初始化项目，由ModelHub XC社区提供模型
Model: Zhengping/conditional-probability-regression Source: Original Platform
This commit is contained in:
ModelHub XC
36
.gitattributes
vendored
Normal file
36
.gitattributes
vendored
Normal file
@@ -0,0 +1,36 @@
|
||||
*.7z filter=lfs diff=lfs merge=lfs -text
|
||||
*.arrow filter=lfs diff=lfs merge=lfs -text
|
||||
*.bin filter=lfs diff=lfs merge=lfs -text
|
||||
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
||||
*.ckpt filter=lfs diff=lfs merge=lfs -text
|
||||
*.ftz filter=lfs diff=lfs merge=lfs -text
|
||||
*.gz filter=lfs diff=lfs merge=lfs -text
|
||||
*.h5 filter=lfs diff=lfs merge=lfs -text
|
||||
*.joblib filter=lfs diff=lfs merge=lfs -text
|
||||
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
||||
*.model filter=lfs diff=lfs merge=lfs -text
|
||||
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
||||
*.npy filter=lfs diff=lfs merge=lfs -text
|
||||
*.npz filter=lfs diff=lfs merge=lfs -text
|
||||
*.onnx filter=lfs diff=lfs merge=lfs -text
|
||||
*.ot filter=lfs diff=lfs merge=lfs -text
|
||||
*.parquet filter=lfs diff=lfs merge=lfs -text
|
||||
*.pb filter=lfs diff=lfs merge=lfs -text
|
||||
*.pickle filter=lfs diff=lfs merge=lfs -text
|
||||
*.pkl filter=lfs diff=lfs merge=lfs -text
|
||||
*.pt filter=lfs diff=lfs merge=lfs -text
|
||||
*.pth filter=lfs diff=lfs merge=lfs -text
|
||||
*.rar filter=lfs diff=lfs merge=lfs -text
|
||||
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
||||
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
||||
*.tar filter=lfs diff=lfs merge=lfs -text
|
||||
*.tflite filter=lfs diff=lfs merge=lfs -text
|
||||
*.tgz filter=lfs diff=lfs merge=lfs -text
|
||||
*.wasm filter=lfs diff=lfs merge=lfs -text
|
||||
*.xz filter=lfs diff=lfs merge=lfs -text
|
||||
*.zip filter=lfs diff=lfs merge=lfs -text
|
||||
*.zst filter=lfs diff=lfs merge=lfs -text
|
||||
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
||||
tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
||||
405
README.md
Normal file
405
README.md
Normal file
@@ -0,0 +1,405 @@
|
||||
---
|
||||
library_name: transformers
|
||||
license: mit
|
||||
datasets:
|
||||
- Zhengping/UNLI
|
||||
- Zhengping/UNLI-style-synthetic
|
||||
language:
|
||||
- en
|
||||
metrics:
|
||||
- pearsonr
|
||||
- spearmanr
|
||||
- accuracy
|
||||
base_model:
|
||||
- Qwen/Qwen2.5-14B-Instruct
|
||||
---
|
||||
|
||||
# Model Card for Model ID
|
||||
|
||||
<!-- Provide a quick summary of what the model is/does. -->
|
||||
|
||||
|
||||
|
||||
## Model Details
|
||||
|
||||
### Model Description
|
||||
|
||||
<!-- Provide a longer summary of what this model is. -->
|
||||
|
||||
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
|
||||
|
||||
- **Developed by:** Liaoyaqi Wang, Zhengping Jiang, Anqi Liu, Benjamin Van Durme
|
||||
- **Model type:** Decoding-based Regression Model (Classification)
|
||||
- **Language(s) (NLP):** `en`
|
||||
- **License:** mit
|
||||
- **Finetuned from model [optional]:** Qwen/Qwen2.5-14B-Instruct
|
||||
|
||||
### Model Sources [optional]
|
||||
|
||||
<!-- Provide the basic links for the model. -->
|
||||
|
||||
- **Repository:** [Decoding-based Regression](https://github.com/zipJiang/decoding-based-regression.git)
|
||||
- **Paper [optional]:** [Always Tell Me The Odds: Fine-grained Conditional Probability Estimation](https://arxiv.org/pdf/2505.01595)
|
||||
|
||||
## Uses
|
||||
|
||||
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
||||
|
||||
### Direct Use
|
||||
|
||||
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
||||
|
||||
```python
|
||||
import enum
|
||||
import transformers
|
||||
import torch
|
||||
from transformers.pipelines import PIPELINE_REGISTRY
