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[GH-ISSUE #3113] [BUG]: 上传文档时报错误 #1994

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opened 2026-02-22 18:27:38 -05:00 by yindo · 13 comments
yindo commented 2026-02-22 18:27:38 -05:00
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Originally created by @choochoo605 on GitHub (Feb 4, 2025).
Original GitHub issue: https://github.com/Mintplex-Labs/anything-llm/issues/3113

How are you running AnythingLLM?

Docker (local)

What happened?

上传文档时报：错误：1 个文档 添加失败。 禁止访问文件： “https://cdn.useanything.com/ support/models/Xenova/all- MiniLM-L6-v2/config.json”。怎么解决？

Are there known steps to reproduce?

No response

Originally created by @choochoo605 on GitHub (Feb 4, 2025). Original GitHub issue: https://github.com/Mintplex-Labs/anything-llm/issues/3113 ### How are you running AnythingLLM? Docker (local) ### What happened? 上传文档时报：错误：1 个文档 添加失败。 禁止访问文件： “https://cdn.useanything.com/ support/models/Xenova/all- MiniLM-L6-v2/config.json”。怎么解决？ ### Are there known steps to reproduce? _No response_
yindo added the possible bug label 2026-02-22 18:27:38 -05:00
yindo closed this issue 2026-02-22 18:27:38 -05:00
yindo commented 2026-02-22 18:27:38 -05:00
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@ahfie commented on GitHub (Feb 5, 2025):

The same thing happens to me. Add document causes: Error: 1 documents failed to add.
Forbidden access to file: "https://cdn.useanything.com/support/models/Xenova/all-MiniLM-L6-v2/config.json".

Anyone knows any solution? Thanks.

@ahfie commented on GitHub (Feb 5, 2025): The same thing happens to me. Add document causes: Error: 1 documents failed to add. Forbidden access to file: "https://cdn.useanything.com/support/models/Xenova/all-MiniLM-L6-v2/config.json". Anyone knows any solution? Thanks.
yindo commented 2026-02-22 18:27:38 -05:00
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@zhixianjuli commented on GitHub (Feb 5, 2025):

The same thing also happens to me.

@zhixianjuli commented on GitHub (Feb 5, 2025): The same thing also happens to me.
yindo commented 2026-02-22 18:27:38 -05:00
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@xoudan commented on GitHub (Feb 5, 2025):

The same thing also happens to me.

@xoudan commented on GitHub (Feb 5, 2025): The same thing also happens to me.
yindo commented 2026-02-22 18:27:39 -05:00
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@fortunerains commented on GitHub (Feb 5, 2025):

The same thing also happens to me.

@fortunerains commented on GitHub (Feb 5, 2025): The same thing also happens to me.
yindo commented 2026-02-22 18:27:39 -05:00
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@mainliner commented on GitHub (Feb 5, 2025):

the same thing alse hammpens to me. but my Chrome can access this CDN url correctly...

@mainliner commented on GitHub (Feb 5, 2025): the same thing alse hammpens to me. but my Chrome can access this CDN url correctly...
yindo commented 2026-02-22 18:27:39 -05:00
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@litao09h commented on GitHub (Feb 5, 2025):

The same thing also happens to me.

@litao09h commented on GitHub (Feb 5, 2025): The same thing also happens to me.
yindo commented 2026-02-22 18:27:39 -05:00
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@taoyuanlichao commented on GitHub (Feb 5, 2025):

The same thing also happens to me:

Error: 1 documents failed to add.

Forbidden access to file: "https://cdn.useanything.com/support/models/Xenova/all-MiniLM-L6-v2/tokenizer_config.json".

