Bug描述
我尝试使用llamacpp和Mistral-7b进行树形摘要。然而,我遇到了一个问题,即模型默认嵌入了一个BOS标记。当我在提示模板中使用prompttemplate时,已经给出了一个BOS标记,它会给出警告并超过上下文窗口。
以下是llamacpp的配置:
llama_model_loader: loaded meta data with 29 key-value pairs and 291 tensors from model.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = llama
llama_model_loader: - kv 1: general.name str = Mistral-7B-Instruct-v0.3
llama_model_loader: - kv 2: llama.block_count u32 = 32
llama_model_loader: - kv 3: llama.context_length u32 = 32768
llama_model_loader: - kv 4: llama.embedding_length u32 = 4096
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.attention.head_count u32 = 32
llama_model_loader: - kv 7: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 8: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: general.file_type u32 = 18
llama_model_loader: - kv 11: llama.vocab_size u32 = 32768
llama_model_loader: - kv 12: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 13: tokenizer.ggml.model str = llama
llama_model_loader: - kv 14: tokenizer.ggml.pre str = default
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,32768] = ["<unk>", "<s>", "</s>", "[INST]", "[...
llama_model_loader: - kv 16: tokenizer.ggml.scores arr[f32,32768] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,32768] = [2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv 18: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 19: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 20: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 21: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 22: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 23: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 24: general.quantization_version u32 = 2
llama_model_loader: - kv 25: quantize.imatrix.file str = /models/Mistral-7B-Instruct-v0.3-GGUF...
llama_model_loader: - kv 26: quantize.imatrix.dataset str = /training_data/calibration_data.txt
llama_model_loader: - kv 27: quantize.imatrix.entries_count i32 = 224
llama_model_loader: - kv 28: quantize.imatrix.chunks_count i32 = 228
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q6_K: 226 tensors
llm_load_vocab: special tokens cache size = 1027
llm_load_vocab: token to piece cache size = 0.1731 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32768
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
llm_load_print_meta: n_embd_k_gqa = 1024
llm_load_print_meta: n_embd_v_gqa = 1024
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-05
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: ssm_d_conv = 0
llm_load_print_meta: ssm_d_inner = 0
llm_load_print_meta: ssm_d_state = 0
llm_load_print_meta: ssm_dt_rank = 0
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q6_K
llm_load_print_meta: model params = 7.25 B
llm_load_print_meta: model size = 5.54 GiB (6.56 BPW)
llm_load_print_meta: general.name = Mistral-7B-Instruct-v0.3
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 781 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.15 MiB
llm_load_tensors: CPU buffer size = 5671.02 MiB
...................................................................................................
llama_new_context_with_model: n_ctx = 3904
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU KV buffer size = 488.00 MiB
llama_new_context_with_model: KV self size = 488.00 MiB, K (f16): 244.00 MiB, V (f16): 244.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.13 MiB
llama_new_context_with_model: CPU compute buffer size = 283.63 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 1
AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 |
Model metadata: {'quantize.imatrix.entries_count': '224', 'quantize.imatrix.dataset': '/training_data/calibration_data.txt', 'quantize.imatrix.chunks_count': '228', 'quantize.imatrix.file': '/models/Mistral-7B-Instruct-v0.3-GGUF/Mistral-7B-Instruct-v0.3.imatrix', 'tokenizer.chat_template': "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}", 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.ggml.unknown_token_id': '0', 'tokenizer.ggml.eos_token_id': '2', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.architecture': 'llama', 'llama.rope.freq_base': '1000000.000000', 'tokenizer.ggml.pre': 'default', 'llama.context_length': '32768', 'general.name': 'Mistral-7B-Instruct-v0.3', 'tokenizer.ggml.add_bos_token': 'true', 'llama.embedding_length': '4096', 'llama.feed_forward_length': '14336', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'tokenizer.ggml.bos_token_id': '1', 'llama.attention.head_count': '32', 'llama.block_count': '32', 'llama.attention.head_count_kv': '8', 'general.file_type': '18', 'llama.vocab_size': '32768', 'llama.rope.dimension_count': '128'}
Available chat formats from metadata: chat_template.default
Guessed chat format: mistral-instruct
我是这样初始化llm的:
llm = LlamaCPP(
model_url=None,
model_path='model.gguf',
temperature=0.1,
max_new_tokens=256,
context_window=3900,
generate_kwargs={},
model_kwargs={"n_gpu_layers": -1},
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
verbose=True,
)
然后像这样调用TreeSummary:
summarizer = TreeSummarize(llm=llm,verbose=True, summary_template=qa_prompt)
response = summarizer.get_response(
"Now please summarize text below", [text], tone_name="an intelligent summarizer"
)
下面的警告错误如下:
llama_tokenize_internal: Added a BOS token to the prompt as specified by the model but the prompt also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. Are you sure this is what you want?
