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Merge pull request #4475 from oobabooga/dev

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26 changed files with 177 additions and 59 deletions
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1
.gitignore vendored
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@ -26,6 +26,7 @@
.DS_Store
.eslintrc.js
.idea
.env
.venv
venv
.vscode

3
README.md
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@ -300,6 +300,7 @@ Optionally, you can use the following command-line flags:
| `--sdp-attention` | Use PyTorch 2.0's SDP attention. Same as above. |
| `--trust-remote-code` | Set `trust_remote_code=True` while loading the model. Necessary for some models. |
| `--use_fast` | Set `use_fast=True` while loading the tokenizer. |
| `--use_flash_attention_2` | Set use_flash_attention_2=True while loading the model. |
#### Accelerate 4-bit
@ -336,6 +337,8 @@ Optionally, you can use the following command-line flags:
|`--gpu-split` | Comma-separated list of VRAM (in GB) to use per GPU device for model layers. Example: 20,7,7. |
|`--max_seq_len MAX_SEQ_LEN` | Maximum sequence length. |
|`--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. |
|`--no_flash_attn` | Force flash-attention to not be used. |
|`--cache_8bit` | Use 8-bit cache to save VRAM. |
#### AutoGPTQ

2
extensions/api/util.py
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@ -25,7 +25,9 @@ def build_parameters(body, chat=False):
'max_tokens_second': int(body.get('max_tokens_second', 0)),
'do_sample': bool(body.get('do_sample', True)),
'temperature': float(body.get('temperature', 0.5)),
'temperature_last': bool(body.get('temperature_last', False)),
'top_p': float(body.get('top_p', 1)),
'min_p': float(body.get('min_p', 0)),
'typical_p': float(body.get('typical_p', body.get('typical', 1))),
'epsilon_cutoff': float(body.get('epsilon_cutoff', 0)),
'eta_cutoff': float(body.get('eta_cutoff', 0)),

6
models/config.yaml
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@ -45,9 +45,6 @@
.*starchat-beta:
instruction_template: 'Starchat-Beta'
custom_stopping_strings: '"<|end|>"'
.*(openorca-platypus2):
instruction_template: 'OpenOrca-Platypus2'
custom_stopping_strings: '"### Instruction:", "### Response:"'
(?!.*v0)(?!.*1.1)(?!.*1_1)(?!.*stable)(?!.*chinese).*vicuna:
instruction_template: 'Vicuna-v0'
.*vicuna.*v0:
@ -152,6 +149,9 @@
instruction_template: 'Orca Mini'
.*(platypus|gplatty|superplatty):
instruction_template: 'Alpaca'
.*(openorca-platypus2):
instruction_template: 'OpenOrca-Platypus2'
custom_stopping_strings: '"### Instruction:", "### Response:"'
.*longchat:
instruction_template: 'Vicuna-v1.1'
.*vicuna-33b:

2
modules/GPTQ_loader.py
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@ -62,7 +62,7 @@ def _load_quant(model, checkpoint, wbits, groupsize=-1, faster_kernel=False, exc
from safetensors.torch import load_file as safe_load
model.load_state_dict(safe_load(checkpoint), strict=False)
else:
model.load_state_dict(torch.load(checkpoint), strict=False)
model.load_state_dict(torch.load(checkpoint, weights_only=True), strict=False)
model.seqlen = 2048
return model

6
modules/exllamav2.py
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@ -6,6 +6,7 @@ import torch
from exllamav2 import (
ExLlamaV2,
ExLlamaV2Cache,
ExLlamaV2Cache_8bit,
ExLlamaV2Config,
ExLlamaV2Tokenizer
)
@ -46,6 +47,7 @@ class Exllamav2Model:
config.max_seq_len = shared.args.max_seq_len
config.scale_pos_emb = shared.args.compress_pos_emb
config.scale_alpha_value = shared.args.alpha_value
config.no_flash_attn = shared.args.no_flash_attn
model = ExLlamaV2(config)
@ -56,7 +58,11 @@ class Exllamav2Model:
model.load(split)
tokenizer = ExLlamaV2Tokenizer(config)
if shared.args.cache_8bit:
cache = ExLlamaV2Cache_8bit(model)
else:
cache = ExLlamaV2Cache(model)
generator = ExLlamaV2BaseGenerator(model, cache, tokenizer)
result = self()

