mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-10-01 01:26:03 -04:00
Merge branch 'main' into Brawlence-main
This commit is contained in:
oobabooga
commit
e07c9e3093
29
.gitignore
vendored
29
.gitignore
vendored
@ -1,26 +1,21 @@
|
||||
cache/*
|
||||
characters/*
|
||||
extensions/silero_tts/outputs/*
|
||||
extensions/elevenlabs_tts/outputs/*
|
||||
extensions/sd_api_pictures/outputs/*
|
||||
logs/*
|
||||
loras/*
|
||||
models/*
|
||||
softprompts/*
|
||||
torch-dumps/*
|
||||
cache
|
||||
characters
|
||||
training/datasets
|
||||
extensions/silero_tts/outputs
|
||||
extensions/elevenlabs_tts/outputs
|
||||
extensions/sd_api_pictures/outputs
|
||||
logs
|
||||
loras
|
||||
models
|
||||
softprompts
|
||||
torch-dumps
|
||||
*pycache*
|
||||
*/*pycache*
|
||||
*/*/pycache*
|
||||
venv/
|
||||
.venv/
|
||||
repositories
|
||||
|
||||
settings.json
|
||||
img_bot*
|
||||
img_me*
|
||||
|
||||
!characters/Example.json
|
||||
!characters/Example.png
|
||||
!loras/place-your-loras-here.txt
|
||||
!models/place-your-models-here.txt
|
||||
!softprompts/place-your-softprompts-here.txt
|
||||
!torch-dumps/place-your-pt-models-here.txt
|
||||
|
||||
22
README.md
22
README.md
@ -27,7 +27,7 @@ Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.
|
||||
* [FlexGen offload](https://github.com/oobabooga/text-generation-webui/wiki/FlexGen).
|
||||
* [DeepSpeed ZeRO-3 offload](https://github.com/oobabooga/text-generation-webui/wiki/DeepSpeed).
|
||||
* Get responses via API, [with](https://github.com/oobabooga/text-generation-webui/blob/main/api-example-streaming.py) or [without](https://github.com/oobabooga/text-generation-webui/blob/main/api-example.py) streaming.
|
||||
* [LLaMA model, including 4-bit mode](https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model).
|
||||
* [LLaMA model, including 4-bit GPTQ support](https://github.com/oobabooga/text-generation-webui/wiki/LLaMA-model).
|
||||
* [RWKV model](https://github.com/oobabooga/text-generation-webui/wiki/RWKV-model).
|
||||
* [Supports LoRAs](https://github.com/oobabooga/text-generation-webui/wiki/Using-LoRAs).
|
||||
* Supports softprompts.
|
||||
@ -84,10 +84,6 @@ pip install -r requirements.txt
|
||||
>
|
||||
> For bitsandbytes and `--load-in-8bit` to work on Linux/WSL, this dirty fix is currently necessary: https://github.com/oobabooga/text-generation-webui/issues/400#issuecomment-1474876859
|
||||
|
||||
### Alternative: native Windows installation
|
||||
|
||||
As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
|
||||
|
||||
### Alternative: one-click installers
|
||||
|
||||
[oobabooga-windows.zip](https://github.com/oobabooga/one-click-installers/archive/refs/heads/oobabooga-windows.zip)
|
||||
@ -101,7 +97,13 @@ Just download the zip above, extract it, and double click on "install". The web
|
||||
|
||||
Source codes: https://github.com/oobabooga/one-click-installers
|
||||
|
||||
This method lags behind the newest developments and does not support 8-bit mode on Windows without additional set up: https://github.com/oobabooga/text-generation-webui/issues/147#issuecomment-1456040134, https://github.com/oobabooga/text-generation-webui/issues/20#issuecomment-1411650652
|
||||
> **Note**
|
||||
>
|
||||
> To get 8-bit and 4-bit models working in your 1-click Windows installation, you can use the [one-click-bandaid](https://github.com/ClayShoaf/oobabooga-one-click-bandaid).
|
||||
|
||||
### Alternative: native Windows installation
|
||||
|
||||
As an alternative to the recommended WSL method, you can install the web UI natively on Windows using this guide. It will be a lot harder and the performance may be slower: [Installation instructions for human beings](https://github.com/oobabooga/text-generation-webui/wiki/Installation-instructions-for-human-beings).