@taoyuanlichao commented on GitHub (Feb 5, 2025): The same thing also happens to me: Error: 1 documents failed to add. Forbidden access to file: "https://cdn.useanything.com/support/models/Xenova/all-MiniLM-L6-v2/tokenizer_config.json".
yindo commented 2026-02-22 18:27:39 -05:00
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@small-eight commented on GitHub (Feb 5, 2025):

该问题是在处理嵌入文本使用默认的anythingLLM enbedder的时候出现，具体原因未知，怀疑请求有问题。
我有个临时解决方案：修改embedder
AI Providers ->embedder ->Embedding Provider
修改成本地或者其他的

i have a temporary solution: Modify the embedder
AI Providers ->embedder ->Embedding Provider

@small-eight commented on GitHub (Feb 5, 2025): 该问题是在处理嵌入文本使用默认的anythingLLM enbedder的时候出现，具体原因未知，怀疑请求有问题。 我有个临时解决方案：修改embedder AI Providers ->embedder ->Embedding Provider 修改成本地或者其他的 i have a temporary solution: Modify the embedder AI Providers ->embedder ->Embedding Provider
yindo commented 2026-02-22 18:27:39 -05:00
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@mainliner commented on GitHub (Feb 5, 2025):

How are you running AnythingLLM?

Docker (local)

What happened?

上传文档时报：错误：1 个文档 添加失败。 禁止访问文件： “https://cdn.useanything.com/ support/models/Xenova/all- MiniLM-L6-v2/config.json”。怎么解决？

Are there known steps to reproduce?

No response

使用CMD启动anythingLLM后，就可以进入Debug mode执行，发现log中显示问题出在去huggingface下载Xenova/all-MiniLM-L6-v2失败。可能是网络环境的原因。
解决方法是自己去huggingface下载这个模型，并将其放在C:\Users<usr>\AppData\Roaming\anythingllm-desktop\storage\models 文件夹下。

Download the Xenova/all-MiniLM-L6-v2 manually from huggingface, and copy the Xenove foder to C:\Users<usr>\AppData\Roaming\anythingllm-desktop\storage\models will fix this issue which because the network block that you can not download the model from huggingface by AnythingLLM

@mainliner commented on GitHub (Feb 5, 2025): > ### How are you running AnythingLLM? > Docker (local) > > ### What happened? > 上传文档时报：错误：1 个文档 添加失败。 禁止访问文件： “https://cdn.useanything.com/ support/models/Xenova/all- MiniLM-L6-v2/config.json”。怎么解决？ > > ### Are there known steps to reproduce? > _No response_ 使用CMD启动anythingLLM后，就可以进入Debug mode执行，发现log中显示问题出在去huggingface下载Xenova/all-MiniLM-L6-v2失败。可能是网络环境的原因。 解决方法是自己去huggingface下载这个模型，并将其放在C:\Users\<usr>\AppData\Roaming\anythingllm-desktop\storage\models 文件夹下。 Download the Xenova/all-MiniLM-L6-v2 manually from huggingface, and copy the Xenove foder to C:\Users\<usr>\AppData\Roaming\anythingllm-desktop\storage\models will fix this issue which because the network block that you can not download the model from huggingface by AnythingLLM
yindo commented 2026-02-22 18:27:39 -05:00
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@seedlings-calm commented on GitHub (Feb 5, 2025):