|
||||
from transformers import (
|
||||
pipeline,
|
||||
Pipeline,
|
||||
TextGenerationPipeline,
|
||||
PreTrainedTokenizer,
|
||||
AutoModelForCausalLM,
|
||||
PreTrainedTokenizer
|
||||
)
|
||||
from transformers.pipelines.text_generation import Chat, ReturnType
|
||||
from typing import (
|
||||
Dict,
|
||||
Callable,
|
||||
Tuple,
|
||||
List,
|
||||
)
|
||||
|
||||
|
||||
class LevelToScorePipeline(TextGenerationPipeline):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
level_to_score_func: Callable[[Tuple[torch.FloatTensor], PreTrainedTokenizer], Tuple[List[float], List[List[float]]]],
|
||||
*args,
|
||||
**kwargs
|
||||
):
|
||||
super().__init__(*args, **kwargs)
|
||||
self._level_to_score_func = level_to_score_func
|
||||
|
||||
def preprocess(
|
||||
self,
|
||||
prompt_text,
|
||||
prefix="",
|
||||
handle_long_generation=None,
|
||||
add_special_tokens=None,
|
||||
truncation=None,
|
||||
padding=None,
|
||||
max_length=None,
|
||||
continue_final_message=None,
|
||||
**generate_kwargs,
|
||||
):
|
||||
# Only set non-None tokenizer kwargs, so as to rely on the tokenizer's defaults
|
||||
tokenizer_kwargs = {
|
||||
"add_special_tokens": add_special_tokens,
|
||||
"truncation": truncation,
|
||||
"padding": padding,
|
||||
"max_length": max_length,
|
||||
}
|
||||
tokenizer_kwargs = {key: value for key, value in tokenizer_kwargs.items() if value is not None}
|
||||
|
||||
if isinstance(prompt_text, Chat):
|
||||
tokenizer_kwargs.pop("add_special_tokens", None) # ignore add_special_tokens on chats
|
||||
# If the user passes a chat that ends in an assistant message, we treat it as a prefill by default
|
||||
# because very few models support multiple separate, consecutive assistant messages
|
||||
if continue_final_message is None:
|
||||
continue_final_message = prompt_text.messages[-1]["role"] == "assistant"
|
||||
inputs = self.tokenizer.apply_chat_template(
|
||||
prompt_text.messages,
|
||||
add_generation_prompt=not continue_final_message,
|
||||
continue_final_message=continue_final_message,
|
||||
return_dict=True,
|
||||
return_tensors=self.framework,
|
||||
**tokenizer_kwargs,
|
||||
)
|
||||
else:
|
||||
inputs = self.tokenizer(prefix + prompt_text, return_tensors=self.framework, **tokenizer_kwargs)
|
||||
|
||||
inputs["prompt_text"] = prompt_text
|
||||
|
||||
if handle_long_generation == "hole":
|
||||
cur_len = inputs["input_ids"].shape[-1]
|
||||
if "max_new_tokens" in generate_kwargs:
|
||||
new_tokens = generate_kwargs["max_new_tokens"]
|
||||
else:
|
||||
new_tokens = generate_kwargs.get("max_length", self.generation_config.max_length) - cur_len
|
||||
if new_tokens < 0:
|
||||
raise ValueError("We cannot infer how many new tokens are expected")
|
||||
if cur_len + new_tokens > self.tokenizer.model_max_length:
|
||||
keep_length = self.tokenizer.model_max_length - new_tokens
|
||||
if keep_length <= 0:
|
||||
raise ValueError(
|
||||
"We cannot use `hole` to handle this generation the number of desired tokens exceeds the"
|
||||
" models max length"
|
||||
)
|
||||
|
||||
inputs["input_ids"] = inputs["input_ids"][:, -keep_length:]
|
||||
if "attention_mask" in inputs:
|
||||
inputs["attention_mask"] = inputs["attention_mask"][:, -keep_length:]
|
||||
|
||||
return inputs
|
||||
|
||||
def _forward(self, model_inputs, **generate_kwargs):
|
||||
input_ids = model_inputs["input_ids"]
|
||||
attention_mask = model_inputs.get("attention_mask", None)
|
||||
# Allow empty prompts
|
||||
if input_ids.shape[1] == 0:
|
||||
input_ids = None
|
||||
attention_mask = None
|
||||
in_b = 1
|
||||
else:
|
||||
in_b = input_ids.shape[0]
|
||||
prompt_text = model_inputs.pop("prompt_text")