|
||||
|
||||
### Alternative: Docker
|
||||
|
||||
@ -174,10 +176,10 @@ Optionally, you can use the following command-line flags:
|
||||
| `--cai-chat` | Launch the web UI in chat mode with a style similar to Character.AI's. If the file `img_bot.png` or `img_bot.jpg` exists in the same folder as server.py, this image will be used as the bot's profile picture. Similarly, `img_me.png` or `img_me.jpg` will be used as your profile picture. |
|
||||
| `--cpu` | Use the CPU to generate text.|
|
||||
| `--load-in-8bit` | Load the model with 8-bit precision.|
|
||||
| `--load-in-4bit` | DEPRECATED: use `--gptq-bits 4` instead. |
|
||||
| `--gptq-bits GPTQ_BITS` | GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8 (bit) are supported. Currently only works with LLaMA and OPT. |
|
||||
| `--gptq-model-type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported. |
|
||||
| `--gptq-pre-layer GPTQ_PRE_LAYER` | GPTQ: The number of layers to preload. |
|
||||
| `--wbits WBITS` | GPTQ: Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
|
||||
| `--model_type MODEL_TYPE` | GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported. |
|
||||
| `--groupsize GROUPSIZE` | GPTQ: Group size. |
|
||||
| `--pre_layer PRE_LAYER` | GPTQ: The number of layers to preload. |
|
||||
| `--bf16` | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
|
||||
| `--auto-devices` | Automatically split the model across the available GPU(s) and CPU.|
|
||||
| `--disk` | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
|
||||
|
||||
@ -23,3 +23,9 @@ div.svelte-362y77>*, div.svelte-362y77>.form>* {
|
||||
.pending.svelte-1ed2p3z {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
#extensions {
|
||||
padding: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
|
||||
10
css/main.css
10
css/main.css
@ -54,3 +54,13 @@ ol li p, ul li p {
|
||||
.gradio-container-3-18-0 .prose * h1, h2, h3, h4 {
|
||||
color: white;
|
||||
}
|
||||
|
||||
.gradio-container {
|
||||
max-width: 100% !important;
|
||||
padding-top: 0 !important;
|
||||
}
|
||||
|
||||
#extensions {
|
||||
padding: 15px;
|
||||
padding: 15px;
|
||||
}
|
||||
|
||||
@ -11,7 +11,7 @@ let extensions = document.getElementById('extensions');
|
||||
main_parent.addEventListener('click', function(e) {
|
||||
// Check if the main element is visible
|
||||
if (main.offsetHeight > 0 && main.offsetWidth > 0) {
|
||||
extensions.style.display = 'block';
|
||||
extensions.style.display = 'flex';
|
||||
} else {
|
||||
extensions.style.display = 'none';
|
||||
}
|
||||
|
||||
@ -116,10 +116,11 @@ def get_download_links_from_huggingface(model, branch):
|
||||
|
||||
is_pytorch = re.match("(pytorch|adapter)_model.*\.bin", fname)
|
||||
is_safetensors = re.match("model.*\.safetensors", fname)
|
||||
is_pt = re.match(".*\.pt", fname)
|
||||
is_tokenizer = re.match("tokenizer.*\.model", fname)
|
||||
is_text = re.match(".*\.(txt|json|py)", fname) or is_tokenizer
|
||||
is_text = re.match(".*\.(txt|json|py|md)", fname) or is_tokenizer
|
||||
|
||||
if any((is_pytorch, is_safetensors, is_text, is_tokenizer)):
|
||||
if any((is_pytorch, is_safetensors, is_pt, is_tokenizer, is_text)):
|
||||
if is_text:
|
||||
links.append(f"https://huggingface.co/{model}/resolve/{branch}/{fname}")
|
||||
classifications.append('text')
|
||||
@ -132,7 +133,8 @@ def get_download_links_from_huggingface(model, branch):
|
||||
elif is_pytorch:
|
||||
has_pytorch = True
|
||||
classifications.append('pytorch')
|
||||
|
||||
elif is_pt:
|
||||
classifications.append('pt')
|
||||
|
||||
cursor = base64.b64encode(f'{{"file_name":"{dict[-1]["path"]}"}}'.encode()) + b':50'
|
||||
cursor = base64.b64encode(cursor)
|
||||
|
||||
@ -1,8 +1,9 @@
|
||||
import json
|
||||
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
|
||||
from threading import Thread
|
||||
|
||||
from modules import shared
|
||||
from modules.text_generation import generate_reply, encode
|
||||
import json
|
||||
from modules.text_generation import encode, generate_reply
|
||||
|
||||
params = {
|
||||
'port': 5000,
|
||||
@ -87,5 +88,5 @@ def run_server():
|
||||
print(f'Starting KoboldAI compatible api at http://{server_addr[0]}:{server_addr[1]}/api')
|
||||
server.serve_forever()
|
||||
|
||||
def ui():
|
||||
def setup():
|
||||
Thread(target=run_server, daemon=True).start()
|
||||
|
||||
@ -14,18 +14,21 @@ import opt
|
||||
|
||||
|
||||
def load_quantized(model_name):
|
||||
if not shared.args.gptq_model_type:
|
||||
if not shared.args.model_type:
|
||||
# Try to determine model type from model name
|
||||
model_type = model_name.split('-')[0].lower()
|
||||
if model_type not in ('llama', 'opt'):
|
||||
print("Can't determine model type from model name. Please specify it manually using --gptq-model-type "
|
||||
if model_name.lower().startswith(('llama', 'alpaca')):
|
||||
model_type = 'llama'
|
||||
elif model_name.lower().startswith(('opt', 'galactica')):
|
||||
model_type = 'opt'
|
||||
else:
|
||||
print("Can't determine model type from model name. Please specify it manually using --model_type "
|
||||
"argument")
|
||||
exit()
|
||||
else:
|
||||
model_type = shared.args.gptq_model_type.lower()
|
||||
model_type = shared.args.model_type.lower()
|
||||
|
||||
if model_type == 'llama':
|
||||
if not shared.args.gptq_pre_layer:
|
||||
if not shared.args.pre_layer:
|
||||
load_quant = llama.load_quant
|
||||
else:
|
||||
load_quant = llama_inference_offload.load_quant
|
||||
@ -35,33 +38,44 @@ def load_quantized(model_name):
|
||||
print("Unknown pre-quantized model type specified. Only 'llama' and 'opt' are supported")
|
||||
exit()