time=2025-02-05T17:56:02.859+08:00 level=INFO source=routes.go:1238 msg="Listening on 127.0.0.1:11434 (version 0.5.7)"
time=2025-02-05T17:56:02.860+08:00 level=INFO source=routes.go:1267 msg="Dynamic LLM libraries" runners=[cpu]
time=2025-02-05T17:56:02.860+08:00 level=INFO source=types.go:131 msg="inference compute" id="" library=cpu variant=avx2 compute="" driver=0.0 name="" total="16.0 GiB" available="9.7 GiB"
[GIN] 2025/02/05 - 17:56:34 | 200 | 96.331µs | 127.0.0.1 | HEAD "/"
time=2025-02-05T17:56:34.182+08:00 level=INFO source=server.go:104 msg="system memory" total="16.0 GiB" free="9.7 GiB" free_swap="0 B"
time=2025-02-05T17:56:34.183+08:00 level=INFO source=memory.go:356 msg="offload to cpu" layers.requested=-1 layers.model=29 layers.offload=0 layers.split="" memory.available="[9.7 GiB]" memory.gpu_overhead="0 B" memory.required.full="5.1 GiB" memory.required.partial="0 B" memory.required.kv="448.0 MiB" memory.required.allocations="[5.1 GiB]" memory.weights.total="4.1 GiB" memory.weights.repeating="3.7 GiB" memory.weights.nonrepeating="426.4 MiB" memory.graph.full="478.0 MiB" memory.graph.partial="730.4 MiB"
time=2025-02-05T17:56:34.184+08:00 level=INFO source=server.go:376 msg="starting llama server" cmd="/usr/local/bin/ollama runner --model /Volumes/T7/AI/models/blobs/sha256-96c415656d377afbff962f6cdb2394ab092ccbcbaab4b82525bc4ca800fe8a49 --ctx-size 8192 --batch-size 512 --threads 6 --no-mmap --parallel 4 --port 53587"
time=2025-02-05T17:56:34.186+08:00 level=INFO source=sched.go:449 msg="loaded runners" count=1
time=2025-02-05T17:56:34.186+08:00 level=INFO source=server.go:555 msg="waiting for llama runner to start responding"
time=2025-02-05T17:56:34.190+08:00 level=INFO source=server.go:589 msg="waiting for server to become available" status="llm server error"
time=2025-02-05T17:56:34.210+08:00 level=INFO source=runner.go:936 msg="starting go runner"
time=2025-02-05T17:56:34.211+08:00 level=INFO source=runner.go:937 msg=system info="CPU : SSE3 = 1 | SSSE3 = 1 | LLAMAFILE = 1 | AARCH64_REPACK = 1 | CPU : SSE3 = 1 | SSSE3 = 1 | LLAMAFILE = 1 | AARCH64_REPACK = 1 | cgo(clang)" threads=6
time=2025-02-05T17:56:34.212+08:00 level=INFO source=runner.go:995 msg="Server listening on 127.0.0.1:53587"
llama_model_loader: loaded meta data with 26 key-value pairs and 339 tensors from /Volumes/T7/AI/models/blobs/sha256-96c415656d377afbff962f6cdb2394ab092ccbcbaab4b82525bc4ca800fe8a49 (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 = qwen2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = DeepSeek R1 Distill Qwen 7B
llama_model_loader: - kv 3: general.basename str = DeepSeek-R1-Distill-Qwen
llama_model_loader: - kv 4: general.size_label str = 7B
llama_model_loader: - kv 5: qwen2.block_count u32 = 28
llama_model_loader: - kv 6: qwen2.context_length u32 = 131072
llama_model_loader: - kv 7: qwen2.embedding_length u32 = 3584
llama_model_loader: - kv 8: qwen2.feed_forward_length u32 = 18944
llama_model_loader: - kv 9: qwen2.attention.head_count u32 = 28
llama_model_loader: - kv 10: qwen2.attention.head_count_kv u32 = 4
llama_model_loader: - kv 11: qwen2.rope.freq_base f32 = 10000.000000
llama_model_loader: - kv 12: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 13: general.file_type u32 = 15
llama_model_loader: - kv 14: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 15: tokenizer.ggml.pre str = qwen2
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,152064] = ["!", """, "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 18: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 151646
