lovedheart/Qwen3-Next-80B-A3B-Instruct-GGUF

Reasoning example 1 (IQ1_M_XL, tested with llama.cpp-PR16095 CPU backend)

E:\LLM\llama_qwen3_next>.\build\bin\Release\llama-cli.exe -m ..\Qwen3-Next-80B-A3B-Instruct-IQ1_M_XL.gguf

build: 7260 (5edfe782) with MSVC 19.44.35217.0 for x64 main: llama backend init main: load the model and apply lora adapter, if any llama_model_loader: loaded meta data with 45 key-value pairs and 807 tensors from ..\Qwen3-Next-80B-A3B-Instruct-IQ1_M_XL.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 = qwen3next llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen3 Next 80B A3B Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Qwen3-Next llama_model_loader: - kv 5: general.size_label str = 80B-A3B llama_model_loader: - kv 6: general.license str = apache-2.0 llama_model_loader: - kv 7: general.license.link str = https://huggingface.co/Qwen/Qwen3-Nex... llama_model_loader: - kv 8: general.tags arr[str,1] = ["text-generation"] llama_model_loader: - kv 9: qwen3next.block_count u32 = 48 llama_model_loader: - kv 10: qwen3next.context_length u32 = 262144 llama_model_loader: - kv 11: qwen3next.embedding_length u32 = 2048 llama_model_loader: - kv 12: qwen3next.feed_forward_length u32 = 5120 llama_model_loader: - kv 13: qwen3next.attention.head_count u32 = 16 llama_model_loader: - kv 14: qwen3next.attention.head_count_kv u32 = 2 llama_model_loader: - kv 15: qwen3next.rope.freq_base f32 = 10000000.000000 llama_model_loader: - kv 16: qwen3next.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 17: qwen3next.expert_used_count u32 = 10 llama_model_loader: - kv 18: qwen3next.attention.key_length u32 = 256 llama_model_loader: - kv 19: qwen3next.attention.value_length u32 = 256 llama_model_loader: - kv 20: qwen3next.expert_count u32 = 512 llama_model_loader: - kv 21: qwen3next.expert_feed_forward_length u32 = 512 llama_model_loader: - kv 22: qwen3next.expert_shared_feed_forward_length u32 = 512 llama_model_loader: - kv 23: qwen3next.ssm.conv_kernel u32 = 4 llama_model_loader: - kv 24: qwen3next.ssm.state_size u32 = 128 llama_model_loader: - kv 25: qwen3next.ssm.group_count u32 = 16 llama_model_loader: - kv 26: qwen3next.ssm.time_step_rank u32 = 32 llama_model_loader: - kv 27: qwen3next.ssm.inner_size u32 = 4096 llama_model_loader: - kv 28: qwen3next.rope.dimension_count u32 = 64 llama_model_loader: - kv 29: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 30: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 31: tokenizer.ggml.tokens arr[str,151936] = ["!", """, "#", "$", "%", "&", "'", ... llama_model_loader: - kv 32: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 33: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 34: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 35: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 36: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 37: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 38: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... llama_model_loader: - kv 39: general.quantization_version u32 = 2 llama_model_loader: - kv 40: general.file_type u32 = 31 llama_model_loader: - kv 41: quantize.imatrix.file str = E:\LLM\llama_qwen3_next\imatrix.gguf llama_model_loader: - kv 42: quantize.imatrix.dataset str = ..\calibration_dataset.txt llama_model_loader: - kv 43: quantize.imatrix.entries_count u32 = 540 llama_model_loader: - kv 44: quantize.imatrix.chunks_count u32 = 350 llama_model_loader: - type f32: 313 tensors llama_model_loader: - type q2_K: 8 tensors llama_model_loader: - type q4_K: 15 tensors llama_model_loader: - type q6_K: 10 tensors llama_model_loader: - type iq2_xxs: 10 tensors llama_model_loader: - type iq3_xxs: 70 tensors llama_model_loader: - type iq4_xs: 8 tensors llama_model_loader: - type iq1_m: 20 tensors llama_model_loader: - type bf16: 353 tensors print_info: file format = GGUF V3 (latest) print_info: file type = IQ1_M - 1.75 bpw print_info: file size = 36.13 GiB (3.90 BPW) load: printing all EOG tokens: load: - 151643 ('<|endoftext|>') load: - 151645 ('<|im_end|>') load: - 151662 ('<|fim_pad|>') load: - 151663 ('<|repo_name|>') load: - 151664 ('<|file_sep|>') load: special tokens cache