mirror of
https://github.com/oobabooga/text-generation-webui.git
synced 2024-11-21 23:57:58 +01:00
lint
This commit is contained in:
oobabooga
parent
9b55d3a9f9
commit
e202190c4f
@ -23,7 +23,7 @@ from tqdm.contrib.concurrent import thread_map
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class ModelDownloader:
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def __init__(self, max_retries = 5):
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def __init__(self, max_retries=5):
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self.s = requests.Session()
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if max_retries:
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self.s.mount('https://cdn-lfs.huggingface.co', HTTPAdapter(max_retries=max_retries))
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@ -22,6 +22,7 @@ options = {
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'session_metadata': 'text-generation-webui',
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}
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def ui():
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settings = shared.settings.get("ngrok")
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if settings:
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@ -33,4 +34,3 @@ def ui():
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logging.info(f"Ingress established at: {tunnel.url()}")
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except ModuleNotFoundError:
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logging.error("===> ngrok library not found, please run `pip install -r extensions/ngrok/requirements.txt`")
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@ -41,7 +41,7 @@ class LogprobProcessor(LogitsProcessor):
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# XXX hack. should find the selected token and include the prob of that
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# ... but we just +1 here instead because we don't know it yet.
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top_values, top_indices = torch.topk(log_e_probabilities, k=self.logprobs + 1)
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top_tokens = [ decode(tok) for tok in top_indices[0] ]
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top_tokens = [decode(tok) for tok in top_indices[0]]
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self.token_alternatives = dict(zip(top_tokens, top_values[0].tolist()))
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return logits
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@ -50,7 +50,7 @@ def convert_logprobs_to_tiktoken(model, logprobs):
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try:
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encoder = tiktoken.encoding_for_model(model)
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# just pick the first one if it encodes to multiple tokens... 99.9% not required and maybe worse overall.
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return dict([ (encoder.decode([encoder.encode(token)[0]]), prob) for token, prob in logprobs.items() ])
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return dict([(encoder.decode([encoder.encode(token)[0]]), prob) for token, prob in logprobs.items()])
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except KeyError:
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# assume native tokens if we can't find the tokenizer
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return logprobs
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@ -220,16 +220,16 @@ def messages_to_prompt(body: dict, req_params: dict, max_tokens):
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if max_tokens > 0 and token_count + max_tokens > req_params['truncation_length']:
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err_msg = f"This model maximum context length is {req_params['truncation_length']} tokens. However, your messages resulted in over {token_count} tokens and max_tokens is {max_tokens}."
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print(f"Warning: ${err_msg}")
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#raise InvalidRequestError(message=err_msg)
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# raise InvalidRequestError(message=err_msg)
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return prompt, token_count
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def chat_completions(body: dict, is_legacy: bool=False) -> dict:
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def chat_completions(body: dict, is_legacy: bool = False) -> dict:
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# Chat Completions
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object_type = 'chat.completions'
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created_time = int(time.time())
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cmpl_id = "chatcmpl-%d" % (int(time.time()*1000000000))
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cmpl_id = "chatcmpl-%d" % (int(time.time() * 1000000000))
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resp_list = 'data' if is_legacy else 'choices'
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# common params
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@ -296,12 +296,12 @@ def chat_completions(body: dict, is_legacy: bool=False) -> dict:
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# generator
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def stream_chat_completions(body: dict, is_legacy: bool=False):
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def stream_chat_completions(body: dict, is_legacy: bool = False):
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# Chat Completions
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stream_object_type = 'chat.completions.chunk'
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created_time = int(time.time())
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cmpl_id = "chatcmpl-%d" % (int(time.time()*1000000000))
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cmpl_id = "chatcmpl-%d" % (int(time.time() * 1000000000))
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resp_list = 'data' if is_legacy else 'choices'
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# common params
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@ -342,7 +342,7 @@ def stream_chat_completions(body: dict, is_legacy: bool=False):
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if logprob_proc: # not official for chat yet
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top_logprobs = convert_logprobs_to_tiktoken(model=requested_model, logprobs=logprob_proc.token_alternatives)
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chunk[resp_list][0]["logprobs"] = {'top_logprobs': [top_logprobs]}
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#else:
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# else:
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# chunk[resp_list][0]["logprobs"] = None
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return chunk
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@ -380,7 +380,6 @@ def stream_chat_completions(body: dict, is_legacy: bool=False):
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yield chunk
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stop_reason = "stop"
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if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= max_tokens:
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stop_reason = "length"
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@ -396,12 +395,12 @@ def stream_chat_completions(body: dict, is_legacy: bool=False):
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yield chunk
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def completions(body: dict, is_legacy: bool=False):
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def completions(body: dict, is_legacy: bool = False):
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# Legacy
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# Text Completions
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object_type = 'text_completion'
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created_time = int(time.time())
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cmpl_id = "conv-%d" % (int(time.time()*1000000000))
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cmpl_id = "conv-%d" % (int(time.time() * 1000000000))
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resp_list = 'data' if is_legacy else 'choices'
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# ... encoded as a string, array of strings, array of tokens, or array of token arrays.