|
||||
|
||||
# If there is a prefix, we may need to adjust the generation length. Do so without permanently modifying
|
||||
# generate_kwargs, as some of the parameterization may come from the initialization of the pipeline.
|
||||
prefix_length = generate_kwargs.pop("prefix_length", 0)
|
||||
if prefix_length > 0:
|
||||
has_max_new_tokens = "max_new_tokens" in generate_kwargs or (
|
||||
"generation_config" in generate_kwargs
|
||||
and generate_kwargs["generation_config"].max_new_tokens is not None
|
||||
)
|
||||
if not has_max_new_tokens:
|
||||
generate_kwargs["max_length"] = generate_kwargs.get("max_length") or self.generation_config.max_length
|
||||
generate_kwargs["max_length"] += prefix_length
|
||||
has_min_new_tokens = "min_new_tokens" in generate_kwargs or (
|
||||
"generation_config" in generate_kwargs
|
||||
and generate_kwargs["generation_config"].min_new_tokens is not None
|
||||
)
|
||||
if not has_min_new_tokens and "min_length" in generate_kwargs:
|
||||
generate_kwargs["min_length"] += prefix_length
|
||||
|
||||
# User-defined `generation_config` passed to the pipeline call take precedence
|
||||
if "generation_config" not in generate_kwargs:
|
||||
generate_kwargs["generation_config"] = self.generation_config
|
||||
|
||||
generate_kwargs["output_scores"] = not generate_kwargs.get("do_sample", False)
|
||||
generate_kwargs["return_dict_in_generate"] = True
|
||||
|
||||
generated_sequence = self.model.generate(input_ids=input_ids, attention_mask=attention_mask, **generate_kwargs)
|
||||
|
||||
logits = None
|
||||
|
||||
# TODO: check good default
|
||||
if generate_kwargs.get("return_scores", True):
|
||||
assert not generate_kwargs.get("do_sample", False), "return_logits=True is only supported for do_sample=False"
|
||||
|
||||
# Proceed to process logits and convert to score average.
|
||||
# next_token_logits is [batch_size, vocab_size]
|
||||
# raw_logits is a tuple of ([next_token_logits, past_key_values])
|
||||
|
||||
logits = generated_sequence.scores
|
||||
|
||||
out_b = generated_sequence.sequences.shape[0]
|
||||
if self.framework == "pt":
|
||||
generated_sequence = generated_sequence.sequences.reshape(in_b, out_b // in_b, *generated_sequence.sequences.shape[1:])
|
||||
# elif self.framework == "tf":
|
||||
# generated_sequence = tf.reshape(generated_sequence, (in_b, out_b // in_b, *generated_sequence.shape[1:]))
|
||||
return {"generated_sequence": generated_sequence, "input_ids": input_ids, "prompt_text": prompt_text, "logits": logits}
|
||||
|
||||
def postprocess(
|
||||
self,
|
||||
model_outputs,
|
||||
return_type=ReturnType.FULL_TEXT,
|
||||
clean_up_tokenization_spaces=True,
|
||||
continue_final_message=None,
|
||||
):
|
||||
generated_sequence = model_outputs["generated_sequence"][0]
|
||||
input_ids = model_outputs["input_ids"]
|
||||
prompt_text = model_outputs["prompt_text"]
|
||||
logits = model_outputs["logits"]
|
||||
|
||||
#TODO: This is now making many assumptions about how the logits are ordered,
|
||||
# Should think about how to make this explicit
|
||||
scores, selective_logits = self._level_to_score_func(logits, self.tokenizer)
|
||||
|
||||
generated_sequence = generated_sequence.numpy().tolist()
|
||||
records = []
|
||||
for sequence in generated_sequence:
|
||||
if return_type == ReturnType.TENSORS:
|
||||
record = {"generated_token_ids": sequence}
|
||||
elif return_type in {ReturnType.NEW_TEXT, ReturnType.FULL_TEXT}:
|
||||
# Decode text
|
||||
text = self.tokenizer.decode(
|
||||
sequence,
|
||||
skip_special_tokens=True,
|
||||
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||||
)
|
||||
|
||||
# Remove PADDING prompt of the sequence if XLNet or Transfo-XL model is used
|
||||
if input_ids is None:
|
||||
prompt_length = 0
|
||||
else:
|
||||
prompt_length = len(
|
||||
self.tokenizer.decode(
|
||||
input_ids[0],
|
||||
skip_special_tokens=True,
|
||||
clean_up_tokenization_spaces=clean_up_tokenization_spaces,
|
||||
)
|
||||
)
|
||||
|
||||
all_text = text[prompt_length:]
|
||||
if return_type == ReturnType.FULL_TEXT:
|
||||
if isinstance(prompt_text, str):
|
||||
all_text = prompt_text + all_text
|
||||
elif isinstance(prompt_text, Chat):
|
||||
if continue_final_message is None:
|
||||
# If the user passes a chat ending in an assistant message, we treat it as a prefill by
|
||||
# default because very few models support multiple separate, consecutive assistant messages
|
||||
continue_final_message = prompt_text.messages[-1]["role"] == "assistant"
|
||||
if continue_final_message:
|
||||
# With assistant prefill, concat onto the end of the last message
|
||||
all_text = list(prompt_text.messages)[:-1] + [
|
||||
{
|
||||
"role": prompt_text.messages[-1]["role"],
|
||||
"content": prompt_text.messages[-1]["content"] + all_text,
|
||||
}
|
||||
]
|
||||
else:
|
||||
# When we're not starting from a prefill, the output is a new assistant message
|
||||
all_text = list(prompt_text.messages) + [{"role": "assistant", "content": all_text}]
|
||||
record = {
|
||||
"generated_text": all_text,
|
||||
"score": scores[0],
|
||||
"selective_logits": selective_logits[0]
|
||||
}
|
||||
records.append(record)
|
||||
|
||||
return records
|
||||
|
||||
|
||||
class SingleLabelRankDict:
|
||||
def __init__(
|
||||
self,
|
||||
rank_dict: Dict[Text, Any]
|
||||
):
|
||||
self._rank_dict = rank_dict
|
||||
|
||||
def __len__(self) -> int:
|
||||
return len(self._rank_dict)
|
||||
|
||||
def get_rank_dict(self, tokenizer: PreTrainedTokenizer) -> Dict[int, Any]:
|
||||
return {tokenizer.convert_tokens_to_ids([token])[0]: value for token, value in self._rank_dict.items()}
|
||||
|
||||
def to_tokenizer(self, tokenizer: PreTrainedTokenizer) -> PreTrainedTokenizer:
|
||||
"""Augment tokenizer vocab with `rank_dict` IN-PLACE.
|
||||
"""
|
||||
vocabs: List[Text] = self._rank_dict.keys()
|
||||
new_vocab = [vocab for vocab in vocabs if vocab not in tokenizer.get_vocab()]
|
||||
tokenizer.add_tokens(new_vocab)
|
||||
return tokenizer
|
||||
def from_tokenizer(cls, tokenizer: PreTrainedTokenizer) -> "SingleLabelRankDict":
|
||||
vocab = tokenizer.get_vocab()
|
||||
rank_dict = {}
|
||||
pattern = re.compile(r" <\|label_level_(\d+)\|>")
|
||||
|
||||
for token in vocab.keys():
|
||||
match = pattern.match(token)
|
||||
if match:
|
||||