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 151643
llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {% if not add_generation_prompt is de...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - type f32: 141 tensors
llama_model_loader: - type q4_K: 169 tensors
llama_model_loader: - type q6_K: 29 tensors
time=2025-02-05T17:56:34.442+08:00 level=INFO source=server.go:589 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
llm_load_vocab: special tokens cache size = 22
llm_load_vocab: token to piece cache size = 0.9310 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 152064
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 3584
llm_load_print_meta: n_layer = 28
llm_load_print_meta: n_head = 28
llm_load_print_meta: n_head_kv = 4
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 7
llm_load_print_meta: n_embd_k_gqa = 512
llm_load_print_meta: n_embd_v_gqa = 512
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
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 = 18944
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 = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
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: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 7B
llm_load_print_meta: model ftype = Q4_K - Medium
llm_load_print_meta: model params = 7.62 B
llm_load_print_meta: model size = 4.36 GiB (4.91 BPW)
llm_load_print_meta: general.name = DeepSeek R1 Distill Qwen 7B
llm_load_print_meta: BOS token = 151646 '<｜begin▁of▁sentence｜>'
llm_load_print_meta: EOS token = 151643 '<｜end▁of▁sentence｜>'
llm_load_print_meta: EOT token = 151643 '<｜end▁of▁sentence｜>'
llm_load_print_meta: PAD token = 151643 '<｜end▁of▁sentence｜>'
llm_load_print_meta: LF token = 148848 'ÄĬ'
llm_load_print_meta: FIM PRE token = 151659 '<|fim_prefix|>'
llm_load_print_meta: FIM SUF token = 151661 '<|fim_suffix|>'
llm_load_print_meta: FIM MID token = 151660 '<|fim_middle|>'
llm_load_print_meta: FIM PAD token = 151662 '<|fim_pad|>'
llm_load_print_meta: FIM REP token = 151663 '<|repo_name|>'
llm_load_print_meta: FIM SEP token = 151664 '<|file_sep|>'
llm_load_print_meta: EOG token = 151643 '<｜end▁of▁sentence｜>'
llm_load_print_meta: EOG token = 151662 '<|fim_pad|>'
llm_load_print_meta: EOG token = 151663 '<|repo_name|>'
llm_load_print_meta: EOG token = 151664 '<|file_sep|>'
llm_load_print_meta: max token length = 256
llm_load_tensors: CPU model buffer size = 4460.45 MiB
llama_new_context_with_model: n_seq_max = 4
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_ctx_per_seq = 2048
llama_new_context_with_model: n_batch = 2048
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: n_ctx_per_seq (2048) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
llama_kv_cache_init: kv_size = 8192, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 28, can_shift = 1
llama_kv_cache_init: CPU KV buffer size = 448.00 MiB
llama_new_context_with_model: KV self size = 448.00 MiB, K (f16): 224.00 MiB, V (f16): 224.00 MiB
llama_new_context_with_model: CPU output buffer size = 2.38 MiB
llama_new_context_with_model: CPU compute buffer size = 492.01 MiB
llama_new_context_with_model: graph nodes = 986
llama_new_context_with_model: graph splits = 1
time=2025-02-05T17:56:40.206+08:00 level=INFO source=server.go:594 msg="llama runner started in 6.02 seconds"
time=2025-02-05T17:56:40.233+08:00 level=WARN source=runner.go:129 msg="truncating input prompt" limit=2048 prompt=7145 keep=1 new=2048