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@ -433,7 +432,7 @@ def completions(body: dict, is_legacy: bool=False):
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if token_count + max_tokens > req_params['truncation_length']:
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err_msg = f"The token count of your prompt ({token_count}) plus max_tokens ({max_tokens}) cannot exceed the model's context length ({req_params['truncation_length']})."
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#print(f"Warning: ${err_msg}")
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# print(f"Warning: ${err_msg}")
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raise InvalidRequestError(message=err_msg, param=max_tokens_str)
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req_params['echo'] = default(body, 'echo', req_params['echo'])
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@ -486,13 +485,13 @@ def completions(body: dict, is_legacy: bool=False):
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# generator
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def stream_completions(body: dict, is_legacy: bool=False):
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def stream_completions(body: dict, is_legacy: bool = False):
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# Legacy
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# Text Completions
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#object_type = 'text_completion'
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# object_type = 'text_completion'
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stream_object_type = 'text_completion.chunk'
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created_time = int(time.time())
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cmpl_id = "conv-%d" % (int(time.time()*1000000000))
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cmpl_id = "conv-%d" % (int(time.time() * 1000000000))
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resp_list = 'data' if is_legacy else 'choices'
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# ... encoded as a string, array of strings, array of tokens, or array of token arrays.
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@ -524,7 +523,7 @@ def stream_completions(body: dict, is_legacy: bool=False):
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if token_count + max_tokens > req_params['truncation_length']:
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err_msg = f"The token count of your prompt ({token_count}) plus max_tokens ({max_tokens}) cannot exceed the model's context length ({req_params['truncation_length']})."
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#print(f"Warning: ${err_msg}")
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# print(f"Warning: ${err_msg}")
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raise InvalidRequestError(message=err_msg, param=max_tokens_str)
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req_params['echo'] = default(body, 'echo', req_params['echo'])
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@ -583,7 +582,6 @@ def stream_completions(body: dict, is_legacy: bool=False):
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completion_token_count += len(encode(new_content)[0])
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yield chunk
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stop_reason = "stop"
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if token_count + completion_token_count >= req_params['truncation_length'] or completion_token_count >= max_tokens:
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stop_reason = "length"
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@ -41,10 +41,13 @@ default_req_params = {
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# 'requested_model' - temporarily used
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}
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def get_default_req_params():
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return copy.deepcopy(default_req_params)
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# little helper to get defaults if arg is present but None and should be the same type as default.
