Converting to native Transformers
#17
by
cyrilvallez
HF staff
- opened
- .gitattributes +1 -0
- config.json +21 -36
- generation_config.json +3 -6
- model-00001-of-00010.safetensors → model-00001-of-00004.safetensors +2 -2
- model-00002-of-00010.safetensors → model-00002-of-00004.safetensors +2 -2
- model-00003-of-00010.safetensors → model-00003-of-00004.safetensors +2 -2
- model-00004-of-00010.safetensors → model-00004-of-00004.safetensors +2 -2
- model-00005-of-00010.safetensors +0 -3
- model-00006-of-00010.safetensors +0 -3
- model-00007-of-00010.safetensors +0 -3
- model-00008-of-00010.safetensors +0 -3
- model-00009-of-00010.safetensors +0 -3
- model-00010-of-00010.safetensors +0 -3
- model.safetensors.index.json +444 -285
- special_tokens_map.json +32 -0
- tokenizer.json +3 -0
- tokenizer_config.json +29 -12
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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+
tokenizer.json filter=lfs diff=lfs merge=lfs -text
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config.json
CHANGED
@@ -1,45 +1,30 @@
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{
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-
"_name_or_path": "THUDM/glm-4-9b-chat-1m",
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-
"model_type": "chatglm",
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"architectures": [
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"
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],
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-
"
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-
"AutoConfig": "configuration_chatglm.ChatGLMConfig",
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-
"AutoModel": "modeling_chatglm.ChatGLMForConditionalGeneration",
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-
"AutoModelForCausalLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
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-
"AutoModelForSeq2SeqLM": "modeling_chatglm.ChatGLMForConditionalGeneration",
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-
"AutoModelForSequenceClassification": "modeling_chatglm.ChatGLMForSequenceClassification"
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-
},
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-
"add_bias_linear": false,
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-
"add_qkv_bias": true,
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-
"apply_query_key_layer_scaling": true,
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-
"apply_residual_connection_post_layernorm": false,
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"attention_dropout": 0.0,
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"
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"
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"hidden_size": 4096,
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"
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"
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"
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"
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"num_attention_heads": 32,
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"num_hidden_layers": 40,
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"
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"
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"
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"
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-
"post_layer_norm": true,
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-
"rmsnorm": true,
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-
"seq_length": 1048576,
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-
"use_cache": true,
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-
"torch_dtype": "bfloat16",
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-
"transformers_version": "4.44.0",
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"tie_word_embeddings": false,
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"
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"
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}
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{
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"architectures": [
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+
"GlmForCausalLM"
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],
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+
"attention_bias": true,
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"attention_dropout": 0.0,
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+
"eos_token_id": [
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151329,
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151336,
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+
151338
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+
],
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+
"head_dim": 128,
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+
"hidden_act": "silu",
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"hidden_size": 4096,
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+
"initializer_range": 0.02,
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+
"intermediate_size": 13696,
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"max_position_embeddings": 1048576,
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+
"model_type": "glm",
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"num_attention_heads": 32,
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"num_hidden_layers": 40,
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+
"num_key_value_heads": 4,
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+
"pad_token_id": 151329,
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+
"rms_norm_eps": 1.5625e-07,
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+
"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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+
"torch_dtype": "bfloat16",
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+
"transformers_version": "4.46.0.dev0",
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+
"use_cache": true,
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+
"vocab_size": 151552
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}
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generation_config.json
CHANGED
@@ -1,13 +1,10 @@
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{
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"eos_token_id": [
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151329,