17
modules/exllamav2_hf.py
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@ -4,7 +4,12 @@ from pathlib import Path
from typing import Any, Dict, Optional, Union
import torch
from exllamav2 import ExLlamaV2, ExLlamaV2Cache, ExLlamaV2Config
from exllamav2 import (
ExLlamaV2,
ExLlamaV2Cache,
ExLlamaV2Cache_8bit,
ExLlamaV2Config
)
from torch.nn import CrossEntropyLoss
from transformers import GenerationConfig, PretrainedConfig, PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
@ -40,11 +45,18 @@ class Exllamav2HF(PreTrainedModel):
self.generation_config = GenerationConfig()
self.loras = None
if shared.args.cache_8bit:
self.ex_cache = ExLlamaV2Cache_8bit(self.ex_model)
else:
self.ex_cache = ExLlamaV2Cache(self.ex_model)
self.past_seq = None
self.past_seq = None
if shared.args.cfg_cache:
if shared.args.cache_8bit:
self.ex_cache_negative = ExLlamaV2Cache_8bit(self.ex_model)
else:
self.ex_cache_negative = ExLlamaV2Cache(self.ex_model)
self.past_seq_negative = None
def _validate_model_class(self):
@ -152,5 +164,6 @@ class Exllamav2HF(PreTrainedModel):
config.max_seq_len = shared.args.max_seq_len
config.scale_pos_emb = shared.args.compress_pos_emb
config.scale_alpha_value = shared.args.alpha_value
config.no_flash_attn = shared.args.no_flash_attn
return Exllamav2HF(config)

20
modules/loaders.py
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@ -9,7 +9,6 @@ loaders_and_params = OrderedDict({
'Transformers': [
'cpu_memory',
'gpu_memory',
'trust_remote_code',
'load_in_8bit',
'bf16',
'cpu',
@ -21,6 +20,7 @@ loaders_and_params = OrderedDict({
'compute_dtype',
'trust_remote_code',
'use_fast',
'use_flash_attention_2',
'alpha_value',
'rope_freq_base',
'compress_pos_emb',
@ -41,6 +41,8 @@ loaders_and_params = OrderedDict({
'gpu_split',
'max_seq_len',
'cfg_cache',
'no_flash_attn',
'cache_8bit',
'alpha_value',
'compress_pos_emb',
'use_fast',
@ -56,6 +58,8 @@ loaders_and_params = OrderedDict({
'ExLlamav2': [
'gpu_split',
'max_seq_len',
'no_flash_attn',
'cache_8bit',
'alpha_value',
'compress_pos_emb',
],
@ -144,7 +148,9 @@ loaders_and_params = OrderedDict({
loaders_samplers = {
'Transformers': {
'temperature',
'temperature_last',
'top_p',
'min_p',
'top_k',
'typical_p',
'epsilon_cutoff',
@ -179,7 +185,9 @@ loaders_samplers = {
},
'ExLlama_HF': {
'temperature',
'temperature_last',
'top_p',
'min_p',
'top_k',
'typical_p',
'epsilon_cutoff',
@ -239,7 +247,9 @@ loaders_samplers = {
},
'ExLlamav2_HF': {
'temperature',
'temperature_last',
'top_p',
'min_p',
'top_k',
'typical_p',
'epsilon_cutoff',
@ -270,7 +280,9 @@ loaders_samplers = {
},
'AutoGPTQ': {
'temperature',
'temperature_last',
'top_p',
'min_p',
'top_k',
'typical_p',
'epsilon_cutoff',
@ -305,7 +317,9 @@ loaders_samplers = {
},
'GPTQ-for-LLaMa': {
'temperature',
'temperature_last',
'top_p',
'min_p',
'top_k',
'typical_p',
'epsilon_cutoff',
@ -356,7 +370,9 @@ loaders_samplers = {
},
'llamacpp_HF': {
'temperature',
'temperature_last',
'top_p',
'min_p',
'top_k',
'typical_p',
'epsilon_cutoff',
@ -394,7 +410,9 @@ loaders_samplers = {
},
'AutoAWQ': {
'temperature',
'temperature_last',
'top_p',
'min_p',
'top_k',
'typical_p',
'epsilon_cutoff',