这是我上传得到的日志，一直卡着上传不成功， embedder
AI Providers ->embedder 选择的是ollama

@seedlings-calm commented on GitHub (Feb 5, 2025): time=2025-02-05T17:56:02.859+08:00 level=INFO source=routes.go:1238 msg="Listening on 127.0.0.1:11434 (version 0.5.7)" time=2025-02-05T17:56:02.860+08:00 level=INFO source=routes.go:1267 msg="Dynamic LLM libraries" runners=[cpu] time=2025-02-05T17:56:02.860+08:00 level=INFO source=types.go:131 msg="inference compute" id="" library=cpu variant=avx2 compute="" driver=0.0 name="" total="16.0 GiB" available="9.7 GiB" [GIN] 2025/02/05 - 17:56:34 | 200 | 96.331µs | 127.0.0.1 | HEAD "/" time=2025-02-05T17:56:34.182+08:00 level=INFO source=server.go:104 msg="system memory" total="16.0 GiB" free="9.7 GiB" free_swap="0 B" time=2025-02-05T17:56:34.183+08:00 level=INFO source=memory.go:356 msg="offload to cpu" layers.requested=-1 layers.model=29 layers.offload=0 layers.split="" memory.available="[9.7 GiB]" memory.gpu_overhead="0 B" memory.required.full="5.1 GiB" memory.required.partial="0 B" memory.required.kv="448.0 MiB" memory.required.allocations="[5.1 GiB]" memory.weights.total="4.1 GiB" memory.weights.repeating="3.7 GiB" memory.weights.nonrepeating="426.4 MiB" memory.graph.full="478.0 MiB" memory.graph.partial="730.4 MiB" time=2025-02-05T17:56:34.184+08:00 level=INFO source=server.go:376 msg="starting llama server" cmd="/usr/local/bin/ollama runner --model /Volumes/T7/AI/models/blobs/sha256-96c415656d377afbff962f6cdb2394ab092ccbcbaab4b82525bc4ca800fe8a49 --ctx-size 8192 --batch-size 512 --threads 6 --no-mmap --parallel 4 --port 53587" time=2025-02-05T17:56:34.186+08:00 level=INFO source=sched.go:449 msg="loaded runners" count=1 time=2025-02-05T17:56:34.186+08:00 level=INFO source=server.go:555 msg="waiting for llama runner to start responding" time=2025-02-05T17:56:34.190+08:00 level=INFO source=server.go:589 msg="waiting for server to become available" status="llm server error" time=2025-02-05T17:56:34.210+08:00 level=INFO source=runner.go:936 msg="starting go runner" time=2025-02-05T17:56:34.211+08:00 level=INFO source=runner.go:937 msg=system info="CPU : SSE3 = 1 | SSSE3 = 1 | LLAMAFILE = 1 | AARCH64_REPACK = 1 | CPU : SSE3 = 1 | SSSE3 = 1 | LLAMAFILE = 1 | AARCH64_REPACK = 1 | cgo(clang)" threads=6 time=2025-02-05T17:56:34.212+08:00 level=INFO source=runner.go:995 msg="Server listening on 127.0.0.1:53587" llama_model_loader: loaded meta data with 26 key-value pairs and 339 tensors from /Volumes/T7/AI/models/blobs/sha256-96c415656d377afbff962f6cdb2394ab092ccbcbaab4b82525bc4ca800fe8a49 (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 = qwen2 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = DeepSeek R1 Distill Qwen 7B llama_model_loader: - kv 3: general.basename str = DeepSeek-R1-Distill-Qwen llama_model_loader: - kv 4: general.size_label str = 7B llama_model_loader: - kv 5: qwen2.block_count u32 = 28 llama_model_loader: - kv 6: qwen2.context_length u32 = 131072 llama_model_loader: - kv 7: qwen2.embedding_length u32 = 3584 llama_model_loader: - kv 8: qwen2.feed_forward_length u32 = 18944 llama_model_loader: - kv 9: qwen2.attention.head_count u32 = 28 llama_model_loader: - kv 10: qwen2.attention.head_count_kv u32 = 4 llama_model_loader: - kv 11: qwen2.rope.freq_base f32 = 10000.000000 llama_model_loader: - kv 12: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 13: general.file_type u32 = 15 llama_model_loader: - kv 14: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 15: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,152064] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,152064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 18: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 151646 llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 151643 llama_model_loader: - kv 21: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 24: tokenizer.chat_template str = {% if not add_generation_prompt is de... llama_model_loader: - kv 25: general.quantization_version u32 = 2 