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151336,
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151338
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],
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"pad_token_id": 151329,
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-
"
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-
"max_length": 1024000,
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-
"top_p": 0.8,
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-
"transformers_version": "4.44.0"
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-
}
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{
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+
"_from_model_config": true,
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"eos_token_id": [
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151329,
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151336,
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151338
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],
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"pad_token_id": 151329,
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+
"transformers_version": "4.46.0.dev0"
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+
}
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model-00001-of-00010.safetensors → model-00001-of-00004.safetensors
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model-00002-of-00010.safetensors → model-00002-of-00004.safetensors
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model.safetensors.index.json
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special_tokens_map.json
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@@ -0,0 +1,32 @@
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|
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{
|
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|
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"<|endoftext|>",
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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|
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tokenizer.json
ADDED
@@ -0,0 +1,3 @@
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|
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version https://git-lfs.github.com/spec/v1
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|
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size 19967863
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tokenizer_config.json
CHANGED
@@ -1,10 +1,4 @@
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{
|
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|
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"AutoTokenizer": [
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|
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|
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|
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|
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|
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|
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"chat_template": "[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的,你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n在调用上述函数时,请使用 Json 格式表示调用的参数。{% elif tool['type'] == 'python' %}\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时,该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出,或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中,`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用,这些在线内容的访问将不会成功。{% elif tool['type'] == 'simple_browser' %}\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数:\n`search(query: str, recency_days: int)`:使用搜索引擎进行查询并显示结果,可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`:获取一系列指定 id 的页面内容。每次调用时,须选择3-10个页面。选择多个角度的页面,同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的,你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`:打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤:1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL,也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息,也可以再次使用 `search` 进行搜索。{% elif tool['type'] == 'cogview' %}\n\n## cogview\n\n如果用户的请求中包含了对图像的描述,你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述,规则:\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求,需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明,所在地为中国,持有中国立场并遵循中国社会主义价值观。{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
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|
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"chat_template": "[gMASK]<sop>{% for item in messages %}{% if item['tools'] is defined %}<|system|>\n你是一个名为 GLM-4 的人工智能助手。你是基于智谱AI训练的语言模型 GLM-4 模型开发的,你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{% set tools = item['tools'] %}{% for tool in tools %}{% if tool['type'] == 'function' %}\n\n## {{ tool['function']['name'] }}\n\n{{ tool['function'] | tojson(indent=4) }}\n在调用上述函数时,请使用 Json 格式表示调用的参数。{% elif tool['type'] == 'python' %}\n\n## python\n\n当你向 `python` 发送包含 Python 代码的消息时,该代码将会在一个有状态的 Jupyter notebook 环境中执行。\n`python` 返回代码执行的输出,或在执行 60 秒后返回超时。\n`/mnt/data` 将会持久化存储你的文件。在此会话中,`python` 无法访问互联网。不要使用 `python` 进行任何网络请求或者在线 API 调用,这些在线内容的访问将不会成功。{% elif tool['type'] == 'simple_browser' %}\n\n## simple_browser\n\n你可以使用 `simple_browser` 工具。该工具支持以下函数:\n`search(query: str, recency_days: int)`:使用搜索引擎进行查询并显示结果,可以使用 `recency_days` 参数控制搜索内容的时效性。\n`mclick(ids: list[int])`:获取一系列指定 id 的页面内容。每次调用时,须选择3-10个页面。选择多个角度的页面,同时尽可能选择可信任的信息来源。考虑到部分页面是无法加载的,你也可以多打开一些可能有用的页面而不用担心内容过多。\n`open_url(url: str)`:打开指定的 URL。\n\n使用 `【{引用 id}†{引用文本}】` 来引用内容。\n\n操作步骤:1. 使用 `search` 来获得信息列表; 2. 使用 `mclick` 来获取指定 ID 页面的内容; 3. 根据获得的内容进行回复。在回复中应当引用信息来源。\n 如果用户提供了 URL,也可以用 `open_url` 直接打开页面。\n如果初次搜索结果没有找到合适的信息,也可以再次使用 `search` 进行搜索。{% elif tool['type'] == 'cogview' %}\n\n## cogview\n\n如果用户的请求中包含了对图像的描述,你可以使用 `cogview` 来生成图像并展示给用户。你需要向 `cogview` 发送图像描述,规则:\n- 发送给 `cogview` 的消息必须使用英语。用户的中文描述必须完全翻译为英语。\n- 应当尽可能详细地描述图像生成的需求,需求描述约 100 英文单词。\n- 保持用户原始描述的意图。不要虚构内容或者没见过的人物。\n- 如无特殊说明,所在地为中国,持有中国立场并遵循中国社会主义价值观。{% endif %}{% endfor %}{% endif %}{% if item['content'] %}<|{{ item['role'] }}|>{{ item['metadata'] }}\n{{ item['content'] }}{% endif %}{% endfor %}{% if add_generation_prompt %}<|assistant|>{% endif %}",
|
139 |
+
"clean_up_tokenization_spaces": false,
|
140 |
"do_lower_case": false,
|
141 |
"eos_token": "<|endoftext|>",
|
142 |
+
"model_input_names": [
|
143 |
+
"input_ids",
|
144 |
+
"attention_mask"
|
145 |
+
],
|
146 |
"model_max_length": 1024000,
|
147 |
+
"pad_token": "<|endoftext|>",
|
148 |
"padding_side": "left",
|
149 |
"remove_space": false,
|
150 |
+
"tokenizer_class": "PreTrainedTokenizerFast"
|
151 |
}
|