2 text chunks after repacking
/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py:1031: RuntimeWarning: Detected duplicate leading "<s>" in prompt, this will likely reduce response quality, consider removing it...
warnings.warn(
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-19-f523ca93d56a>](https://localhost:8080/#) in <cell line: 1>()
----> 1 response = summarizer.get_response(
2 "Now please summarize the meeting from the given coversation below", [text], tone_name="an intelligent meeting notes keeper"
3 )
10 frames
[/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py](https://localhost:8080/#) in _create_completion(self, prompt, suffix, max_tokens, temperature, top_p, min_p, typical_p, logprobs, echo, stop, frequency_penalty, presence_penalty, repeat_penalty, top_k, stream, seed, tfs_z, mirostat_mode, mirostat_tau, mirostat_eta, model, stopping_criteria, logits_processor, grammar, logit_bias)
1060
1061 if len(prompt_tokens) >= self._n_ctx:
-> 1062 raise ValueError(
1063 f"Requested tokens ({len(prompt_tokens)}) exceed context window of {llama_cpp.llama_n_ctx(self.ctx)}"
1064 )
ValueError: Requested tokens (4212) exceed context window of 3904
我可以向你保证问题不在于上下文长度本身。因为每次我在配置中增加上下文长度时,消息都会不断增加其阈值,例如
ValueError: 请求的标记数(8462)超过了上下文窗口大小为8000时的上下文窗口大小。
版本
llama_index_llms_llama_cpp-0.1.3
重现步骤
!wget -L https://huggingface.co/bartowski/Mistral-7B-Instruct-v0.3-GGUF/resolve/main/Mistral-7B-Instruct-v0.3-Q6_K.gguf -O model.gguf
from llama_index.llms.llama_cpp import LlamaCPP
from llama_index.llms.llama_cpp.llama_utils import (
messages_to_prompt,
completion_to_prompt,
)
llm = LlamaCPP(
# You can pass in the URL to a GGML model to download it automatically
model_url=None,
# optionally, you can set the path to a pre-downloaded model instead of model_url
model_path='model.gguf',
temperature=0.1,
max_new_tokens=256,
# llama2 has a context window of 4096 tokens, but we set it lower to allow for some wiggle room
context_window=3900,
# kwargs to pass to __call__()
generate_kwargs={},
# kwargs to pass to __init__()
# set to at least 1 to use GPU
model_kwargs={"n_gpu_layers": -1},
# transform inputs into Llama2 format
messages_to_prompt=messages_to_prompt,
completion_to_prompt=completion_to_prompt,
verbose=True,
)
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader(
input_files=["con.txt"]
)
docs = reader.load_data()
text = docs[0].text
from llama_index.core import PromptTemplate
# NOTE: we add an extra tone_name variable here
qa_prompt_tmpl = (
"Please also write the answer in the style of {tone_name}.\n"
"Query: {query_str}\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
)
qa_prompt = PromptTemplate(qa_prompt_tmpl)
refine_prompt_tmpl = (
"The original query is as follows: {query_str}\n"
"We have provided an existing answer: {existing_answer}\n"
"We have the opportunity to refine the existing answer "
"(only if needed) with some more context below.\n"
"------------\n"
"{context_msg}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the query. "
"Please also write the answer in the style of {tone_name}.\n"
"If the context isn't useful, return the original answer.\n"
"Refined Answer: "
)
refine_prompt = PromptTemplate(refine_prompt_tmpl)
from llama_index.core.response_synthesizers import TreeSummarize, Refine
from llama_index.core.types import BaseModel
from typing import List
summarizer = TreeSummarize(llm=llm,verbose=True, summary_template=qa_prompt)
response = summarizer.get_response(
"Now please summarize text below", [text], tone_name="an intelligent summarizer"
)
相关日志/回溯
llama_tokenize_internal: Added a BOS token to the prompt as specified by the model but the prompt also starts with a BOS token. So now the final prompt starts with 2 BOS tokens. Are you sure this is what you want?
2 text chunks after repacking
/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py:1031: RuntimeWarning: Detected duplicate leading "<s>" in prompt, this will likely reduce response quality, consider removing it...
warnings.warn(
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
[<ipython-input-19-f523ca93d56a>](https://localhost:8080/#) in <cell line: 1>()
----> 1 response = summarizer.get_response(
2 "Now please summarize the meeting from the given coversation below", [text], tone_name="an intelligent meeting notes keeper"
3 )
10 frames
[/usr/local/lib/python3.10/dist-packages/llama_cpp/llama.py](https://localhost:8080/#) in _create_completion(self, prompt, suffix, max_tokens, temperature, top_p, min_p, typical_p, logprobs, echo, stop, frequency_penalty, presence_penalty, repeat_penalty, top_k, stream, seed, tfs_z, mirostat_mode, mirostat_tau, mirostat_eta, model, stopping_criteria, logits_processor, grammar, logit_bias)
1060
1061 if len(prompt_tokens) >= self._n_ctx:
-> 1062 raise ValueError(
1063 f"Requested tokens ({len(prompt_tokens)}) exceed context window of {llama_cpp.llama_n_ctx(self.ctx)}"
1064 )
ValueError: Requested tokens (4212) exceed context window of 3904
2条答案
按热度按时间k75qkfdt1#
嘿,@syedfahimabrar!我在这里帮助你解决任何错误、问题或与贡献相关的事情。告诉我如何帮助你!
要解决Mistra-7b模型添加重复BOS令牌的问题,这会导致警告并在使用
TreeSummarize
和llamacpp
时超过上下文窗口,你需要确保在重新打包文本块时不会多次添加BOS令牌。这可以通过修改messages_to_prompt
函数来正确处理BOS令牌来实现。以下是确保提示以系统提示开头并避免重复BOS令牌的
messages_to_prompt
函数的相关部分:此函数通过检查提示是否已经以
"<|system|>\n"
开头来确保提示以系统提示开头并避免添加重复的BOS令牌。如果没有,它会在提示前添加"<|system|>\n</s>\n"
。此外,你还可以在
PromptHelper
类中修改repack
方法来正确处理BOS令牌:请确保将
BOS_TOKEN
替换为Mistra-7b模型实际使用的BOS令牌。这种修改可以确保在重新打包过程中不会出现重复的BOS令牌,从而防止警告和上下文窗口溢出。通过实施这些更改,你应该能够避免重复的BOS令牌及其导致的上下文窗口溢出问题。
zqry0prt2#
@syedfahimabrar set the tokenizer so that the token counting is correct