llama_model_loader: - type f32: 141 tensors llama_model_loader: - type q4_K: 169 tensors llama_model_loader: - type q6_K: 29 tensors time=2025-02-05T17:56:34.442+08:00 level=INFO source=server.go:589 msg="waiting for server to become available" status="llm server loading model" llm_load_vocab: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect llm_load_vocab: special tokens cache size = 22 llm_load_vocab: token to piece cache size = 0.9310 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = qwen2 llm_load_print_meta: vocab type = BPE llm_load_print_meta: n_vocab = 152064 llm_load_print_meta: n_merges = 151387 llm_load_print_meta: vocab_only = 0 llm_load_print_meta: n_ctx_train = 131072 llm_load_print_meta: n_embd = 3584 llm_load_print_meta: n_layer = 28 llm_load_print_meta: n_head = 28 llm_load_print_meta: n_head_kv = 4 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_swa = 0 llm_load_print_meta: n_embd_head_k = 128 llm_load_print_meta: n_embd_head_v = 128 llm_load_print_meta: n_gqa = 7 llm_load_print_meta: n_embd_k_gqa = 512 llm_load_print_meta: n_embd_v_gqa = 512 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-06 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 = 18944 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 = 2 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 131072 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: ssm_dt_b_c_rms = 0 llm_load_print_meta: model type = 7B llm_load_print_meta: model ftype = Q4_K - Medium llm_load_print_meta: model params = 7.62 B llm_load_print_meta: model size = 4.36 GiB (4.91 BPW) llm_load_print_meta: general.name = DeepSeek R1 Distill Qwen 7B llm_load_print_meta: BOS token = 151646 '<｜begin▁of▁sentence｜>' llm_load_print_meta: EOS token = 151643 '<｜end▁of▁sentence｜>' llm_load_print_meta: EOT token = 151643 '<｜end▁of▁sentence｜>' llm_load_print_meta: PAD token = 151643 '<｜end▁of▁sentence｜>' llm_load_print_meta: LF token = 148848 'ÄĬ' llm_load_print_meta: FIM PRE token = 151659 '<|fim_prefix|>' llm_load_print_meta: FIM SUF token = 151661 '<|fim_suffix|>' llm_load_print_meta: FIM MID token = 151660 '<|fim_middle|>' llm_load_print_meta: FIM PAD token = 151662 '<|fim_pad|>' llm_load_print_meta: FIM REP token = 151663 '<|repo_name|>' llm_load_print_meta: FIM SEP token = 151664 '<|file_sep|>' llm_load_print_meta: EOG token = 151643 '<｜end▁of▁sentence｜>' llm_load_print_meta: EOG token = 151662 '<|fim_pad|>' llm_load_print_meta: EOG token = 151663 '<|repo_name|>' llm_load_print_meta: EOG token = 151664 '<|file_sep|>' llm_load_print_meta: max token length = 256 llm_load_tensors: CPU model buffer size = 4460.45 MiB llama_new_context_with_model: n_seq_max = 4 llama_new_context_with_model: n_ctx = 8192 llama_new_context_with_model: n_ctx_per_seq = 2048 llama_new_context_with_model: n_batch = 2048 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: flash_attn = 0 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_new_context_with_model: n_ctx_per_seq (2048) < n_ctx_train (131072) -- the full capacity of the model will not be utilized llama_kv_cache_init: kv_size = 8192, offload = 1, type_k = 'f16', type_v = 'f16', n_layer = 28, can_shift = 1 llama_kv_cache_init: CPU KV buffer size = 448.00 MiB llama_new_context_with_model: KV self size = 448.00 MiB, K (f16): 224.00 MiB, V (f16): 224.00 MiB llama_new_context_with_model: CPU output buffer size = 2.38 MiB llama_new_context_with_model: CPU compute buffer size = 492.01 MiB llama_new_context_with_model: graph nodes = 986 llama_new_context_with_model: graph splits = 1 time=2025-02-05T17:56:40.206+08:00 level=INFO source=server.go:594 msg="llama runner started in 6.02 seconds" time=2025-02-05T17:56:40.233+08:00 level=WARN source=runner.go:129 msg="truncating input prompt" limit=2048 prompt=7145 keep=1 new=2048 这是我上传得到的日志，一直卡着上传不成功， embedder AI Providers ->embedder 选择的是ollama
yindo commented 2026-02-22 18:27:39 -05:00
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@irebit commented on GitHub (Feb 5, 2025):