value = int(match.group(1))
|
||||
# normalized_value = value / (len(vocab) - 1)
|
||||
rank_dict[token] = value
|
||||
|
||||
# normalize rank_values
|
||||
num_levels = max(rank_dict.values()) + 1
|
||||
for token in rank_dict.keys():
|
||||
rank_dict[token] = 1. / num_levels * (rank_dict[token] + 0.5)
|
||||
|
||||
return cls(rank_dict=rank_dict)
|
||||
|
||||
|
||||
model = transformers.AutoModelForCausalLM.from_pretrained(
|
||||
"Zhengping/conditional-probability-regression",
|
||||
torch_dtype="auto",
|
||||
attn_implementation="flash_attention_2",
|
||||
)
|
||||
tokenizer = transformers.AutoTokenizer.from_pretrained(
|
||||
"Zhengping/conditional-probability-regression",
|
||||
)
|
||||
|
||||
rank_dict = SingleLabelRankDict.from_tokenizer(tokenizer)
|
||||
|
||||
PIPELINE_REGISTRY.register_pipeline(
|
||||
"level-to-score",
|
||||
pipeline_class=LevelToScorePipeline,
|
||||
pt_model=AutoModelForCausalLM
|
||||
)
|
||||
|
||||
# This allows fine-grained labeling, the greedy decoding gives a coarse score,
|
||||
# one can also attach their own level-to-score function to the pipeline, e.g. using UNLI
|
||||
# label transformation to get it more binarized
|
||||
def _level_to_score_func(
|
||||
logits: Tuple[torch.FloatTensor],
|
||||
tokenizer: PreTrainedTokenizer
|
||||
) -> Tuple[List[float], List[float]]:
|
||||
""" """
|
||||
logits = logits[0]
|
||||
num_labels = len(rank_dict)
|
||||
considering_ids = tokenizer.convert_tokens_to_ids([f" <|label_level_{i}|>" for i in range(num_labels)])
|
||||
selective_logits = torch.index_select(logits, 1, torch.tensor(considering_ids, device=logits.device))
|
||||
step_size = 1 / num_labels
|
||||
expectation = torch.tensor([[i * step_size + 1 / 2 * step_size for i in range(num_labels)]], device=selective_logits.device)
|
||||
scores = torch.softmax(selective_logits, dim=-1) @ expectation.T
|
||||
scores = scores.squeeze(-1).tolist()
|
||||
return scores, selective_logits.tolist()
|
||||
|
||||
pipe = pipeline(
|
||||
"level-to-score",
|
||||
model=model,
|
||||
max_new_tokens=2,
|
||||
tokenizer=tokenizer,
|
||||
device=0,
|
||||
level_to_score_func=_level_to_score_func,
|
||||
torch_dtype=torch.bfloat16,
|
||||
)
|
||||
|
||||
template = UNLITemplate()
|
||||
|
||||
premise = "Sam is sleeping."
|
||||
hypothesis = "Sam is awake."
|
||||
|
||||
inputs = [
|
||||
{
|
||||
"role": "user",
|
||||
"content": "### Question: Given the premise \"{premise}\", how likely is it that the hypothesis \"{hypothesis}\" is true?\n\n".format(
|
||||
premise=premise,
|
||||
hypothesis=hypothesis
|
||||
)
|
||||
},
|
||||
{
|
||||
"role": "assitant",
|
||||
"content": "### Answer:"
|
||||
}
|
||||
]
|
||||
|
||||
result = pipe(inputs)
|
||||
print(result)
|
||||
```
|
||||
|
||||
|
||||
## Use with vLLM
|
||||
|
||||
`TODO`
|
||||
|
||||
|
||||
#### Summary
|
||||
|
||||
LLM-based Fine-grained Conditional Probability Estimation
|
||||
|
||||
## Citation [optional]
|
||||
|
||||
```bibtex
|
||||
@article{wang2025always,
|
||||
title={Always Tell Me The Odds: Fine-grained Conditional Probability Estimation},
|
||||
author={Wang, Liaoyaqi and Jiang, Zhengping and Liu, Anqi and Van Durme, Benjamin},
|
||||
journal={arXiv preprint arXiv:2505.01595},
|
||||
year={2025}
|
||||
}
|
||||
```
|
||||
|
||||
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
||||
34
added_tokens.json
Normal file
34
added_tokens.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
" <|label_level_0|>": 151665,
|
||||
" <|label_level_1|>": 151666,
|
||||
" <|label_level_2|>": 151667,
|
||||
" <|label_level_3|>": 151668,
|
||||
" <|label_level_4|>": 151669,
|
||||
" <|label_level_5|>": 151670,
|
||||
" <|label_level_6|>": 151671,
|
||||
" <|label_level_7|>": 151672,
|
||||
" <|label_level_8|>": 151673,
|
||||
" <|label_level_9|>": 151674,
|
||||
"</tool_call>": 151658,
|
||||
"<tool_call>": 151657,
|
||||
"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
||||
"<|endoftext|>": 151643,
|
||||
"<|file_sep|>": 151664,
|
||||
"<|fim_middle|>": 151660,
|
||||
"<|fim_pad|>": 151662,
|
||||
"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
||||
"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
||||
"<|quad_end|>": 151651,
|
||||
"<|quad_start|>": 151650,
|
||||
"<|repo_name|>": 151663,
|
||||
"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
28
config.json
Normal file
28
config.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 5120,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 13824,
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 70,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 40,
|
||||
"num_hidden_layers": 48,
|
||||
"num_key_value_heads": 8,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000.0,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": false,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.50.3",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151675
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"repetition_penalty": 1.05,
|
||||
"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "4.50.3"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00006.safetensors
Normal file
3
model-00001-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4998343b6b0c2d825e2b4152e7f93e946f6c1dea93876f1c267ea9ce1f0f2cdf
|
||||
size 4982227920
|
||||
3
model-00002-of-00006.safetensors
Normal file
3
model-00002-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:1708990176271a4a4c5a0ee5ede4a7b852208faf493074e28235ff73c9d60a72
|
||||
size 4954847344
|
||||
3
model-00003-of-00006.safetensors
Normal file
3
model-00003-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:aaa91879893fb2b8c047e0f3193d1404d416f21abce9a48d090c489e9f0403d6
|
||||
size 4954847392
|
||||
3
model-00004-of-00006.safetensors
Normal file
3
model-00004-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:98f2e8ee1773efeb9cca2984e571795f52cc566c2e5e98b61d35291bf06607ff
|
||||
size 4954847392
|
||||
3
model-00005-of-00006.safetensors
Normal file
3
model-00005-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:32346ae0b004873f98fb2a75759285b8e04fbbe8623e99d1e9561ffd5ad024be
|
||||
size 4954847392
|
||||
3
model-00006-of-00006.safetensors
Normal file
3
model-00006-of-00006.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2ccd6ebc89920b19495f7c40c9e6d9ba4fcb16f8b21af33cc0ea6131e8ee6049