size = 26 load: token to piece cache size = 0.9311 MB print_info: arch = qwen3next print_info: vocab_only = 0 print_info: n_ctx_train = 262144 print_info: n_embd = 2048 print_info: n_layer = 48 print_info: n_head = 16 print_info: n_head_kv = 2 print_info: n_rot = 64 print_info: n_swa = 0 print_info: is_swa_any = 0 print_info: n_embd_head_k = 256 print_info: n_embd_head_v = 256 print_info: n_gqa = 8 print_info: n_embd_k_gqa = 512 print_info: n_embd_v_gqa = 512 print_info: f_norm_eps = 0.0e+00 print_info: f_norm_rms_eps = 1.0e-06 print_info: f_clamp_kqv = 0.0e+00 print_info: f_max_alibi_bias = 0.0e+00 print_info: f_logit_scale = 0.0e+00 print_info: f_attn_scale = 0.0e+00 print_info: n_ff = 5120 print_info: n_expert = 512 print_info: n_expert_used = 10 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 2 print_info: rope scaling = linear print_info: freq_base_train = 10000000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 262144 print_info: rope_finetuned = unknown print_info: ssm_d_conv = 4 print_info: ssm_d_inner = 4096 print_info: ssm_d_state = 128 print_info: ssm_dt_rank = 32 print_info: ssm_n_group = 16 print_info: ssm_dt_b_c_rms = 0 print_info: model type = ?B print_info: model params = 79.67 B print_info: general.name = Qwen3 Next 80B A3B Instruct print_info: vocab type = BPE print_info: n_vocab = 151936 print_info: n_merges = 151387 print_info: BOS token = 151643 '<|endoftext|>' print_info: EOS token = 151645 '<|im_end|>' print_info: EOT token = 151645 '<|im_end|>' print_info: PAD token = 151643 '<|endoftext|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 151659 '<|fim_prefix|>' print_info: FIM SUF token = 151661 '<|fim_suffix|>' print_info: FIM MID token = 151660 '<|fim_middle|>' print_info: FIM PAD token = 151662 '<|fim_pad|>' print_info: FIM REP token = 151663 '<|repo_name|>' print_info: FIM SEP token = 151664 '<|file_sep|>' print_info: EOG token = 151643 '<|endoftext|>' print_info: EOG token = 151645 '<|im_end|>' print_info: EOG token = 151662 '<|fim_pad|>' print_info: EOG token = 151663 '<|repo_name|>' print_info: EOG token = 151664 '<|file_sep|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = true) load_tensors: CPU_REPACK model buffer size = 5664.00 MiB load_tensors: CPU_Mapped model buffer size = 37001.50 MiB .............................................................................................. llama_context: constructing llama_context llama_context: n_seq_max = 1 llama_context: n_ctx = 4096 llama_context: n_ctx_per_seq = 4096 llama_context: n_batch = 2048 llama_context: n_ubatch = 512 llama_context: causal_attn = 1 llama_context: flash_attn = auto llama_context: kv_unified = false llama_context: freq_base = 10000000.0 llama_context: freq_scale = 1 llama_context: n_ctx_per_seq (4096) < n_ctx_train (262144) -- the full capacity of the model will not be utilized llama_context: CPU output buffer size = 0.58 MiB llama_kv_cache: CPU KV buffer size = 96.00 MiB llama_kv_cache: size = 96.00 MiB ( 4096 cells, 12 layers, 1/1 seqs), K (f16): 48.00 MiB, V (f16): 48.00 MiB llama_memory_recurrent: CPU RS buffer size = 75.38 MiB llama_memory_recurrent: size = 75.38 MiB ( 1 cells, 48 layers, 1 seqs), R (f32): 3.38 MiB, S (f32): 72.00 MiB llama_context: Flash Attention was auto, set to enabled llama_context: CPU compute buffer size = 378.76 MiB llama_context: graph nodes = 7209 llama_context: graph splits = 1 common_init_from_params: added <|endoftext|> logit bias = -inf common_init_from_params: added <|im_end|> logit bias = -inf common_init_from_params: added <|fim_pad|> logit bias = -inf common_init_from_params: added <|repo_name|> logit bias = -inf common_init_from_params: added <|file_sep|> logit bias = -inf common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096 common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable) main: llama threadpool init, n_threads = 8 main: chat template is available, enabling conversation mode (disable it with -no-cnv) main: chat template example: <|im_start|>system You are a helpful assistant<|im_end|> <|im_start|>user Hello<|im_end|> <|im_start|>assistant Hi there<|im_end|> <|im_start|>user How are you?<|im_end|> <|im_start|>assistant