|
||||
|
||||
# Now we are going to try to locate the quantized model file.
|
||||
path_to_model = Path(f'models/{model_name}')
|
||||
if path_to_model.name.lower().startswith('llama-7b'):
|
||||
pt_model = f'llama-7b-{shared.args.gptq_bits}bit.pt'
|
||||
elif path_to_model.name.lower().startswith('llama-13b'):
|
||||
pt_model = f'llama-13b-{shared.args.gptq_bits}bit.pt'
|
||||
elif path_to_model.name.lower().startswith('llama-30b'):
|
||||
pt_model = f'llama-30b-{shared.args.gptq_bits}bit.pt'
|
||||
elif path_to_model.name.lower().startswith('llama-65b'):
|
||||
pt_model = f'llama-65b-{shared.args.gptq_bits}bit.pt'
|
||||
else:
|
||||
pt_model = f'{model_name}-{shared.args.gptq_bits}bit.pt'
|
||||
|
||||
# Try to find the .pt both in models/ and in the subfolder
|
||||
found_pts = list(path_to_model.glob("*.pt"))
|
||||
found_safetensors = list(path_to_model.glob("*.safetensors"))
|
||||
pt_path = None
|
||||
for path in [Path(p) for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
|
||||
|
||||
if len(found_pts) == 1:
|
||||
pt_path = found_pts[0]
|
||||
elif len(found_safetensors) == 1:
|
||||
pt_path = found_safetensors[0]
|
||||
else:
|
||||
if path_to_model.name.lower().startswith('llama-7b'):
|
||||
pt_model = f'llama-7b-{shared.args.wbits}bit'
|
||||
elif path_to_model.name.lower().startswith('llama-13b'):
|
||||
pt_model = f'llama-13b-{shared.args.wbits}bit'
|
||||
elif path_to_model.name.lower().startswith('llama-30b'):
|
||||
pt_model = f'llama-30b-{shared.args.wbits}bit'
|
||||
elif path_to_model.name.lower().startswith('llama-65b'):
|
||||
pt_model = f'llama-65b-{shared.args.wbits}bit'
|
||||
else:
|
||||
pt_model = f'{model_name}-{shared.args.wbits}bit'
|
||||
|
||||
# Try to find the .safetensors or .pt both in models/ and in the subfolder
|
||||
for path in [Path(p+ext) for ext in ['.safetensors', '.pt'] for p in [f"models/{pt_model}", f"{path_to_model}/{pt_model}"]]:
|
||||
if path.exists():
|
||||
print(f"Found {path}")
|
||||
pt_path = path
|
||||
break
|
||||
|
||||
if not pt_path:
|
||||
print(f"Could not find {pt_model}, exiting...")
|
||||
print("Could not find the quantized model in .pt or .safetensors format, exiting...")
|
||||
exit()
|
||||
|
||||
# qwopqwop200's offload
|
||||
if shared.args.gptq_pre_layer:
|
||||
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits, shared.args.gptq_pre_layer)
|
||||
if shared.args.pre_layer:
|
||||
model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize, shared.args.pre_layer)
|
||||
else:
|
||||
model = load_quant(str(path_to_model), str(pt_path), shared.args.gptq_bits)
|
||||
model = load_quant(str(path_to_model), str(pt_path), shared.args.wbits, shared.args.groupsize)
|
||||
|
||||
# accelerate offload (doesn't work properly)
|
||||
if shared.args.gpu_memory:
|
||||
|
||||
@ -1,22 +1,43 @@
|
||||
from pathlib import Path
|
||||
|
||||
import torch
|
||||
|
||||
import modules.shared as shared
|
||||
from modules.models import load_model
|
||||
from modules.text_generation import clear_torch_cache
|
||||
|
||||
|
||||
def reload_model():
|
||||
shared.model = shared.tokenizer = None
|
||||
clear_torch_cache()
|
||||
shared.model, shared.tokenizer = load_model(shared.model_name)
|
||||
|
||||
def add_lora_to_model(lora_name):
|
||||
|
||||
from peft import PeftModel
|
||||
|
||||
# Is there a more efficient way of returning to the base model?
|
||||
if lora_name == "None":
|
||||
print("Reloading the model to remove the LoRA...")
|
||||
shared.model, shared.tokenizer = load_model(shared.model_name)
|
||||
else:
|
||||
# Why doesn't this work in 16-bit mode?
|
||||
print(f"Adding the LoRA {lora_name} to the model...")
|
||||
# If a LoRA had been previously loaded, or if we want
|
||||
# to unload a LoRA, reload the model
|
||||
if shared.lora_name != "None" or lora_name == "None":
|
||||
reload_model()
|
||||
shared.lora_name = lora_name
|
||||
|
||||
if lora_name != "None":
|
||||
print(f"Adding the LoRA {lora_name} to the model...")
