Commit
·
73c95a1
verified
·
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Parent(s):
Duplicate from CodeDPO/qwen_coder_2.5_rm
Browse files- .gitattributes +36 -0
- README.md +153 -0
- added_tokens.json +24 -0
- config.json +29 -0
- generation_config.json +14 -0
- merges.txt +0 -0
- model-00001-of-00004.safetensors +3 -0
- model-00001-of-00007.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00002-of-00007.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00003-of-00007.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model-00004-of-00007.safetensors +3 -0
- model-00005-of-00007.safetensors +3 -0
- model-00006-of-00007.safetensors +3 -0
- model-00007-of-00007.safetensors +3 -0
- model.safetensors.index.json +348 -0
- special_tokens_map.json +31 -0
- tokenizer.json +3 -0
- tokenizer_config.json +208 -0
- vocab.json +0 -0
.gitattributes
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*.rar filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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README.md
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---
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library_name: transformers
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tags: []
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---
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## Uses
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```python
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import torch
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import torch.nn as nn
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from transformers import Qwen2ForCausalLM, AutoTokenizer
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class ValueHead(nn.Module):
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r"""
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The ValueHead class implements a head for GPT2 that returns a scalar for each output token.
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"""
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def __init__(self, config, **kwargs):
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super().__init__()
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if not hasattr(config, "summary_dropout_prob"):
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summary_dropout_prob = kwargs.pop("summary_dropout_prob", 0.1)
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else:
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summary_dropout_prob = config.summary_dropout_prob
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self.dropout = (
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nn.Dropout(summary_dropout_prob) if summary_dropout_prob else nn.Identity()
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)
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# some models such as OPT have a projection layer before the word embeddings - e.g. OPT-350m
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if hasattr(config, "hidden_size"):
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hidden_size = config.hidden_size
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if hasattr(config, "word_embed_proj_dim"):
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hidden_size = config.word_embed_proj_dim
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elif hasattr(config, "is_encoder_decoder"):
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if config.is_encoder_decoder and hasattr(config, "decoder"):
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if hasattr(config.decoder, "hidden_size"):
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hidden_size = config.decoder.hidden_size
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self.summary = nn.Linear(hidden_size, 1)
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self.flatten = nn.Flatten()
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def forward(self, hidden_states):
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output = self.dropout(hidden_states)
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# For now force upcast in fp32 if needed. Let's keep the
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# output in fp32 for numerical stability.
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if output.dtype != self.summary.weight.dtype:
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output = output.to(self.summary.weight.dtype)
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output = self.summary(output)
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return output
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class Qwen2ForCausalRM(Qwen2ForCausalLM):
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def __init__(self, config):
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super().__init__(config)
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self.v_head = ValueHead(config)
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def forward(
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self,
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input_ids=None,
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past_key_values=None,
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attention_mask=None,
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return_past_key_values=False,
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**kwargs,
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):
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r"""
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Applies a forward pass to the wrapped model and returns the logits of the value head.
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Args:
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input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
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Indices of input sequence tokens in the vocabulary.
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past_key_values (`tuple(tuple(torch.FloatTensor))`, `optional`):
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Contains pre-computed hidden-states (key and values in the attention blocks) as computed by the model
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(see `past_key_values` input) to speed up sequential decoding.
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attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, `optional`):
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Mask to avoid performing attention on padding token indices. Mask values selected in ``[0, 1]``:
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- 1 for tokens that are **not masked**,
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- 0 for tokens that are **masked**.
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return_past_key_values (bool): A flag indicating if the computed hidden-states should be returned.
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kwargs (`dict`, `optional`):
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Additional keyword arguments, that are passed to the wrapped model.