|
||||
size 4730549800
|
||||
586
model.safetensors.index.json
Normal file
586
model.safetensors.index.json
Normal file
@@ -0,0 +1,586 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 29532100608
|
||||
},
|
||||
"weight_map": {
|
||||
"lm_head.weight": "model-00006-of-00006.safetensors",
|
||||
"model.embed_tokens.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.input_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.0.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.input_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.1.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.10.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.10.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.11.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.12.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.13.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.14.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.15.input_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.15.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.15.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.15.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.15.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.15.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.15.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.15.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.15.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.15.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.15.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.15.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.16.input_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.self_attn.k_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.self_attn.q_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.self_attn.v_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.16.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.input_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.self_attn.k_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.self_attn.q_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.self_attn.v_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.17.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.input_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.self_attn.k_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.self_attn.q_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.self_attn.v_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.18.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.input_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.self_attn.k_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.self_attn.q_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.self_attn.v_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.19.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.2.input_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.self_attn.k_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.self_attn.q_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.self_attn.v_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.2.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.20.input_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.self_attn.k_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.self_attn.q_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.self_attn.v_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.20.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.input_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.self_attn.k_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.self_attn.q_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.self_attn.v_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.21.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.input_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.self_attn.k_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.self_attn.q_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.self_attn.v_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.22.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.input_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.mlp.down_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.mlp.gate_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.mlp.up_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.post_attention_layernorm.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.self_attn.k_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.self_attn.q_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.self_attn.v_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.23.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.24.input_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.24.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.24.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.24.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.24.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.24.self_attn.k_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.24.self_attn.k_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.24.self_attn.o_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.24.self_attn.q_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.24.self_attn.q_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.24.self_attn.v_proj.bias": "model-00003-of-00006.safetensors",
|
||||
"model.layers.24.self_attn.v_proj.weight": "model-00003-of-00006.safetensors",
|
||||