7
modules/models.py
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@ -123,8 +123,13 @@ def huggingface_loader(model_name):
params = {
'low_cpu_mem_usage': True,
'trust_remote_code': shared.args.trust_remote_code,
'torch_dtype': torch.bfloat16 if shared.args.bf16 else torch.float16
'torch_dtype': torch.bfloat16 if shared.args.bf16 else torch.float16,
'use_safetensors': True if shared.args.force_safetensors else None
}
if shared.args.use_flash_attention_2:
params['use_flash_attention_2'] = True
config = AutoConfig.from_pretrained(path_to_model, trust_remote_code=params['trust_remote_code'])
if 'chatglm' in model_name.lower():

2
modules/presets.py
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@ -8,7 +8,9 @@ def default_preset():
return {
'do_sample': True,
'temperature': 1,
'temperature_last': False,
'top_p': 1,
'min_p': 0,
'top_k': 0,
'typical_p': 1,
'epsilon_cutoff': 0,

59
modules/sampler_hijack.py
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@ -13,6 +13,36 @@ from transformers.generation.logits_process import (
global_scores = None
class MinPLogitsWarper(LogitsWarper):
def __init__(self, min_p: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
if min_p < 0 or min_p > 1.0:
raise ValueError(f"`min_p` has to be a float >= 0 and <= 1, but is {min_p}")
self.min_p = min_p
self.filter_value = filter_value
self.min_tokens_to_keep = min_tokens_to_keep
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor) -> torch.FloatTensor:
# Convert logits to probabilities
probs = torch.softmax(scores, dim=-1)
# Get the probability of the top token for each sequence in the batch
top_probs, _ = probs.max(dim=-1, keepdim=True)
# Calculate the actual min_p threshold by scaling min_p with the top token's probability
scaled_min_p = self.min_p * top_probs
# Create a mask for tokens that have a probability less than the scaled min_p
tokens_to_remove = probs < scaled_min_p
sorted_indices = torch.argsort(scores, descending=True, dim=-1)
sorted_indices_to_remove = torch.gather(tokens_to_remove, dim=-1, index=sorted_indices)
if self.min_tokens_to_keep > 1:
# Keep at least min_tokens_to_keep
sorted_indices_to_remove[..., : self.min_tokens_to_keep] = False
indices_to_remove = sorted_indices_to_remove.scatter(1, sorted_indices, sorted_indices_to_remove)
scores = scores.masked_fill(indices_to_remove, self.filter_value)
return scores
class TailFreeLogitsWarper(LogitsWarper):
def __init__(self, tfs: float, filter_value: float = -float("Inf"), min_tokens_to_keep: int = 1):
tfs = float(tfs)
@ -186,17 +216,36 @@ def get_logits_warper_patch(self, generation_config):
if not isinstance(warper, TemperatureLogitsWarper):
warpers.remove(warper)
else:
if generation_config.tfs is not None and 0.0 <= generation_config.tfs <= 1.0:
if generation_config.tfs is not None and 0.0 <= generation_config.tfs < 1.0:
warpers_to_add.append(TailFreeLogitsWarper(tfs=generation_config.tfs, min_tokens_to_keep=min_tokens_to_keep))
if generation_config.top_a is not None and 0.0 <= generation_config.top_a <= 1.0:
if generation_config.top_a is not None and 0.0 < generation_config.top_a <= 1.0:
warpers_to_add.append(TopALogitsWarper(top_a=generation_config.top_a, min_tokens_to_keep=min_tokens_to_keep))
if generation_config.min_p is not None and 0.0 < generation_config.min_p <= 1.0:
warpers_to_add.append(MinPLogitsWarper(min_p=generation_config.min_p, min_tokens_to_keep=min_tokens_to_keep))
if warpers and isinstance(warpers[-1], LogitNormalization):
warpers = warpers[:-1] + warpers_to_add + [warpers[-1]]
if len(warpers) > 0 and isinstance(warpers[-1], LogitNormalization):
normalize = warpers.pop(-1)
else:
normalize = None
warpers += warpers_to_add
if generation_config.temperature_last:
temperature_idx = None
for i in range(len(warpers)):
if warpers[i].__class__.__name__ == 'TemperatureLogitsWarper':
temperature_idx = i
break
if temperature_idx is not None:
warpers = warpers[:temperature_idx] + warpers[temperature_idx + 1:] + [warpers[temperature_idx]]
warpers = LogitsProcessorList(warpers)
if normalize is not None:
warpers.append(normalize)
warpers.append(SpyLogitsWarper())
# for i in range(len(warpers)):
# print(warpers[i].__class__.__name__)
return warpers
@ -222,6 +271,7 @@ def get_logits_processor_patch(self, **kwargs):
def generation_config_init_patch(self, **kwargs):
self.__init___old(**kwargs)
self.min_p = kwargs.pop("min_p", 0.0)
self.tfs = kwargs.pop("tfs", 1.0)
self.top_a = kwargs.pop("top_a", 0.0)
self.mirostat_mode = kwargs.pop("mirostat_mode", 0)
@ -230,6 +280,7 @@ def generation_config_init_patch(self, **kwargs):
self.repetition_penalty_range = kwargs.pop("repetition_penalty_range", 0)
self.presence_penalty = kwargs.pop("presence_penalty", 0)
self.frequency_penalty = kwargs.pop("frequency_penalty", 0)
self.temperature_last = kwargs.pop("temperature_last", False)
def hijack_samplers():