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def default(dic, key, default):
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val = dic.get(key, default)
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if type(val) != type(default):
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@ -59,6 +62,6 @@ def default(dic, key, default):
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val = default
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return val
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def clamp(value, minvalue, maxvalue):
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return max(minvalue, min(value, maxvalue))
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@ -8,9 +8,9 @@ from extensions.openai.errors import *
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from modules.text_generation import encode, generate_reply
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def edits(instruction: str, input: str, temperature = 1.0, top_p = 1.0) -> dict:
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def edits(instruction: str, input: str, temperature=1.0, top_p=1.0) -> dict:
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created_time = int(time.time()*1000)
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created_time = int(time.time() * 1000)
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# Request parameters
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req_params = get_default_req_params()
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@ -41,7 +41,7 @@ def edits(instruction: str, input: str, temperature = 1.0, top_p = 1.0) -> dict:
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instruction_template = instruct.get('context', '') + template[:template.find('<|bot-message|>')].rstrip(' ')
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if instruct['user']:
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stopping_strings.extend(['\n' + instruct['user'], instruct['user'] ])
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stopping_strings.extend(['\n' + instruct['user'], instruct['user']])
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except Exception as e:
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instruction_template = default_template
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@ -6,26 +6,30 @@ from extensions.openai.errors import *
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st_model = os.environ["OPENEDAI_EMBEDDING_MODEL"] if "OPENEDAI_EMBEDDING_MODEL" in os.environ else "all-mpnet-base-v2"
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embeddings_model = None
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def load_embedding_model(model):
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try:
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emb_model = SentenceTransformer(model)
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print(f"\nLoaded embedding model: {model}, max sequence length: {emb_model.max_seq_length}")
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except Exception as e:
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print(f"\nError: Failed to load embedding model: {model}")
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raise ServiceUnavailableError(f"Error: Failed to load embedding model: {model}", internal_message = repr(e))
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raise ServiceUnavailableError(f"Error: Failed to load embedding model: {model}", internal_message=repr(e))
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return emb_model
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def get_embeddings_model():
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global embeddings_model, st_model
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if st_model and not embeddings_model:
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embeddings_model = load_embedding_model(st_model) # lazy load the model
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return embeddings_model
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def get_embeddings_model_name():
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global st_model
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return st_model
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def embeddings(input: list, encoding_format: str):
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embeddings = get_embeddings_model().encode(input).tolist()
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@ -1,8 +1,9 @@
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class OpenAIError(Exception):
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def __init__(self, message = None, code = 500, internal_message = ''):
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def __init__(self, message=None, code=500, internal_message=''):
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self.message = message
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self.code = code
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self.internal_message = internal_message
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def __repr__(self):
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return "%s(message=%r, code=%d)" % (
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self.__class__.__name__,
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@ -10,10 +11,12 @@ class OpenAIError(Exception):
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self.code,
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)
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class InvalidRequestError(OpenAIError):
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def __init__(self, message, param, code = 400, error_type ='InvalidRequestError', internal_message = ''):
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def __init__(self, message, param, code=400, error_type='InvalidRequestError', internal_message=''):
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super(OpenAIError, self).__init__(message, code, error_type, internal_message)
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self.param = param
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def __repr__(self):
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return "%s(message=%r, code=%d, param=%s)" % (
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self.__class__.__name__,
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@ -22,6 +25,7 @@ class InvalidRequestError(OpenAIError):
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self.param,
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)
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class ServiceUnavailableError(OpenAIError):
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def __init__(self, message = None, code = 500, error_type ='ServiceUnavailableError', internal_message = ''):
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def __init__(self, message=None, code=500, error_type='ServiceUnavailableError', internal_message=''):
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super(OpenAIError, self).__init__(message, code, error_type, internal_message)
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@ -3,6 +3,7 @@ import time
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import requests
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from extensions.openai.errors import *
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def generations(prompt: str, size: str, response_format: str, n: int):
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# Stable Diffusion callout wrapper for txt2img
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# Low effort implementation for compatibility. With only "prompt" being passed and assuming DALL-E
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@ -15,7 +16,7 @@ def generations(prompt: str, size: str, response_format: str, n: int):
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# require changing the form data handling to accept multipart form data, also to properly support
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# url return types will require file management and a web serving files... Perhaps later!
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width, height = [ int(x) for x in size.split('x') ] # ignore the restrictions on size
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width, height = [int(x) for x in size.split('x')] # ignore the restrictions on size
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# to hack on better generation, edit default payload.