|
||||
params = {}
|
||||
if not shared.args.cpu:
|
||||
params['dtype'] = shared.model.dtype
|
||||
if hasattr(shared.model, "hf_device_map"):
|
||||
params['device_map'] = {"base_model.model."+k: v for k, v in shared.model.hf_device_map.items()}
|
||||
elif shared.args.load_in_8bit:
|
||||
params['device_map'] = {'': 0}
|
||||
#params['dtype'] = shared.model.dtype
|
||||
|
||||
shared.model = PeftModel.from_pretrained(shared.model, Path(f"loras/{lora_name}"), **params)
|
||||
if not shared.args.load_in_8bit and not shared.args.cpu:
|
||||
shared.model.half()
|
||||
if not hasattr(shared.model, "hf_device_map"):
|
||||
if torch.has_mps:
|
||||
device = torch.device('mps')
|
||||
shared.model = shared.model.to(device)
|
||||
else:
|
||||
shared.model = shared.model.cuda()
|
||||
|
||||
@ -45,11 +45,11 @@ class RWKVModel:
|
||||
token_stop = token_stop
|
||||
)
|
||||
|
||||
return context+self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
|
||||
return self.pipeline.generate(context, token_count=token_count, args=args, callback=callback)
|
||||
|
||||
def generate_with_streaming(self, **kwargs):
|
||||
with Iteratorize(self.generate, kwargs, callback=None) as generator:
|
||||
reply = kwargs['context']
|
||||
reply = ''
|
||||
for token in generator:
|
||||
reply += token
|
||||
yield reply
|
||||
|
||||
@ -11,23 +11,21 @@ import modules.shared as shared
|
||||
# Copied from https://github.com/PygmalionAI/gradio-ui/
|
||||
class _SentinelTokenStoppingCriteria(transformers.StoppingCriteria):
|
||||
|
||||
def __init__(self, sentinel_token_ids: torch.LongTensor,
|
||||
starting_idx: int):
|
||||
def __init__(self, sentinel_token_ids: list[torch.LongTensor], starting_idx: int):
|
||||
transformers.StoppingCriteria.__init__(self)
|
||||
self.sentinel_token_ids = sentinel_token_ids
|
||||
self.starting_idx = starting_idx
|
||||
|
||||
def __call__(self, input_ids: torch.LongTensor,
|
||||
_scores: torch.FloatTensor) -> bool:
|
||||
def __call__(self, input_ids: torch.LongTensor, _scores: torch.FloatTensor) -> bool:
|
||||
for sample in input_ids:
|
||||
trimmed_sample = sample[self.starting_idx:]
|
||||
# Can't unfold, output is still too tiny. Skip.
|
||||
if trimmed_sample.shape[-1] < self.sentinel_token_ids.shape[-1]:
|
||||
continue
|
||||
|
||||
for window in trimmed_sample.unfold(
|
||||
0, self.sentinel_token_ids.shape[-1], 1):
|
||||
if torch.all(torch.eq(self.sentinel_token_ids, window)):
|
||||
for i in range(len(self.sentinel_token_ids)):
|
||||
# Can't unfold, output is still too tiny. Skip.
|
||||
if trimmed_sample.shape[-1] < self.sentinel_token_ids[i].shape[-1]:
|
||||
continue
|
||||
for window in trimmed_sample.unfold(0, self.sentinel_token_ids[i].shape[-1], 1):
|
||||
if torch.all(torch.eq(self.sentinel_token_ids[i][0], window)):
|
||||
return True
|
||||
return False
|
||||
|
||||
|
||||
@ -33,11 +33,13 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
|
||||
i = len(shared.history['internal'])-1
|
||||
while i >= 0 and len(encode(''.join(rows), max_new_tokens)[0]) < max_length:
|
||||
rows.insert(1, f"{name2}: {shared.history['internal'][i][1].strip()}\n")
|
||||
if not (shared.history['internal'][i][0] == '<|BEGIN-VISIBLE-CHAT|>'):
|
||||
rows.insert(1, f"{name1}: {shared.history['internal'][i][0].strip()}\n")
|
||||
prev_user_input = shared.history['internal'][i][0]
|
||||
if len(prev_user_input) > 0 and prev_user_input != '<|BEGIN-VISIBLE-CHAT|>':
|
||||
rows.insert(1, f"{name1}: {prev_user_input.strip()}\n")
|
||||
i -= 1
|
||||
|
||||
if not impersonate:
|
||||
if len(user_input) > 0:
|
||||
rows.append(f"{name1}: {user_input}\n")
|
||||
rows.append(apply_extensions(f"{name2}:", "bot_prefix"))
|
||||
limit = 3
|
||||
@ -51,41 +53,31 @@ def generate_chat_prompt(user_input, max_new_tokens, name1, name2, context, chat
|
||||
prompt = ''.join(rows)
|
||||
return prompt
|
||||
|
||||
def extract_message_from_reply(question, reply, name1, name2, check, impersonate=False):
|
||||
def extract_message_from_reply(reply, name1, name2, check):
|
||||
next_character_found = False
|
||||
|
||||
asker = name1 if not impersonate else name2
|
||||
replier = name2 if not impersonate else name1
|
||||
|
||||
previous_idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", question)]
|
||||
idx = [m.start() for m in re.finditer(f"(^|\n){re.escape(replier)}:", reply)]
|
||||
idx = idx[max(len(previous_idx)-1, 0)]
|
||||
|
||||
if not impersonate:
|
||||
reply = reply[idx + 1 + len(apply_extensions(f"{replier}:", "bot_prefix")):]
|
||||
else:
|
||||
reply = reply[idx + 1 + len(f"{replier}:"):]
|
||||
|
||||
if check:
|
||||
lines = reply.split('\n')
|
||||
reply = lines[0].strip()
|
||||
if len(lines) > 1:
|
||||
next_character_found = True
|
||||
else:
|
||||
idx = reply.find(f"\n{asker}:")
|
||||
for string in [f"\n{name1}:", f"\n{name2}:"]:
|
||||
idx = reply.find(string)
|
||||
if idx != -1:
|
||||
reply = reply[:idx]
|
||||
next_character_found = True
|
||||
reply = fix_newlines(reply)