4
modules/shared.py
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@ -91,7 +91,9 @@ parser.add_argument('--no-cache', action='store_true', help='Set use_cache to Fa
parser.add_argument('--xformers', action='store_true', help='Use xformer\'s memory efficient attention. This is really old and probably doesn\'t do anything.')
parser.add_argument('--sdp-attention', action='store_true', help='Use PyTorch 2.0\'s SDP attention. Same as above.')
parser.add_argument('--trust-remote-code', action='store_true', help='Set trust_remote_code=True while loading the model. Necessary for some models.')
parser.add_argument('--force-safetensors', action='store_true', help='Set use_safetensors=True while loading the model. This prevents arbitrary code execution.')
parser.add_argument('--use_fast', action='store_true', help='Set use_fast=True while loading the tokenizer.')
parser.add_argument('--use_flash_attention_2', action='store_true', help='Set use_flash_attention_2=True while loading the model.')
# Accelerate 4-bit
parser.add_argument('--load-in-4bit', action='store_true', help='Load the model with 4-bit precision (using bitsandbytes).')
@ -117,6 +119,8 @@ parser.add_argument('--cache-capacity', type=str, help='Maximum cache capacity (
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.')
parser.add_argument('--no_flash_attn', action='store_true', help='Force flash-attention to not be used.')
parser.add_argument('--cache_8bit', action='store_true', help='Use 8-bit cache to save VRAM.')
# AutoGPTQ
parser.add_argument('--triton', action='store_true', help='Use triton.')

2
modules/text_generation.py
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@ -274,7 +274,7 @@ def apply_stopping_strings(reply, all_stop_strings):
def generate_reply_HF(question, original_question, seed, state, stopping_strings=None, is_chat=False):
generate_params = {}
for k in ['max_new_tokens', 'do_sample', 'temperature', 'top_p', 'typical_p', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'guidance_scale']:
for k in ['max_new_tokens', 'do_sample', 'temperature', 'temperature_last', 'top_p', 'min_p', 'typical_p', 'repetition_penalty', 'presence_penalty', 'frequency_penalty', 'repetition_penalty_range', 'encoder_repetition_penalty', 'top_k', 'min_length', 'no_repeat_ngram_size', 'num_beams', 'penalty_alpha', 'length_penalty', 'early_stopping', 'tfs', 'top_a', 'mirostat_mode', 'mirostat_tau', 'mirostat_eta', 'guidance_scale']:
generate_params[k] = state[k]
if state['negative_prompt'] != '':

2
modules/training.py
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@ -544,7 +544,7 @@ def do_train(lora_name: str, always_override: bool, q_proj_en: bool, v_proj_en:
lora_model = get_peft_model(shared.model, config)
if not always_override and Path(f"{lora_file_path}/adapter_model.bin").is_file():
logger.info("Loading existing LoRA data...")
state_dict_peft = torch.load(f"{lora_file_path}/adapter_model.bin")
state_dict_peft = torch.load(f"{lora_file_path}/adapter_model.bin", weights_only=True)
set_peft_model_state_dict(lora_model, state_dict_peft)
except:
yield traceback.format_exc().replace('\n', '\n\n')