"model.layers.25.input_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.self_attn.k_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.self_attn.q_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.self_attn.v_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.25.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.input_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.self_attn.k_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.self_attn.q_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.self_attn.v_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.26.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.input_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.self_attn.k_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.self_attn.q_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.self_attn.v_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.27.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.input_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.self_attn.k_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.self_attn.q_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.self_attn.v_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.28.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.input_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.self_attn.k_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.self_attn.q_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.self_attn.v_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.29.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.3.input_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.self_attn.k_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.self_attn.q_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.self_attn.v_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.3.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.30.input_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.self_attn.k_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.self_attn.q_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.self_attn.v_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.30.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.input_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.self_attn.k_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.self_attn.q_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.self_attn.v_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.31.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.input_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.mlp.down_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.mlp.gate_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.mlp.up_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.post_attention_layernorm.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.self_attn.k_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.self_attn.q_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.self_attn.v_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.32.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.33.input_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.33.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.33.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.33.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.33.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.33.self_attn.k_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.33.self_attn.k_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.33.self_attn.o_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.33.self_attn.q_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.33.self_attn.q_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.33.self_attn.v_proj.bias": "model-00004-of-00006.safetensors",
|
||||
"model.layers.33.self_attn.v_proj.weight": "model-00004-of-00006.safetensors",
|
||||
"model.layers.34.input_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.self_attn.k_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.self_attn.q_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.self_attn.v_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.34.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.input_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.self_attn.k_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.self_attn.q_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.self_attn.v_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.35.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.input_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.self_attn.k_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.self_attn.q_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.self_attn.v_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.36.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.input_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.self_attn.k_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.self_attn.q_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.self_attn.v_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.37.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.input_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.self_attn.k_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.self_attn.q_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.self_attn.v_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.38.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.input_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.self_attn.k_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.self_attn.q_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.self_attn.v_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.39.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.4.input_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.self_attn.k_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.self_attn.q_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.self_attn.v_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.4.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.40.input_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.self_attn.k_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.self_attn.q_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.self_attn.v_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.40.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.input_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.mlp.down_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.mlp.gate_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.mlp.up_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.post_attention_layernorm.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.self_attn.k_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.self_attn.q_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.self_attn.v_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.41.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.42.input_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.42.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.42.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.42.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.42.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.42.self_attn.k_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.42.self_attn.k_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.42.self_attn.o_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.42.self_attn.q_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.42.self_attn.q_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.42.self_attn.v_proj.bias": "model-00005-of-00006.safetensors",