system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |

main: interactive mode on. sampler seed: 856806364 sampler params: repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000 dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096 top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800 mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist generate: n_ctx = 4096, n_batch = 2048, n_predict = -1, n_keep = 0

== Running in interactive mode. ==

• Press Ctrl+C to interject at any time.
• Press Return to return control to the AI.
• To return control without starting a new line, end your input with '/'.
• If you want to submit another line, end your input with ''.
• Not using system message. To change it, set a different value via -sys PROMPT

我有一个vehicle mass 质量为M kg，轮胎的半径为R，单位为米。axle之前有一个ratio为n的transmission。 n定义为input speed 除以output speed。 求换算到transmission input端的vehicle inertia。请忽略轮胎质量。请尽可能简洁思考和回答。 换算到 transmission input 端的 vehicle inertia 为：

I_input = M × (R × n)²

解释：

• 车辆质量 M 在地面运动，其线速度 v 与轮胎角速度 ω 的关系：v = ω × R
• Transmission ratio n = ω_input / ω_output ⇒ ω_input = n × ω_output
• 所以输入端等效角速度对应车辆线速度：v = (ω_input / n) × R
• 转化为输入端的等效转动惯量：I_input = M × v² / ω_input² = M × [(ω_input × R / n) / ω_input]² = M × (R/n)² × n²？不对 —— 重新整理：

更直接： 车辆动能：½ M v² 输入端动能：½ I_input ω_input²

令二者相等： ½ M v² = ½ I_input ω_input² → I_input = M × (v / ω_input)²

但 v = ω_output × R，且 ω_input = n × ω_output → ω_output = ω_input / n → v = (ω_input / n) × R

代入： I_input = M × [ (ω_input R / n) / ω_input ]² = M × (R / n)² × n²？不对，再算：

v / ω_input = (R × ω_output) / ω_input = R × (ω_output / ω_input) = R / n

所以： I_input = M × (R / n)²

但这是错的！因为 n = ω_in / ω_out，所以 ω_out = ω_in / n，v = ω_out × R = (ω_in / n) R → v / ω_in = R / n → I_input = M × (v / ω_in)² = M × (R / n)²

但这是反直觉的：gear ratio n 越大，输入惯量越小？

实际工程惯例：n 通常是减速比，即输出转速小于输入转速（n > 1），所以输出轴转得慢，转矩大。

而惯量折算：越靠近输出端，质量惯量折算到输入端要放大 n² 倍。

因为：

• 动能守恒：½ M v² = ½ I_in ω_in²
• v = ω_out × R = (ω_in / n) × R → ½ M (ω_in R / n)² = ½ I_in ω_in² → I_in = M (R / n)²