5
modules/ui.py
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@ -53,6 +53,7 @@ def list_model_elements():
'load_in_8bit',
'trust_remote_code',
'use_fast',
'use_flash_attention_2',
'load_in_4bit',
'compute_dtype',
'quant_type',
@ -68,6 +69,8 @@ def list_model_elements():
'no_use_cuda_fp16',
'disable_exllama',
'cfg_cache',
'no_flash_attn',
'cache_8bit',
'threads',
'threads_batch',
'n_batch',
@ -102,7 +105,9 @@ def list_interface_input_elements():
'max_tokens_second',
'seed',
'temperature',
'temperature_last',
'top_p',
'min_p',
'top_k',
'typical_p',
'epsilon_cutoff',

12
modules/ui_model_menu.py
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@ -86,7 +86,7 @@ def create_ui():
shared.gradio['quant_type'] = gr.Dropdown(label="quant_type", choices=["nf4", "fp4"], value=shared.args.quant_type)
shared.gradio['n_gpu_layers'] = gr.Slider(label="n-gpu-layers", minimum=0, maximum=128, value=shared.args.n_gpu_layers)
shared.gradio['n_ctx'] = gr.Slider(minimum=0, maximum=32768, step=256, label="n_ctx", value=shared.args.n_ctx)
shared.gradio['n_ctx'] = gr.Slider(minimum=0, maximum=shared.settings['truncation_length_max'], step=256, label="n_ctx", value=shared.args.n_ctx)
shared.gradio['threads'] = gr.Slider(label="threads", minimum=0, step=1, maximum=32, value=shared.args.threads)
shared.gradio['threads_batch'] = gr.Slider(label="threads_batch", minimum=0, step=1, maximum=32, value=shared.args.threads_batch)
shared.gradio['n_batch'] = gr.Slider(label="n_batch", minimum=1, maximum=2048, value=shared.args.n_batch)
@ -97,7 +97,7 @@ def create_ui():
shared.gradio['pre_layer'] = gr.Slider(label="pre_layer", minimum=0, maximum=100, value=shared.args.pre_layer[0] if shared.args.pre_layer is not None else 0)
shared.gradio['autogptq_info'] = gr.Markdown('* ExLlama_HF is recommended over AutoGPTQ for models derived from LLaMA.')
shared.gradio['gpu_split'] = gr.Textbox(label='gpu-split', info='Comma-separated list of VRAM (in GB) to use per GPU. Example: 20,7,7')
shared.gradio['max_seq_len'] = gr.Slider(label='max_seq_len', minimum=0, maximum=32768, step=256, info='Maximum sequence length.', value=shared.args.max_seq_len)
shared.gradio['max_seq_len'] = gr.Slider(label='max_seq_len', minimum=0, maximum=shared.settings['truncation_length_max'], step=256, info='Maximum sequence length.', value=shared.args.max_seq_len)
shared.gradio['alpha_value'] = gr.Slider(label='alpha_value', minimum=1, maximum=8, step=0.05, info='Positional embeddings alpha factor for NTK RoPE scaling. Recommended values (NTKv1): 1.75 for 1.5x context, 2.5 for 2x context. Use either this or compress_pos_emb, not both.', value=shared.args.alpha_value)
shared.gradio['rope_freq_base'] = gr.Slider(label='rope_freq_base', minimum=0, maximum=1000000, step=1000, info='If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63)', value=shared.args.rope_freq_base)
shared.gradio['compress_pos_emb'] = gr.Slider(label='compress_pos_emb', minimum=1, maximum=8, step=1, info='Positional embeddings compression factor. Should be set to (context length) / (model\'s original context length). Equal to 1/rope_freq_scale.', value=shared.args.compress_pos_emb)
@ -124,9 +124,12 @@ def create_ui():
shared.gradio['llama_cpp_seed'] = gr.Number(label='Seed (0 for random)', value=shared.args.llama_cpp_seed)
shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='To enable this option, start the web UI with the --trust-remote-code flag. It is necessary for some models.', interactive=shared.args.trust_remote_code)
shared.gradio['use_fast'] = gr.Checkbox(label="use_fast", value=shared.args.use_fast, info='Set use_fast=True while loading the tokenizer. May trigger a conversion that takes several minutes.')
shared.gradio['use_flash_attention_2'] = gr.Checkbox(label="use_flash_attention_2", value=shared.args.use_flash_attention_2, info='Set use_flash_attention_2=True while loading the model.')
shared.gradio['disable_exllama'] = gr.Checkbox(label="disable_exllama", value=shared.args.disable_exllama, info='Disable ExLlama kernel.')
shared.gradio['no_flash_attn'] = gr.Checkbox(label="no_flash_attn", value=shared.args.no_flash_attn, info='Force flash-attention to not be used.')
shared.gradio['cache_8bit'] = gr.Checkbox(label="cache_8bit", value=shared.args.cache_8bit, info='Use 8-bit cache to save VRAM.')
shared.gradio['gptq_for_llama_info'] = gr.Markdown('GPTQ-for-LLaMa support is currently only kept for compatibility with older GPUs. AutoGPTQ or ExLlama is preferred when compatible. GPTQ-for-LLaMa is installed by default with the webui on supported systems. Otherwise, it has to be installed manually following the instructions here: [instructions](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#installation-1).')
shared.gradio['exllama_info'] = gr.Markdown('For more information, consult the [docs](https://github.com/oobabooga/text-generation-webui/blob/main/docs/ExLlama.md).')
shared.gradio['exllama_info'] = gr.Markdown('For more information, consult the [docs](https://github.com/oobabooga/text-generation-webui/wiki/04-%E2%80%90-Model-Tab#exllama_hf).')
shared.gradio['exllama_HF_info'] = gr.Markdown('ExLlama_HF is a wrapper that lets you use ExLlama like a Transformers model, which means it can use the Transformers samplers. It\'s a bit slower than the regular ExLlama.')
shared.gradio['llamacpp_HF_info'] = gr.Markdown('llamacpp_HF loads llama.cpp as a Transformers model. To use it, you need to download a tokenizer.\n\nOption 1: download `oobabooga/llama-tokenizer` under "Download model or LoRA". That\'s a default Llama tokenizer.\n\nOption 2: place your .gguf in a subfolder of models/ along with these 3 files: tokenizer.model, tokenizer_config.json, and special_tokens_map.json. This takes precedence over Option 1.')
@ -212,6 +215,9 @@ def load_model_wrapper(selected_model, loader, autoload=False):
if 'instruction_template' in settings:
output += '\n\nIt seems to be an instruction-following model with template "{}". In the chat tab, instruct or chat-instruct modes should be used.'.format(settings['instruction_template'])
# Applying the changes to the global shared settings (in-memory)
shared.settings.update({k: v for k, v in settings.items() if k in shared.settings})
yield output
else:
yield f"Failed to load `{selected_model}`."