|
||||
|
||||
# If something like "\nYo" is generated just before "\nYou:"
|
||||
# is completed, trim it
|
||||
next_turn = f"\n{asker}:"
|
||||
for j in range(len(next_turn)-1, 0, -1):
|
||||
if reply[-j:] == next_turn[:j]:
|
||||
if not next_character_found:
|
||||
for string in [f"\n{name1}:", f"\n{name2}:"]:
|
||||
for j in range(len(string)-1, 0, -1):
|
||||
if reply[-j:] == string[:j]:
|
||||
reply = reply[:-j]
|
||||
break
|
||||
|
||||
reply = fix_newlines(reply)
|
||||
return reply, next_character_found
|
||||
|
||||
def stop_everything_event():
|
||||
@ -125,12 +117,13 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
|
||||
yield shared.history['visible']+[[visible_text, shared.processing_message]]
|
||||
|
||||
# Generate
|
||||
reply = ''
|
||||
cumulative_reply = ''
|
||||
for i in range(chat_generation_attempts):
|
||||
for reply in generate_reply(f"{prompt}{' ' if len(reply) > 0 else ''}{reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_string=f"\n{name1}:"):
|
||||
for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
|
||||
reply = cumulative_reply + reply
|
||||
|
||||
# Extracting the reply
|
||||
reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check)
|
||||
reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
|
||||
visible_reply = re.sub("(<USER>|<user>|{{user}})", name1_original, reply)
|
||||
visible_reply = apply_extensions(visible_reply, "output")
|
||||
if shared.args.chat:
|
||||
@ -152,6 +145,8 @@ def chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical
|
||||
if next_character_found:
|
||||
break
|
||||
|
||||
cumulative_reply = reply
|
||||
|
||||
yield shared.history['visible']
|
||||
|
||||
def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
|
||||
@ -162,15 +157,20 @@ def impersonate_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typ
|
||||
|
||||
prompt = generate_chat_prompt(text, max_new_tokens, name1, name2, context, chat_prompt_size, impersonate=True)
|
||||
|
||||
reply = ''
|
||||
# Yield *Is typing...*
|
||||
yield shared.processing_message
|
||||
|
||||
cumulative_reply = ''
|
||||
for i in range(chat_generation_attempts):
|
||||
for reply in generate_reply(prompt+reply, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_string=f"\n{name2}:"):
|
||||
reply, next_character_found = extract_message_from_reply(prompt, reply, name1, name2, check, impersonate=True)
|
||||
for reply in generate_reply(f"{prompt}{' ' if len(cumulative_reply) > 0 else ''}{cumulative_reply}", max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=eos_token, stopping_strings=[f"\n{name1}:", f"\n{name2}:"]):
|
||||
reply = cumulative_reply + reply
|
||||
reply, next_character_found = extract_message_from_reply(reply, name1, name2, check)
|
||||
yield reply
|
||||
if next_character_found:
|
||||
break
|
||||
|
||||
cumulative_reply = reply
|
||||
|
||||
yield reply
|
||||
|
||||
def cai_chatbot_wrapper(text, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, name1, name2, context, check, chat_prompt_size, chat_generation_attempts=1):
|
||||
|
||||
@ -7,6 +7,7 @@ import modules.shared as shared
|
||||
|
||||
state = {}
|
||||
available_extensions = []
|
||||
setup_called = False
|
||||
|
||||
def load_extensions():
|
||||
global state
|
||||
@ -39,6 +40,8 @@ def apply_extensions(text, typ):
|
||||
return text
|
||||
|
||||
def create_extensions_block():
|
||||
global setup_called
|
||||
|
||||
# Updating the default values
|
||||
for extension, name in iterator():
|
||||
if hasattr(extension, 'params'):
|
||||
@ -47,10 +50,21 @@ def create_extensions_block():
|
||||
if _id in shared.settings:
|
||||
extension.params[param] = shared.settings[_id]
|
||||
|
||||
# Creating the extension ui elements
|
||||
if len(state) > 0:
|
||||
with gr.Box(elem_id="extensions"):
|
||||
gr.Markdown("Extensions")
|
||||
should_display_ui = False
|
||||
|
||||
# Running setup function
|
||||
if not setup_called:
|
||||
for extension, name in iterator():
|
||||
if hasattr(extension, "setup"):
|
||||
extension.setup()
|
||||
if hasattr(extension, "ui"):
|
||||
should_display_ui = True
|
||||
setup_called = True
|
||||
|
||||
# Creating the extension ui elements
|
||||
if should_display_ui:
|
||||
with gr.Column(elem_id="extensions"):
|
||||
for extension, name in iterator():
|
||||
gr.Markdown(f"\n### {name}")
|
||||
if hasattr(extension, "ui"):
|
||||
extension.ui()
|
||||
|
||||
@ -142,7 +142,9 @@ def generate_chat_html(history, name1, name2, character):
|
||||
</div>
|
||||
"""
|
||||
|
||||
if not (i == len(history)-1 and len(row[0]) == 0):
|
||||
if len(row[0]) == 0: # don't display empty user messages
|
||||
continue
|
||||
|
||||
output += f"""
|
||||
<div class="message">
|
||||
<div class="circle-you">
|
||||
|
||||
@ -44,7 +44,7 @@ def load_model(model_name):
|
||||
shared.is_RWKV = model_name.lower().startswith('rwkv-')
|
||||
|
||||
# Default settings
|
||||
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.gptq_bits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