更换成ollama支持的其他模型就可以了，大家可以试下。
ollama pull nomic-embed-text

@irebit commented on GitHub (Feb 5, 2025): 更换成ollama支持的其他模型就可以了，大家可以试下。 ollama pull nomic-embed-text
yindo commented 2026-02-22 18:27:39 -05:00
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@coolleaf commented on GitHub (Feb 5, 2025):

How are you running AnythingLLM?

Docker (local)

What happened?

上传文档时报：错误：1 个文档 添加失败。 禁止访问文件： “https://cdn.useanything.com/ support/models/Xenova/all- MiniLM-L6-v2/config.json”。怎么解决？

Are there known steps to reproduce?

No response

使用CMD启动anythingLLM后，就可以进入Debug mode执行，发现log中显示问题出在去huggingface下载Xenova/all-MiniLM-L6-v2失败。可能是网络环境的原因。 解决方法是自己去huggingface下载这个模型，并将其放在C:\Users\AppData\Roaming\anythingllm-desktop\storage\models 文件夹下。

Download the Xenova/all-MiniLM-L6-v2 manually from huggingface, and copy the Xenove foder to C:\Users\AppData\Roaming\anythingllm-desktop\storage\models will fix this issue which because the network block that you can not download the model from huggingface by AnythingLLM

这个是问题所在，似乎是由于某些原因，自动下载模型的网址无法访问，改善网络的连通性有助于这个问题的解决。

手动下载这些文件也是一个很好的选择。

我提供一个刚刚下载的文件包，

解压缩到: \AppData\Roaming\anythingllm-desktop\storage\models 可以改善这一问题。

This is where the problem lies. It seems that, for some reason, the URL for automatic model downloads is inaccessible. Improving network connectivity can help resolve this issue.

Manually downloading these files is also a good option.

I am providing a recently downloaded file package.

Extract it to: \AppData\Roaming\anythingllm-desktop\storage\models to help resolve the issue.

Xenova.zip

@coolleaf commented on GitHub (Feb 5, 2025): > > ### How are you running AnythingLLM? > > Docker (local) > > ### What happened? > > 上传文档时报：错误：1 个文档 添加失败。 禁止访问文件： “https://cdn.useanything.com/ support/models/Xenova/all- MiniLM-L6-v2/config.json”。怎么解决？ > > ### Are there known steps to reproduce? > > _No response_ > > 使用CMD启动anythingLLM后，就可以进入Debug mode执行，发现log中显示问题出在去huggingface下载Xenova/all-MiniLM-L6-v2失败。可能是网络环境的原因。 解决方法是自己去huggingface下载这个模型，并将其放在C:\Users<usr>\AppData\Roaming\anythingllm-desktop\storage\models 文件夹下。 > > Download the Xenova/all-MiniLM-L6-v2 manually from huggingface, and copy the Xenove foder to C:\Users<usr>\AppData\Roaming\anythingllm-desktop\storage\models will fix this issue which because the network block that you can not download the model from huggingface by AnythingLLM 这个是问题所在，似乎是由于某些原因，自动下载模型的网址无法访问，改善网络的连通性有助于这个问题的解决。 手动下载这些文件也是一个很好的选择。 我提供一个刚刚下载的文件包， 解压缩到: \AppData\Roaming\anythingllm-desktop\storage\models 可以改善这一问题。 This is where the problem lies. It seems that, for some reason, the URL for automatic model downloads is inaccessible. Improving network connectivity can help resolve this issue. Manually downloading these files is also a good option. I am providing a recently downloaded file package. Extract it to: \AppData\Roaming\anythingllm-desktop\storage\models to help resolve the issue. [Xenova.zip](https://github.com/user-attachments/files/18676036/Xenova.zip)
yindo commented 2026-02-22 18:27:39 -05:00
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@timothycarambat commented on GitHub (Feb 5, 2025):

Due to a DDOS coming directly from china we have suspended all CN-based IPs from accessing our mirror. That is why you get this error. We run the mirror for free, but when it is constantly abused like is was, we cannot just sit and do nothing while a huge CDN bill racks up.

I hope to undo the block in the next update. But for now, that will be the case.

@timothycarambat commented on GitHub (Feb 5, 2025): Due to a DDOS coming directly from china we have suspended all CN-based IPs from accessing our mirror. That is why you get this error. We run the mirror for free, but when it is constantly abused like is was, we cannot just sit and do nothing while a huge CDN bill racks up. I hope to undo the block in the next update. But for now, that will be the case.
yindo changed title from [BUG]: 上传文档时报错误 to [GH-ISSUE #3113] [BUG]: 上传文档时报错误 2026-06-05 14:43:55 -04:00
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Reference: Mintplex-Labs/anything-llm#1994
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