6
modules/ui_parameters.py
View File

@ -29,9 +29,10 @@ def create_ui(default_preset):
shared.gradio['max_new_tokens'] = gr.Slider(minimum=shared.settings['max_new_tokens_min'], maximum=shared.settings['max_new_tokens_max'], step=1, label='max_new_tokens', value=shared.settings['max_new_tokens'])
shared.gradio['temperature'] = gr.Slider(0.01, 1.99, value=generate_params['temperature'], step=0.01, label='temperature')
shared.gradio['top_p'] = gr.Slider(0.0, 1.0, value=generate_params['top_p'], step=0.01, label='top_p')
shared.gradio['min_p'] = gr.Slider(0.0, 1.0, value=generate_params['min_p'], step=0.01, label='min_p')
shared.gradio['top_k'] = gr.Slider(0, 200, value=generate_params['top_k'], step=1, label='top_k')
shared.gradio['repetition_penalty'] = gr.Slider(1.0, 1.5, value=generate_params['repetition_penalty'], step=0.01, label='repetition_penalty')
shared.gradio['presence_penalty'] = gr.Slider(0, 4, value=generate_params['presence_penalty'], step=0.05, label='presence_penalty')
shared.gradio['presence_penalty'] = gr.Slider(0, 2, value=generate_params['presence_penalty'], step=0.05, label='presence_penalty')
shared.gradio['frequency_penalty'] = gr.Slider(0, 2, value=generate_params['frequency_penalty'], step=0.05, label='frequency_penalty')
shared.gradio['repetition_penalty_range'] = gr.Slider(0, 4096, step=64, value=generate_params['repetition_penalty_range'], label='repetition_penalty_range')
shared.gradio['typical_p'] = gr.Slider(0.0, 1.0, value=generate_params['typical_p'], step=0.01, label='typical_p')
@ -47,6 +48,7 @@ def create_ui(default_preset):
shared.gradio['mirostat_mode'] = gr.Slider(0, 2, step=1, value=generate_params['mirostat_mode'], label='mirostat_mode', info='mode=1 is for llama.cpp only.')
shared.gradio['mirostat_tau'] = gr.Slider(0, 10, step=0.01, value=generate_params['mirostat_tau'], label='mirostat_tau')
shared.gradio['mirostat_eta'] = gr.Slider(0, 1, step=0.01, value=generate_params['mirostat_eta'], label='mirostat_eta')
shared.gradio['temperature_last'] = gr.Checkbox(value=generate_params['temperature_last'], label='temperature_last', info='Makes temperature the last sampler instead of the first.')
shared.gradio['do_sample'] = gr.Checkbox(value=generate_params['do_sample'], label='do_sample')
shared.gradio['seed'] = gr.Number(value=shared.settings['seed'], label='Seed (-1 for random)')
with gr.Accordion('Other parameters', open=False):
@ -57,7 +59,7 @@ def create_ui(default_preset):
shared.gradio['length_penalty'] = gr.Slider(-5, 5, value=generate_params['length_penalty'], label='length_penalty')
shared.gradio['early_stopping'] = gr.Checkbox(value=generate_params['early_stopping'], label='early_stopping')
gr.Markdown("[Learn more](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Generation-Parameters.md)")
gr.Markdown("[Learn more](https://github.com/oobabooga/text-generation-webui/wiki/03-%E2%80%90-Parameters-Tab)")
with gr.Column():
with gr.Row():