|
||||
if not any([shared.args.cpu, shared.args.load_in_8bit, shared.args.wbits, shared.args.auto_devices, shared.args.disk, shared.args.gpu_memory is not None, shared.args.cpu_memory is not None, shared.args.deepspeed, shared.args.flexgen, shared.is_RWKV]):
|
||||
if any(size in shared.model_name.lower() for size in ('13b', '20b', '30b')):
|
||||
model = AutoModelForCausalLM.from_pretrained(Path(f"models/{shared.model_name}"), device_map='auto', load_in_8bit=True)
|
||||
else:
|
||||
@ -95,7 +95,7 @@ def load_model(model_name):
|
||||
return model, tokenizer
|
||||
|
||||
# Quantized model
|
||||
elif shared.args.gptq_bits > 0:
|
||||
elif shared.args.wbits > 0:
|
||||
from modules.GPTQ_loader import load_quantized
|
||||
|
||||
model = load_quantized(model_name)
|
||||
|
||||
@ -27,9 +27,9 @@ settings = {
|
||||
'max_new_tokens': 200,
|
||||
'max_new_tokens_min': 1,
|
||||
'max_new_tokens_max': 2000,
|
||||
'name1': 'Person 1',
|
||||
'name2': 'Person 2',
|
||||
'context': 'This is a conversation between two people.',
|
||||
'name1': 'You',
|
||||
'name2': 'Assistant',
|
||||
'context': 'This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.',
|
||||
'stop_at_newline': False,
|
||||
'chat_prompt_size': 2048,
|
||||
'chat_prompt_size_min': 0,
|
||||
@ -52,7 +52,8 @@ settings = {
|
||||
'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
|
||||
'^(gpt4chan|gpt-4chan|4chan)': '-----\n--- 865467536\nInput text\n--- 865467537\n',
|
||||
'(rosey|chip|joi)_.*_instruct.*': 'User: \n',
|
||||
'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>'
|
||||
'oasst-*': '<|prompter|>Write a story about future of AI development<|endoftext|><|assistant|>',
|
||||
'alpaca-*': "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction:\nWrite a poem about the transformers Python library. \nMention the word \"large language models\" in that poem.\n### Response:\n",
|
||||
},
|
||||
'lora_prompts': {
|
||||
'default': 'Common sense questions and answers\n\nQuestion: \nFactual answer:',
|
||||
@ -78,10 +79,15 @@ parser.add_argument('--chat', action='store_true', help='Launch the web UI in ch
|
||||
parser.add_argument('--cai-chat', action='store_true', help='Launch the web UI in chat mode with a style similar to Character.AI\'s. If the file img_bot.png or img_bot.jpg exists in the same folder as server.py, this image will be used as the bot\'s profile picture. Similarly, img_me.png or img_me.jpg will be used as your profile picture.')
|
||||
parser.add_argument('--cpu', action='store_true', help='Use the CPU to generate text.')
|
||||
parser.add_argument('--load-in-8bit', action='store_true', help='Load the model with 8-bit precision.')
|
||||
parser.add_argument('--load-in-4bit', action='store_true', help='DEPRECATED: use --gptq-bits 4 instead.')
|
||||
parser.add_argument('--gptq-bits', type=int, default=0, help='GPTQ: Load a pre-quantized model with specified precision. 2, 3, 4 and 8bit are supported. Currently only works with LLaMA and OPT.')
|
||||
parser.add_argument('--gptq-model-type', type=str, help='GPTQ: Model type of pre-quantized model. Currently only LLaMa and OPT are supported.')
|
||||
parser.add_argument('--gptq-pre-layer', type=int, default=0, help='GPTQ: The number of layers to preload.')
|
||||
|
||||
parser.add_argument('--gptq-bits', type=int, default=0, help='DEPRECATED: use --wbits instead.')
|
||||
parser.add_argument('--gptq-model-type', type=str, help='DEPRECATED: use --model_type instead.')
|
||||
parser.add_argument('--gptq-pre-layer', type=int, default=0, help='DEPRECATED: use --pre_layer instead.')
|
||||
parser.add_argument('--wbits', type=int, default=0, help='GPTQ: 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='GPTQ: Model type of pre-quantized model. Currently only LLaMA and OPT are supported.')
|
||||
parser.add_argument('--groupsize', type=int, default=-1, help='GPTQ: Group size.')
|
||||
parser.add_argument('--pre_layer', type=int, default=0, help='GPTQ: The number of layers to preload.')
|
||||
|
||||
parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
|
||||
parser.add_argument('--auto-devices', action='store_true', help='Automatically split the model across the available GPU(s) and CPU.')
|
||||
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.')
|
||||
@ -109,6 +115,8 @@ parser.add_argument('--verbose', action='store_true', help='Print the prompts to
|
||||
args = parser.parse_args()
|
||||
|
||||
# Provisional, this will be deleted later
|
||||
if args.load_in_4bit:
|
||||
print("Warning: --load-in-4bit is deprecated and will be removed. Use --gptq-bits 4 instead.\n")
|
||||
args.gptq_bits = 4
|
||||
deprecated_dict = {'gptq_bits': ['wbits', 0], 'gptq_model_type': ['model_type', None], 'gptq_pre_layer': ['prelayer', 0]}
|
||||
for k in deprecated_dict:
|
||||
if eval(f"args.{k}") != deprecated_dict[k][1]:
|
||||
print(f"Warning: --{k} is deprecated and will be removed. Use --{deprecated_dict[k][0]} instead.")