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"""
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kwargs["output_hidden_states"] = (
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True # this had already been set in the LORA / PEFT examples
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)
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kwargs["past_key_values"] = past_key_values
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# if (
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# self.is_peft_model
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# and
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# self.pretrained_model.active_peft_config.peft_type == "PREFIX_TUNING"
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# ):
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# kwargs.pop("past_key_values")
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base_model_output = super().forward(
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input_ids=input_ids,
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attention_mask=attention_mask,
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**kwargs,
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)
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last_hidden_state = base_model_output.hidden_states[-1]
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lm_logits = base_model_output.logits
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loss = base_model_output.loss
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if last_hidden_state.device != self.v_head.summary.weight.device:
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last_hidden_state = last_hidden_state.to(self.v_head.summary.weight.device)
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value = self.v_head(last_hidden_state).squeeze(-1)
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# force upcast in fp32 if logits are in half-precision
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if lm_logits.dtype != torch.float32:
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lm_logits = lm_logits.float()
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if return_past_key_values:
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return (lm_logits, loss, value, base_model_output.past_key_values)
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else:
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return (lm_logits, loss, value)
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model_path = "CodeDPO/qwen_coder_2.5_rm"
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model = Qwen2ForCausalRM.from_pretrained(model_path, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
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input_chat = [
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{"role": "user", "content": "Hello, how are you?"},
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{
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"role": "assistant",
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"content": "I'm doing great. How can I help you today?",
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},
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{
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"role": "user",
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"content": "I'd like to show off how chat templating works!",
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},
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]
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input_tokens = tokenizer.apply_chat_template(
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input_chat,
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tokenize=True,
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return_dict=True,
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padding=True,
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return_tensors="pt",
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).to(model.device)
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_, _, values = model(
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**input_tokens,
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output_hidden_states=True,
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return_dict=True,
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use_cache=False,
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)
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masks = input_tokens["attention_mask"]
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chosen_scores = values.gather(
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dim=-1, index=(masks.sum(dim=-1, keepdim=True) - 1)
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) # find the last token (eos) in each sequence, a
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chosen_scores = chosen_scores.squeeze()
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print(chosen_scores)
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```
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added_tokens.json
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{
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"</tool_call>": 151658,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|object_ref_start|>": 151646,
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"<|quad_end|>": 151651,
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"<|quad_start|>": 151650,
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"<|repo_name|>": 151663,
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"<|video_pad|>": 151656,
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"<|vision_end|>": 151653,
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"<|vision_pad|>": 151654,
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"<|vision_start|>": 151652
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}
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config.json
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{
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"_name_or_path": "./export_model/qwen_coder_2.5_7b_rm_inf",
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"architectures": [
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"Qwen2ForCausalRM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 3584,
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"initializer_range": 0.02,
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"intermediate_size": 18944,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 28,
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"num_hidden_layers": 28,
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"num_key_value_heads": 4,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.45.2",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 152064
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}
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generation_config.json
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{
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"bos_token_id": 151643,
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"do_sample": true,
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"eos_token_id": [
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151645,
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151643
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],
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"pad_token_id": 151643,
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"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
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"top_p": 0.8,
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"transformers_version": "4.45.2"
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}
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merges.txt
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The diff for this file is too large to render.
See raw diff
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model-00001-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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size 9755309568
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model-00001-of-00007.safetensors
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version https://git-lfs.github.com/spec/v1
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size 4976687216
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model-00002-of-00004.safetensors
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version https://git-lfs.github.com/spec/v1
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197 |
+
"bos_token": null,
|
198 |
+
"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",
|
199 |
+
"clean_up_tokenization_spaces": false,
|
200 |
+
"eos_token": "<|im_end|>",
|
201 |
+
"errors": "replace",
|
202 |
+
"model_max_length": 32768,
|
203 |
+
"pad_token": "<|endoftext|>",
|
204 |
+
"padding_side": "right",
|
205 |
+
"split_special_tokens": false,
|
206 |
+
"tokenizer_class": "Qwen2Tokenizer",
|
207 |
+
"unk_token": null
|
208 |
+
}
|
vocab.json
ADDED
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|
|