument('--llama_cpp_seed', type=int, default=0, help='Seed for llama-cpp models. Default 0 (random)')
|
||||
parser.add_argument('--numa', action='store_true', help='Activate NUMA task allocation for llama.cpp')
|
||||
parser.add_argument('--tensor_split', type=str, default=None, help='Split the model across multiple GPUs. Comma-separated list of proportions. Example: 18,17.')
|
||||
parser.add_argument('--llama_cpp_seed', type=int, default=0, help='Seed for llama-cpp models. Default is 0 (random).')
|
||||
parser.add_argument('--numa', action='store_true', help='Activate NUMA task allocation for llama.cpp.')
|
||||
parser.add_argument('--cache-capacity', type=str, help='Maximum cache capacity (llama-cpp-python). Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed.')
|
||||
|
||||
# GPTQ
|
||||
parser.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
|
||||
parser.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.')
|
||||
parser.add_argument('--groupsize', type=int, default=-1, help='Group size.')
|
||||
parser.add_argument('--pre_layer', type=int, nargs="+", help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.')
|
||||
parser.add_argument('--checkpoint', type=str, help='The path to the quantized checkpoint file. If not specified, it will be automatically detected.')
|
||||
parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.')
|
||||
# ExLlama
|
||||
parser.add_argument('--gpu-split', type=str, help='Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7.')
|
||||
parser.add_argument('--max_seq_len', type=int, default=2048, help='Maximum sequence length.')
|
||||
parser.add_argument('--cfg-cache', action='store_true', help='ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama.')
|
||||
|
||||
# AutoGPTQ
|
||||
parser.add_argument('--triton', action='store_true', help='Use triton.')
|
||||
parser.add_argument('--no_inject_fused_attention', action='store_true', help='Do not use fused attention (lowers VRAM requirements).')
|
||||
parser.add_argument('--no_inject_fused_mlp', action='store_true', help='Triton mode only: Do not use fused MLP (lowers VRAM requirements).')
|
||||
parser.add_argument('--no_inject_fused_attention', action='store_true', help='Disable the use of fused attention, which will use less VRAM at the cost of slower inference.')
|
||||
parser.add_argument('--no_inject_fused_mlp', action='store_true', help='Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference.')
|
||||
parser.add_argument('--no_use_cuda_fp16', action='store_true', help='This can make models faster on some systems.')
|
||||
parser.add_argument('--desc_act', action='store_true', help='For models that don\'t have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.')
|
||||
parser.add_argument('--desc_act', action='store_true', help='For models that do not have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig.')
|
||||
parser.add_argument('--disable_exllama', action='store_true', help='Disable ExLlama kernel, which can improve inference speed on some systems.')
|
||||
|
||||
# ExLlama
|
||||
parser.add_argument('--gpu-split', type=str, help="Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. 20,7,7")
|
||||
parser.add_argument('--max_seq_len', type=int, default=2048, help="Maximum sequence length.")
|
||||
parser.add_argument('--cfg-cache', action='store_true', help="ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama.")
|
||||
# GPTQ-for-LLaMa
|
||||
parser.add_argument('--wbits', type=int, default=0, help='Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported.')
|
||||
parser.add_argument('--model_type', type=str, help='Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported.')
|
||||
parser.add_argument('--groupsize', type=int, default=-1, help='Group size.')
|
||||
parser.add_argument('--pre_layer', type=int, nargs='+', help='The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg --pre_layer 30 60.')
|
||||
parser.add_argument('--checkpoint', type=str, help='The path to the quantized checkpoint file. If not specified, it will be automatically detected.')
|
||||
parser.add_argument('--monkey-patch', action='store_true', help='Apply the monkey patch for using LoRAs with quantized models.')
|
||||
|
||||
# DeepSpeed
|
||||
parser.add_argument('--deepspeed', action='store_true', help='Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration.')
|
||||
@ -158,31 +143,36 @@ parser.add_argument('--rwkv-strategy', type=str, default=None, help='RWKV: The s
|
||||
parser.add_argument('--rwkv-cuda-on', action='store_true', help='RWKV: Compile the CUDA kernel for better performance.')
|
||||
|
||||
# RoPE
|
||||
parser.add_argument('--alpha_value', type=float, default=1, help="Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.")
|
||||
parser.add_argument('--rope_freq_base', type=int, default=0, help="If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63).")
|
||||
parser.add_argument('--alpha_value', type=float, default=1, help='Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both.')
|
||||
parser.add_argument('--rope_freq_base', type=int, default=0, help='If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63).')