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payload = {
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@ -37,7 +38,7 @@ def generations(prompt: str, size: str, response_format: str, n: int):
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response = requests.post(url=sd_url, json=payload)
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r = response.json()
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if response.status_code != 200 or 'images' not in r:
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raise ServiceUnavailableError(r.get('detail', [{'msg': 'Unknown error calling Stable Diffusion'}])[0]['msg'], code = response.status_code)
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raise ServiceUnavailableError(r.get('detail', [{'msg': 'Unknown error calling Stable Diffusion'}])[0]['msg'], code=response.status_code)
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# r['parameters']...
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for b64_json in r['images']:
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if response_format == 'b64_json':
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@ -7,8 +7,10 @@ from modules.models_settings import (get_model_settings_from_yamls,
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from extensions.openai.embeddings import get_embeddings_model_name
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from extensions.openai.errors import *
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def get_current_model_list() -> list:
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return [ shared.model_name ] # The real chat/completions model, maybe "None"
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return [shared.model_name] # The real chat/completions model, maybe "None"
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def get_pseudo_model_list() -> list:
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return [ # these are expected by so much, so include some here as a dummy
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@ -16,6 +18,7 @@ def get_pseudo_model_list() -> list:
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'text-embedding-ada-002',
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]
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def load_model(model_name: str) -> dict:
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resp = {
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"id": model_name,
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@ -23,7 +26,7 @@ def load_model(model_name: str) -> dict:
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"owner": "self",
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"ready": True,
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}
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if model_name not in get_pseudo_model_list() + [ get_embeddings_model_name() ] + get_current_model_list(): # Real model only
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if model_name not in get_pseudo_model_list() + [get_embeddings_model_name()] + get_current_model_list(): # Real model only
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# No args. Maybe it works anyways!
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# TODO: hack some heuristics into args for better results
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@ -48,16 +51,16 @@ def load_model(model_name: str) -> dict:
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def list_models(is_legacy: bool = False) -> dict:
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# TODO: Lora's?
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all_model_list = get_current_model_list() + [ get_embeddings_model_name() ] + get_pseudo_model_list() + get_available_models()
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all_model_list = get_current_model_list() + [get_embeddings_model_name()] + get_pseudo_model_list() + get_available_models()
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models = {}
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if is_legacy:
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models = [{ "id": id, "object": "engine", "owner": "user", "ready": True } for id in all_model_list ]
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models = [{"id": id, "object": "engine", "owner": "user", "ready": True} for id in all_model_list]
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if not shared.model:
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models[0]['ready'] = False
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else:
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models = [{ "id": id, "object": "model", "owned_by": "user", "permission": [] } for id in all_model_list ]
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models = [{"id": id, "object": "model", "owned_by": "user", "permission": []} for id in all_model_list]
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resp = {
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"object": "list",
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@ -74,4 +77,3 @@ def model_info(model_name: str) -> dict:
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"owned_by": "user",
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"permission": []
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}
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@ -7,7 +7,7 @@ from extensions.openai.embeddings import get_embeddings_model
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moderations_disabled = False # return 0/false
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category_embeddings = None
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antonym_embeddings = None
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categories = [ "sexual", "hate", "harassment", "self-harm", "sexual/minors", "hate/threatening", "violence/graphic", "self-harm/intent", "self-harm/instructions", "harassment/threatening", "violence" ]