|
||||
exec(f"args.{deprecated_dict[k][0]} = args.{k}")
|
||||
|
||||
@ -99,25 +99,37 @@ def set_manual_seed(seed):
|
||||
if torch.cuda.is_available():
|
||||
torch.cuda.manual_seed_all(seed)
|
||||
|
||||
def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=None, stopping_string=None):
|
||||
def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typical_p, repetition_penalty, encoder_repetition_penalty, top_k, min_length, no_repeat_ngram_size, num_beams, penalty_alpha, length_penalty, early_stopping, seed, eos_token=None, stopping_strings=[]):
|
||||
clear_torch_cache()
|
||||
set_manual_seed(seed)
|
||||
t0 = time.time()
|
||||
|
||||
original_question = question
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
question = apply_extensions(question, "input")
|
||||
if shared.args.verbose:
|
||||
print(f"\n\n{question}\n--------------------\n")
|
||||
|
||||
# These models are not part of Hugging Face, so we handle them
|
||||
# separately and terminate the function call earlier
|
||||
if shared.is_RWKV:
|
||||
try:
|
||||
if shared.args.no_stream:
|
||||
reply = shared.model.generate(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k)
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
reply = original_question + apply_extensions(reply, "output")
|
||||
yield formatted_outputs(reply, shared.model_name)
|
||||
else:
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
yield formatted_outputs(question, shared.model_name)
|
||||
|
||||
# RWKV has proper streaming, which is very nice.
|
||||
# No need to generate 8 tokens at a time.
|
||||
for reply in shared.model.generate_with_streaming(context=question, token_count=max_new_tokens, temperature=temperature, top_p=top_p, top_k=top_k):
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
reply = original_question + apply_extensions(reply, "output")
|
||||
yield formatted_outputs(reply, shared.model_name)
|
||||
|
||||
except Exception:
|
||||
traceback.print_exc()
|
||||
finally:
|
||||
@ -127,12 +139,6 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
||||
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(input_ids[0])} tokens)")
|
||||
return
|
||||
|
||||
original_question = question
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
question = apply_extensions(question, "input")
|
||||
if shared.args.verbose:
|
||||
print(f"\n\n{question}\n--------------------\n")
|
||||
|
||||
input_ids = encode(question, max_new_tokens)
|
||||
original_input_ids = input_ids
|
||||
output = input_ids[0]
|
||||
@ -142,9 +148,8 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
||||
if eos_token is not None:
|
||||
eos_token_ids.append(int(encode(eos_token)[0][-1]))
|
||||
stopping_criteria_list = transformers.StoppingCriteriaList()
|
||||
if stopping_string is not None:
|
||||
# Copied from https://github.com/PygmalionAI/gradio-ui/blob/master/src/model.py
|
||||
t = encode(stopping_string, 0, add_special_tokens=False)
|
||||
if type(stopping_strings) is list and len(stopping_strings) > 0:
|
||||
t = [encode(string, 0, add_special_tokens=False) for string in stopping_strings]
|
||||
stopping_criteria_list.append(_SentinelTokenStoppingCriteria(sentinel_token_ids=t, starting_idx=len(input_ids[0])))
|
||||
|
||||
generate_params = {}
|
||||
@ -195,12 +200,10 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
||||
if shared.soft_prompt:
|
||||
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
|
||||
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
new_tokens = len(output) - len(input_ids[0])
|
||||
reply = decode(output[-new_tokens:])
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
reply = original_question + apply_extensions(reply, "output")
|
||||
else:
|
||||
reply = decode(output)
|
||||
|
||||
yield formatted_outputs(reply, shared.model_name)
|
||||
|
||||
@ -223,12 +226,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
||||
for output in generator:
|
||||
if shared.soft_prompt:
|
||||
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
|
||||
new_tokens = len(output) - len(input_ids[0])
|
||||
reply = decode(output[-new_tokens:])
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
reply = original_question + apply_extensions(reply, "output")
|
||||
else:
|
||||
reply = decode(output)
|
||||
|
||||
if output[-1] in eos_token_ids:
|
||||
break
|
||||
@ -244,12 +246,11 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
||||
output = shared.model.generate(**generate_params)[0]
|
||||
if shared.soft_prompt:
|
||||
output = torch.cat((input_ids[0], output[filler_input_ids.shape[1]:]))
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
|
||||
new_tokens = len(output) - len(original_input_ids[0])
|
||||
reply = decode(output[-new_tokens:])
|
||||
if not (shared.args.chat or shared.args.cai_chat):
|
||||
reply = original_question + apply_extensions(reply, "output")
|
||||
else:
|
||||
reply = decode(output)
|
||||
|
||||
if np.count_nonzero(np.isin(input_ids[0], eos_token_ids)) < np.count_nonzero(np.isin(output, eos_token_ids)):
|
||||
break
|
||||
@ -269,5 +270,5 @@ def generate_reply(question, max_new_tokens, do_sample, temperature, top_p, typi
|
||||
traceback.print_exc()
|
||||
finally:
|
||||
t1 = time.time()
|
||||
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens)")
|
||||
print(f"Output generated in {(t1-t0):.2f} seconds ({(len(output)-len(original_input_ids[0]))/(t1-t0):.2f} tokens/s, {len(output)-len(original_input_ids[0])} tokens, context {len(original_input_ids[0])})")
|
||||
return
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
accelerate==0.17.1
|
||||
bitsandbytes==0.37.1
|
||||
flexgen==0.1.7
|
||||
gradio==3.18.0
|
||||
gradio==3.23.0
|
||||
markdown
|
||||
numpy
|
||||
peft==0.2.0
|
||||
|
||||
31
server.py
31
server.py
@ -1,4 +1,3 @@
|
||||
import gc
|
||||
import io
|
||||
import json
|
||||
import re
|
||||
@ -8,7 +7,6 @@ import zipfile
|
||||
from pathlib import Path
|
||||
|
||||
import gradio as gr
|
||||
import torch
|
||||
|
||||
import modules.chat as chat
|
||||
import modules.extensions as extensions_module
|
||||
@ -17,7 +15,7 @@ import modules.ui as ui
|
||||
from modules.html_generator import generate_chat_html
|
||||
from modules.LoRA import add_lora_to_model
|
||||
from modules.models import load_model, load_soft_prompt
|
||||
from modules.text_generation import generate_reply
|
||||
from modules.text_generation import clear_torch_cache, generate_reply
|
||||
|
||||
# Loading custom settings
|
||||
settings_file = None
|
||||
@ -56,9 +54,7 @@ def load_model_wrapper(selected_model):
|
||||
if selected_model != shared.model_name:
|
||||
shared.model_name = selected_model
|
||||
shared.model = shared.tokenizer = None
|
||||
if not shared.args.cpu:
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
clear_torch_cache()
|
||||
shared.model, shared.tokenizer = load_model(shared.model_name)
|
||||
|
||||
return selected_model
|
||||
@ -75,13 +71,8 @@ def unload_model():
|
||||
print("Model weights unloaded.")