|
||||
parser.add_argument('--compress_pos_emb', type=int, default=1, help="Positional embeddings compression factor. Should be set to (context length) / (model\'s original context length). Equal to 1/rope_freq_scale.")
|
||||
|
||||
# Gradio
|
||||
parser.add_argument('--listen', action='store_true', help='Make the web UI reachable from your local network.')
|
||||
parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.')
|
||||
parser.add_argument('--listen-port', type=int, help='The listening port that the server will use.')
|
||||
parser.add_argument('--listen-host', type=str, help='The hostname that the server will use.')
|
||||
parser.add_argument('--share', action='store_true', help='Create a public URL. This is useful for running the web UI on Google Colab or similar.')
|
||||
parser.add_argument('--auto-launch', action='store_true', default=False, help='Open the web UI in the default browser upon launch.')
|
||||
parser.add_argument("--gradio-auth", type=str, help='set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3"', default=None)
|
||||
parser.add_argument("--gradio-auth-path", type=str, help='Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3"', default=None)
|
||||
parser.add_argument("--ssl-keyfile", type=str, help='The path to the SSL certificate key file.', default=None)
|
||||
parser.add_argument("--ssl-certfile", type=str, help='The path to the SSL certificate cert file.', default=None)
|
||||
parser.add_argument('--gradio-auth', type=str, help='Set Gradio authentication password in the format "username:password". Multiple credentials can also be supplied with "u1:p1,u2:p2,u3:p3".', default=None)
|
||||
parser.add_argument('--gradio-auth-path', type=str, help='Set the Gradio authentication file path. The file should contain one or more user:password pairs in the same format as above.', default=None)
|
||||
parser.add_argument('--ssl-keyfile', type=str, help='The path to the SSL certificate key file.', default=None)
|
||||
parser.add_argument('--ssl-certfile', type=str, help='The path to the SSL certificate cert file.', default=None)
|
||||
|
||||
# API
|
||||
parser.add_argument('--api', action='store_true', help='Enable the API extension.')
|
||||
parser.add_argument('--api-blocking-port', type=int, default=5000, help='The listening port for the blocking API.')
|
||||
parser.add_argument('--api-streaming-port', type=int, default=5005, help='The listening port for the streaming API.')
|
||||
parser.add_argument('--public-api', action='store_true', help='Create a public URL for the API using Cloudfare.')
|
||||
parser.add_argument('--public-api-id', type=str, help='Tunnel ID for named Cloudflare Tunnel. Use together with public-api option.', default=None)
|
||||
parser.add_argument('--api-blocking-port', type=int, default=5000, help='The listening port for the blocking API.')
|
||||
parser.add_argument('--api-streaming-port', type=int, default=5005, help='The listening port for the streaming API.')
|
||||
|
||||
# Multimodal
|
||||
parser.add_argument('--multimodal-pipeline', type=str, default=None, help='The multimodal pipeline to use. Examples: llava-7b, llava-13b.')
|
||||
|
||||
# Deprecated parameters
|
||||
parser.add_argument('--notebook', action='store_true', help='DEPRECATED')
|
||||
parser.add_argument('--chat', action='store_true', help='DEPRECATED')
|
||||
parser.add_argument('--no-stream', action='store_true', help='DEPRECATED')
|
||||
|
||||
args = parser.parse_args()
|
||||
args_defaults = parser.parse_args([])
|
||||
provided_arguments = []
|
||||
@ -198,13 +188,13 @@ for k in ['chat', 'notebook', 'no_stream']:
|
||||
|
||||
# Security warnings
|
||||
if args.trust_remote_code:
|
||||
logger.warning("trust_remote_code is enabled. This is dangerous.")
|
||||
logger.warning('trust_remote_code is enabled. This is dangerous.')
|
||||
if args.share:
|
||||
logger.warning("The gradio \"share link\" feature uses a proprietary executable to create a reverse tunnel. Use it with care.")
|
||||
if any((args.listen, args.share)) and not any((args.gradio_auth, args.gradio_auth_path)):
|
||||
logger.warning("\nYou are potentially exposing the web UI to the entire internet without any access password.\nYou can create one with the \"--gradio-auth\" flag like this:\n\n--gradio-auth username:password\n\nMake sure to replace username:password with your own.")
|
||||
if args.multi_user:
|
||||
logger.warning("\nThe multi-user mode is highly experimental and should not be shared publicly.")
|
||||
logger.warning('\nThe multi-user mode is highly experimental and should not be shared publicly.')
|
||||
|
||||
|
||||
def fix_loader_name(name):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user