|
||||
"model.layers.42.self_attn.v_proj.weight": "model-00005-of-00006.safetensors",
|
||||
"model.layers.43.input_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.self_attn.k_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.self_attn.q_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.self_attn.v_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.43.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.input_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.self_attn.k_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.self_attn.q_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.self_attn.v_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.44.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.input_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.self_attn.k_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.self_attn.q_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.self_attn.v_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.45.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.input_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.self_attn.k_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.self_attn.q_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.self_attn.v_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.46.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.input_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.mlp.down_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.mlp.gate_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.mlp.up_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.post_attention_layernorm.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.self_attn.k_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.self_attn.k_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.self_attn.o_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.self_attn.q_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.self_attn.q_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.self_attn.v_proj.bias": "model-00006-of-00006.safetensors",
|
||||
"model.layers.47.self_attn.v_proj.weight": "model-00006-of-00006.safetensors",
|
||||
"model.layers.5.input_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.mlp.down_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.mlp.gate_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.mlp.up_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.post_attention_layernorm.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.self_attn.k_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.self_attn.q_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.self_attn.v_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.5.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.6.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.6.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.6.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.6.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.6.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.6.self_attn.k_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.6.self_attn.o_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.6.self_attn.q_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.6.self_attn.v_proj.bias": "model-00001-of-00006.safetensors",
|
||||
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00006.safetensors",
|
||||
"model.layers.7.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.bias": "model-00002-of-00006.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00006.safetensors",
|
||||
"model.norm.weight": "model-00006-of-00006.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:9841fdd11514476b346ba03bd1a124098233b1aa855c2692e58d330c4761ecdd
|
||||
size 11423846
|
||||
288
tokenizer_config.json
Normal file
288
tokenizer_config.json
Normal file
@@ -0,0 +1,288 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
"content": "<|quad_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": " <|label_level_0|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": " <|label_level_1|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": " <|label_level_2|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": " <|label_level_3|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151669": {
|
||||
"content": " <|label_level_4|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151670": {
|
||||
"content": " <|label_level_5|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151671": {
|
||||
"content": " <|label_level_6|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151672": {
|
||||
"content": " <|label_level_7|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151673": {
|
||||
"content": " <|label_level_8|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151674": {
|
||||
"content": " <|label_level_9|>",
|
||||
"lstrip": false,
|
||||
"normalized": true,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0]['role'] == 'system' %}\n {{- messages[0]['content'] }}\n {%- else %}\n {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n {%- endif %}\n {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n {%- if messages[0]['role'] == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n {%- else %}\n {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {{- '<|im_start|>' + message.role }}\n {%- if message.content %}\n {{- '\\n' + message.content }}\n {%- endif %}\n {%- for tool_call in message.tool_calls %}\n {%- if tool_call.function is defined %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '\\n<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- message.content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user