22
requirements.txt
View File

@ -2,7 +2,7 @@ accelerate==0.24.*
colorama
datasets
einops
exllamav2==0.0.6; platform_system != "Darwin" and platform_machine != "x86_64"
exllamav2==0.0.7; platform_system != "Darwin" and platform_machine != "x86_64"
gradio==3.50.*
markdown
numpy==1.24.*
@ -16,7 +16,7 @@ safetensors==0.4.0
scipy
sentencepiece
tensorboard
transformers==4.34.*
transformers==4.35.*
tqdm
wandb
@ -53,14 +53,14 @@ https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp38-cp38-win_amd64.whl; platform_system == "Windows" and python_version == "3.8"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp38-cp38-win_amd64.whl; platform_system == "Windows" and python_version == "3.8"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
https://github.com/bdashore3/flash-attention/releases/download/2.3.2-2/flash_attn-2.3.2+cu122-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
https://github.com/bdashore3/flash-attention/releases/download/2.3.2-2/flash_attn-2.3.2+cu122-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
https://github.com/Dao-AILab/flash-attention/releases/download/v2.3.2/flash_attn-2.3.2+cu122torch2.1cxx11abiFALSE-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
@ -84,4 +84,4 @@ https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
https://github.com/jllllll/ctransformers-cuBLAS-wheels/releases/download/AVX2/ctransformers-0.2.27+cu121-py3-none-any.whl
autoawq==0.1.4; platform_system == "Linux" or platform_system == "Windows"
autoawq==0.1.6; platform_system == "Linux" or platform_system == "Windows"

4
requirements_amd.txt
View File

@ -2,7 +2,7 @@ accelerate==0.24.*
colorama
datasets
einops
exllamav2==0.0.6
exllamav2==0.0.7
gradio==3.50.*
markdown
numpy==1.24.*
@ -16,7 +16,7 @@ safetensors==0.4.0
scipy
sentencepiece
tensorboard
transformers==4.34.*
transformers==4.35.*
tqdm
wandb

4
requirements_amd_noavx2.txt
View File

@ -2,7 +2,7 @@ accelerate==0.24.*
colorama
datasets
einops
exllamav2==0.0.6
exllamav2==0.0.7
gradio==3.50.*
markdown
numpy==1.24.*
@ -16,7 +16,7 @@ safetensors==0.4.0
scipy
sentencepiece
tensorboard
transformers==4.34.*
transformers==4.35.*
tqdm
wandb

4
requirements_apple_intel.txt
View File

@ -2,7 +2,7 @@ accelerate==0.24.*
colorama
datasets
einops
exllamav2==0.0.6
exllamav2==0.0.7
gradio==3.50.*
markdown
numpy==1.24.*
@ -16,7 +16,7 @@ safetensors==0.4.0
scipy
sentencepiece
tensorboard
transformers==4.34.*
transformers==4.35.*
tqdm
wandb