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categories = ["sexual", "hate", "harassment", "self-harm", "sexual/minors", "hate/threatening", "violence/graphic", "self-harm/intent", "self-harm/instructions", "harassment/threatening", "violence"]
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flag_threshold = 0.5
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@ -40,23 +40,22 @@ def moderations(input):
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embeddings_model = get_embeddings_model()
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if not embeddings_model or moderations_disabled:
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results['results'] = [{
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'categories': dict([ (C, False) for C in categories]),
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'category_scores': dict([ (C, 0.0) for C in categories]),
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'categories': dict([(C, False) for C in categories]),
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'category_scores': dict([(C, 0.0) for C in categories]),
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'flagged': False,
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}]
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return results
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category_embeddings = get_category_embeddings()
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# input, string or array
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if isinstance(input, str):
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input = [input]
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for in_str in input:
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for ine in embeddings_model.encode([in_str]).tolist():
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category_scores = dict([ (C, mod_score(category_embeddings[C], ine)) for C in categories ])
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category_flags = dict([ (C, bool(category_scores[C] > flag_threshold)) for C in categories ])
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category_scores = dict([(C, mod_score(category_embeddings[C], ine)) for C in categories])
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category_flags = dict([(C, bool(category_scores[C] > flag_threshold)) for C in categories])
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flagged = any(category_flags.values())
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results['results'].extend([{
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@ -22,6 +22,7 @@ params = {
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'port': int(os.environ.get('OPENEDAI_PORT')) if 'OPENEDAI_PORT' in os.environ else 5001,
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}
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class Handler(BaseHTTPRequestHandler):
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def send_access_control_headers(self):
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self.send_header("Access-Control-Allow-Origin", "*")
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@ -72,7 +73,7 @@ class Handler(BaseHTTPRequestHandler):
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if not no_debug:
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debug_msg(r_utf8)
|
||||
|
||||
def openai_error(self, message, code = 500, error_type = 'APIError', param = '', internal_message = ''):
|
||||
def openai_error(self, message, code=500, error_type='APIError', param='', internal_message=''):
|
||||
|
||||
error_resp = {
|
||||
'error': {
|
||||
@ -84,7 +85,7 @@ class Handler(BaseHTTPRequestHandler):
|
||||
}
|
||||
if internal_message:
|
||||
print(internal_message)
|
||||
#error_resp['internal_message'] = internal_message
|
||||
# error_resp['internal_message'] = internal_message
|
||||
|
||||
self.return_json(error_resp, code)
|
||||
|
||||
|
||||
@ -1,6 +1,7 @@
|
||||
from extensions.openai.utils import float_list_to_base64
|
||||
from modules.text_generation import encode, decode
|
||||
|
||||
|
||||
def token_count(prompt):
|
||||
tokens = encode(prompt)[0]
|
||||
|
||||
@ -11,8 +12,8 @@ def token_count(prompt):
|
||||
}
|
||||
|
||||
|
||||
def token_encode(input, encoding_format = ''):
|
||||
#if isinstance(input, list):
|
||||
def token_encode(input, encoding_format=''):
|
||||
# if isinstance(input, list):
|
||||
tokens = encode(input)[0]
|
||||
|
||||
return {
|
||||
@ -25,9 +26,9 @@ def token_encode(input, encoding_format = ''):
|
||||
|
||||
|
||||
def token_decode(tokens, encoding_format):
|
||||
#if isinstance(input, list):
|
||||
# if encoding_format == "base64":
|
||||
# tokens = base64_to_float_list(tokens)
|
||||
# if isinstance(input, list):
|
||||
# if encoding_format == "base64":
|
||||
# tokens = base64_to_float_list(tokens)
|
||||
output = decode(tokens)[0]
|
||||
|
||||
return {
|
||||
|
||||
@ -2,6 +2,7 @@ import os
|
||||
import base64
|
||||
import numpy as np
|
||||
|
||||
|
||||
def float_list_to_base64(float_list):
|
||||
# Convert the list to a float32 array that the OpenAPI client expects
|
||||
float_array = np.array(float_list, dtype="float32")
|
||||
@ -16,11 +17,13 @@ def float_list_to_base64(float_list):
|
||||
ascii_string = encoded_bytes.decode('ascii')
|
||||
return ascii_string
|
||||
|
||||
|
||||
def end_line(s):
|
||||
if s and s[-1] != '\n':
|
||||
s = s + '\n'
|
||||
return s
|
||||
|
||||
|
||||
def debug_msg(*args, **kwargs):
|
||||
if 'OPENEDAI_DEBUG' in os.environ:
|
||||
print(*args, **kwargs)
|
||||
@ -126,6 +126,8 @@ def input_modifier(string):
|
||||
return string
|
||||
|
||||
# Get and save the Stable Diffusion-generated picture
|
||||
|
||||
|
||||
def get_SD_pictures(description, character):
|
||||
|
||||
global params
|
||||
@ -186,6 +188,8 @@ def get_SD_pictures(description, character):
|
||||
|
||||
# TODO: how do I make the UI history ignore the resulting pictures (I don't want HTML to appear in history)
|
||||
# and replace it with 'text' for the purposes of logging?
|
||||
|
||||
|
||||
def output_modifier(string, state):
|
||||
"""
|
||||
This function is applied to the model outputs.