|
||||
|
||||
def load_lora_wrapper(selected_lora):
|
||||
shared.lora_name = selected_lora
|
||||
default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')]
|
||||
|
||||
if not shared.args.cpu:
|
||||
gc.collect()
|
||||
torch.cuda.empty_cache()
|
||||
add_lora_to_model(selected_lora)
|
||||
default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')]
|
||||
|
||||
return selected_lora, default_text
|
||||
|
||||
@ -258,14 +249,13 @@ else:
|
||||
shared.model_name = available_models[i]
|
||||
shared.model, shared.tokenizer = load_model(shared.model_name)
|
||||
if shared.args.lora:
|
||||
print(shared.args.lora)
|
||||
shared.lora_name = shared.args.lora
|
||||
add_lora_to_model(shared.lora_name)
|
||||
add_lora_to_model(shared.args.lora)
|
||||
|
||||
# Default UI settings
|
||||
default_preset = shared.settings['presets'][next((k for k in shared.settings['presets'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
|
||||
default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')]
|
||||
if default_text == '':
|
||||
if shared.lora_name != "None":
|
||||
default_text = shared.settings['lora_prompts'][next((k for k in shared.settings['lora_prompts'] if re.match(k.lower(), shared.lora_name.lower())), 'default')]
|
||||
else:
|
||||
default_text = shared.settings['prompts'][next((k for k in shared.settings['prompts'] if re.match(k.lower(), shared.model_name.lower())), 'default')]
|
||||
title ='Text generation web UI'
|
||||
description = '\n\n# Text generation lab\nGenerate text using Large Language Models.\n'
|
||||
@ -354,7 +344,7 @@ def create_interface():
|
||||
gen_events.append(shared.gradio['textbox'].submit(eval(function_call), shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
|
||||
gen_events.append(shared.gradio['Regenerate'].click(chat.regenerate_wrapper, shared.input_params, shared.gradio['display'], show_progress=shared.args.no_stream))
|
||||
gen_events.append(shared.gradio['Impersonate'].click(chat.impersonate_wrapper, shared.input_params, shared.gradio['textbox'], show_progress=shared.args.no_stream))
|
||||
shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events)
|
||||
shared.gradio['Stop'].click(chat.stop_everything_event, [], [], cancels=gen_events, queue=False)
|
||||
|
||||
shared.gradio['Copy last reply'].click(chat.send_last_reply_to_input, [], shared.gradio['textbox'], show_progress=shared.args.no_stream)
|
||||
shared.gradio['Replace last reply'].click(chat.replace_last_reply, [shared.gradio['textbox'], shared.gradio['name1'], shared.gradio['name2']], shared.gradio['display'], show_progress=shared.args.no_stream)
|
||||
@ -395,8 +385,10 @@ def create_interface():
|
||||
|
||||
elif shared.args.notebook:
|
||||
with gr.Tab("Text generation", elem_id="main"):
|
||||
with gr.Row():
|
||||
with gr.Column(scale=4):
|
||||
with gr.Tab('Raw'):
|
||||
shared.gradio['textbox'] = gr.Textbox(value=default_text, lines=25)
|
||||
shared.gradio['textbox'] = gr.Textbox(value=default_text, elem_id="textbox", lines=25)
|
||||
with gr.Tab('Markdown'):
|
||||
shared.gradio['markdown'] = gr.Markdown()
|
||||
with gr.Tab('HTML'):
|
||||
@ -405,6 +397,7 @@ def create_interface():
|
||||
with gr.Row():
|
||||
shared.gradio['Stop'] = gr.Button('Stop')
|
||||
shared.gradio['Generate'] = gr.Button('Generate')
|
||||
with gr.Column(scale=1):
|
||||
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'])
|
||||
|
||||
create_model_and_preset_menus()
|
||||
|
||||
@ -2,9 +2,9 @@
|
||||
"max_new_tokens": 200,
|
||||
"max_new_tokens_min": 1,
|
||||
"max_new_tokens_max": 2000,
|
||||
"name1": "Person 1",
|
||||
"name2": "Person 2",
|
||||
"context": "This is a conversation between two people.",
|
||||
"name1": "You",
|
||||
"name2": "Assistant",
|
||||
"context": "This is a conversation with your Assistant. The Assistant is very helpful and is eager to chat with you and answer your questions.",
|
||||
"stop_at_newline": false,
|
||||
"chat_prompt_size": 2048,
|
||||
"chat_prompt_size_min": 0,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user