4
requirements_apple_silicon.txt
View File

@ -2,7 +2,7 @@ accelerate==0.24.*
colorama
datasets
einops
exllamav2==0.0.6
exllamav2==0.0.7
gradio==3.50.*
markdown
numpy==1.24.*
@ -16,7 +16,7 @@ safetensors==0.4.0
scipy
sentencepiece
tensorboard
transformers==4.34.*
transformers==4.35.*
tqdm
wandb

4
requirements_cpu_only.txt
View File

@ -2,7 +2,7 @@ accelerate==0.24.*
colorama
datasets
einops
exllamav2==0.0.6
exllamav2==0.0.7
gradio==3.50.*
markdown
numpy==1.24.*
@ -16,7 +16,7 @@ safetensors==0.4.0
scipy
sentencepiece
tensorboard
transformers==4.34.*
transformers==4.35.*
tqdm
wandb

4
requirements_cpu_only_noavx2.txt
View File

@ -2,7 +2,7 @@ accelerate==0.24.*
colorama
datasets
einops
exllamav2==0.0.6
exllamav2==0.0.7
gradio==3.50.*
markdown
numpy==1.24.*
@ -16,7 +16,7 @@ safetensors==0.4.0
scipy
sentencepiece
tensorboard
transformers==4.34.*
transformers==4.35.*
tqdm
wandb

22
requirements_noavx2.txt
View File

@ -2,7 +2,7 @@ accelerate==0.24.*
colorama
datasets
einops
exllamav2==0.0.6; platform_system != "Darwin" and platform_machine != "x86_64"
exllamav2==0.0.7; platform_system != "Darwin" and platform_machine != "x86_64"
gradio==3.50.*
markdown
numpy==1.24.*
@ -16,7 +16,7 @@ safetensors==0.4.0
scipy
sentencepiece
tensorboard
transformers==4.34.*
transformers==4.35.*
tqdm
wandb
@ -53,14 +53,14 @@ https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
https://github.com/jllllll/exllama/releases/download/0.0.18/exllama-0.0.18+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp38-cp38-win_amd64.whl; platform_system == "Windows" and python_version == "3.8"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
https://github.com/turboderp/exllamav2/releases/download/v0.0.6/exllamav2-0.0.6+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp39-cp39-win_amd64.whl; platform_system == "Windows" and python_version == "3.9"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp38-cp38-win_amd64.whl; platform_system == "Windows" and python_version == "3.8"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp310-cp310-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.10"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
https://github.com/turboderp/exllamav2/releases/download/v0.0.7/exllamav2-0.0.7+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
https://github.com/bdashore3/flash-attention/releases/download/2.3.2-2/flash_attn-2.3.2+cu122-cp311-cp311-win_amd64.whl; platform_system == "Windows" and python_version == "3.11"
https://github.com/bdashore3/flash-attention/releases/download/2.3.2-2/flash_attn-2.3.2+cu122-cp310-cp310-win_amd64.whl; platform_system == "Windows" and python_version == "3.10"
https://github.com/Dao-AILab/flash-attention/releases/download/v2.3.2/flash_attn-2.3.2+cu122torch2.1cxx11abiFALSE-cp311-cp311-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.11"
@ -84,4 +84,4 @@ https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp39-cp39-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.9"
https://github.com/jllllll/GPTQ-for-LLaMa-CUDA/releases/download/0.1.1/gptq_for_llama-0.1.1+cu121-cp38-cp38-linux_x86_64.whl; platform_system == "Linux" and platform_machine == "x86_64" and python_version == "3.8"
https://github.com/jllllll/ctransformers-cuBLAS-wheels/releases/download/AVX/ctransformers-0.2.27+cu121-py3-none-any.whl
autoawq==0.1.4; platform_system == "Linux" or platform_system == "Windows"
autoawq==0.1.6; platform_system == "Linux" or platform_system == "Windows"

4
requirements_nowheels.txt
View File

@ -2,7 +2,7 @@ accelerate==0.24.*
colorama
datasets
einops
exllamav2==0.0.6
exllamav2==0.0.7
gradio==3.50.*
markdown
numpy==1.24.*
@ -16,7 +16,7 @@ safetensors==0.4.0
scipy
sentencepiece
tensorboard
transformers==4.34.*
transformers==4.35.*
tqdm
wandb