|
||||
|
||||
@ -113,7 +113,7 @@ def custom_generate_chat_prompt(user_input, state, **kwargs):
|
||||
if len(history['internal']) > params['chunk_count'] and user_input != '':
|
||||
chunks = []
|
||||
hist_size = len(history['internal'])
|
||||
for i in range(hist_size-1):
|
||||
for i in range(hist_size - 1):
|
||||
chunks.append(make_single_exchange(i))
|
||||
|
||||
add_chunks_to_collector(chunks, chat_collector)
|
||||
|
||||
@ -16,7 +16,7 @@ params = {
|
||||
}
|
||||
|
||||
|
||||
def do_stt(audio,whipser_model,whipser_language):
|
||||
def do_stt(audio, whipser_model, whipser_language):
|
||||
transcription = ""
|
||||
r = sr.Recognizer()
|
||||
|
||||
@ -33,10 +33,10 @@ def do_stt(audio,whipser_model,whipser_language):
|
||||
return transcription
|
||||
|
||||
|
||||
def auto_transcribe(audio, auto_submit,whipser_model,whipser_language):
|
||||
def auto_transcribe(audio, auto_submit, whipser_model, whipser_language):
|
||||
if audio is None:
|
||||
return "", ""
|
||||
transcription = do_stt(audio,whipser_model,whipser_language)
|
||||
transcription = do_stt(audio, whipser_model, whipser_language)
|
||||
if auto_submit:
|
||||
input_hijack.update({"state": True, "value": [transcription, transcription]})
|
||||
|
||||
@ -50,11 +50,11 @@ def ui():
|
||||
with gr.Row():
|
||||
with gr.Accordion("Settings", open=False):
|
||||
auto_submit = gr.Checkbox(label='Submit the transcribed audio automatically', value=params['auto_submit'])
|
||||
whipser_model = gr.Dropdown(label='Whisper Model', value=params['whipser_model'],choices=["tiny.en","base.en", "small.en","medium.en","tiny","base","small","medium","large"])
|
||||
whipser_language = gr.Dropdown(label='Whisper Language', value=params['whipser_language'],choices=["chinese","german","spanish","russian","korean","french","japanese","portuguese","turkish","polish","catalan","dutch","arabic","swedish","italian","indonesian","hindi","finnish","vietnamese","hebrew","ukrainian","greek","malay","czech","romanian","danish","hungarian","tamil","norwegian","thai","urdu","croatian","bulgarian","lithuanian","latin","maori","malayalam","welsh","slovak","telugu","persian","latvian","bengali","serbian","azerbaijani","slovenian","kannada","estonian","macedonian","breton","basque","icelandic","armenian","nepali","mongolian","bosnian","kazakh","albanian","swahili","galician","marathi","punjabi","sinhala","khmer","shona","yoruba","somali","afrikaans","occitan","georgian","belarusian","tajik","sindhi","gujarati","amharic","yiddish","lao","uzbek","faroese","haitian creole","pashto","turkmen","nynorsk","maltese","sanskrit","luxembourgish","myanmar","tibetan","tagalog","malagasy","assamese","tatar","hawaiian","lingala","hausa","bashkir","javanese","sundanese"])
|
||||
whipser_model = gr.Dropdown(label='Whisper Model', value=params['whipser_model'], choices=["tiny.en", "base.en", "small.en", "medium.en", "tiny", "base", "small", "medium", "large"])
|
||||
whipser_language = gr.Dropdown(label='Whisper Language', value=params['whipser_language'], choices=["chinese", "german", "spanish", "russian", "korean", "french", "japanese", "portuguese", "turkish", "polish", "catalan", "dutch", "arabic", "swedish", "italian", "indonesian", "hindi", "finnish", "vietnamese", "hebrew", "ukrainian", "greek", "malay", "czech", "romanian", "danish", "hungarian", "tamil", "norwegian", "thai", "urdu", "croatian", "bulgarian", "lithuanian", "latin", "maori", "malayalam", "welsh", "slovak", "telugu", "persian", "latvian", "bengali", "serbian", "azerbaijani", "slovenian", "kannada", "estonian", "macedonian", "breton", "basque", "icelandic", "armenian", "nepali", "mongolian", "bosnian", "kazakh", "albanian", "swahili", "galician", "marathi", "punjabi", "sinhala", "khmer", "shona", "yoruba", "somali", "afrikaans", "occitan", "georgian", "belarusian", "tajik", "sindhi", "gujarati", "amharic", "yiddish", "lao", "uzbek", "faroese", "haitian creole", "pashto", "turkmen", "nynorsk", "maltese", "sanskrit", "luxembourgish", "myanmar", "tibetan", "tagalog", "malagasy", "assamese", "tatar", "hawaiian", "lingala", "hausa", "bashkir", "javanese", "sundanese"])
|
||||
|
||||
audio.change(
|
||||
auto_transcribe, [audio, auto_submit,whipser_model,whipser_language], [shared.gradio['textbox'], audio]).then(
|
||||
auto_transcribe, [audio, auto_submit, whipser_model, whipser_language], [shared.gradio['textbox'], audio]).then(
|
||||
None, auto_submit, None, _js="(check) => {if (check) { document.getElementById('Generate').click() }}")
|
||||
whipser_model.change(lambda x: params.update({"whipser_model": x}), whipser_model, None)
|
||||
whipser_language.change(lambda x: params.update({"whipser_language": x}), whipser_language, None)
|
||||
|
||||
@ -54,14 +54,14 @@ loaders_and_params = {
|
||||
'trust_remote_code',
|
||||
'transformers_info'
|
||||
],
|
||||
'ExLlama' : [
|
||||
'ExLlama': [
|
||||
'gpu_split',
|
||||
'max_seq_len',
|
||||
'compress_pos_emb',
|
||||
'alpha_value',
|
||||
'exllama_info',
|
||||
],
|
||||
'ExLlama_HF' : [
|
||||
'ExLlama_HF': [
|
||||
'gpu_split',
|
||||
'max_seq_len',
|
||||
'compress_pos_emb',
|
||||
|
||||
@ -126,6 +126,7 @@ class RepetitionPenaltyLogitsProcessorWithRange(LogitsProcessor):
|
||||
'''
|
||||
Copied from the transformers library
|
||||
'''
|
||||
|
||||
def __init__(self, penalty: float, _range: int):
|
||||
if not isinstance(penalty, float) or not (penalty > 0):
|
||||
raise ValueError(f"`penalty` has to be a strictly positive float, but is {penalty}")
|
||||
|
||||
@ -116,7 +116,7 @@ def get_available_loras():
|
||||
def get_datasets(path: str, ext: str):
|
||||
# include subdirectories for raw txt files to allow training from a subdirectory of txt files
|
||||
if ext == "txt":
|
||||
return ['None'] + sorted(set([k.stem for k in list(Path(path).glob('txt'))+list(Path(path).glob('*/')) if k.stem != 'put-trainer-datasets-here']), key=natural_keys)
|
||||
return ['None'] + sorted(set([k.stem for k in list(Path(path).glob('txt')) + list(Path(path).glob('*/')) if k.stem != 'put-trainer-datasets-here']), key=natural_keys)
|
||||
|
||||
return ['None'] + sorted(set([k.stem for k in Path(path).glob(f'*.{ext}') if k.stem != 'put-trainer-datasets-here']), key=natural_keys)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user