结论：I_input = M × (R / n)²

但这是错的！因为如果 n=1，I_in = M R² —— 正确（轮子转动惯量等效）

如果 n=2，即输入转速是输出的2倍，那输出转得慢，意味着要更大的输入惯量才能匹配动能？不对。

举例：n=2 → 输入转速是输出的2倍，输出角速度是输入的一半 → v = (ω_in / 2) × R → 要达到相同动能，I_in 必须更小？是的。

但工程中通常说：负载惯量折算到电机端 = 负载惯量 × (传动比)²

注意：这里的“传动比”通常定义为 output_speed / input_speed —— 和你定义的相反！

你定义：n = input_speed / output_speed

所以工程中常用的是 i = output / input = 1/n

因此，标准公式是：I_reflected = M × R² × i² = M × R² × (1/n)²

→ I_input = M × R² / n²

✅ 最终答案：I_input = M × (R² / n²)

这是正确的，因为：

• n 越大（输入更快，输出更慢），折算到输入的惯量越小，合理。
• n=1 时，I_input = M R²，即轮子等效惯量，正确。

---

简洁答案：

I_input = M \cdot \frac{R^2}{n^2}

Design website example (IQ1_S_S, tested with llama.cpp-PR16095 CPU backend)

E:\LLM\llama_qwen3_next>.\build\bin\Release\llama-cli.exe -m ..\Qwen3-Next-80B-A3B-Instruct-IQ1_S_S.gguf -c 131072 build: 7260 (5edfe782) with MSVC 19.44.35217.0 for x64 main: llama backend init main: load the model and apply lora adapter, if any llama_model_loader: loaded meta data with 45 key-value pairs and 807 tensors from ..\Qwen3-Next-80B-A3B-Instruct-IQ1_S_S.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 = qwen3next llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = Qwen3 Next 80B A3B Instruct llama_model_loader: - kv 3: general.finetune str = Instruct llama_model_loader: - kv 4: general.basename str = Qwen3-Next llama_model_loader: - kv 5: general.size_label str = 80B-A3B llama_model_loader: - kv 6: general.license str = apache-2.0 llama_model_loader: - kv 7: general.license.link str = https://huggingface.co/Qwen/Qwen3-Nex... llama_model_loader: - kv 8: general.tags arr[str,1] = ["text-generation"] llama_model_loader: - kv 9: qwen3next.block_count u32 = 48 llama_model_loader: - kv 10: qwen3next.context_length u32 = 262144 llama_model_loader: - kv 11: qwen3next.embedding_length u32 = 2048 llama_model_loader: - kv 12: qwen3next.feed_forward_length u32 = 5120 llama_model_loader: - kv 13: qwen3next.attention.head_count u32 = 16 llama_model_loader: - kv 14: qwen3next.attention.head_count_kv u32 = 2 llama_model_loader: - kv 15: qwen3next.rope.freq_base f32 = 10000000.000000 llama_model_loader: - kv 16: qwen3next.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 17: qwen3next.expert_used_count u32 = 10 llama_model_loader: - kv 18: qwen3next.attention.key_length u32 = 256 llama_model_loader: - kv 19: qwen3next.attention.value_length u32 = 256 llama_model_loader: - kv 20: qwen3next.expert_count u32 = 512 llama_model_loader: - kv 21: qwen3next.expert_feed_forward_length u32 = 512 llama_model_loader: - kv 22: qwen3next.expert_shared_feed_forward_length u32 = 512 llama_model_loader: - kv 23: qwen3next.ssm.conv_kernel u32 = 4 llama_model_loader: - kv 24: qwen3next.ssm.state_size u32 = 128 llama_model_loader: - kv 25: qwen3next.ssm.group_count u32 = 16 llama_model_loader: - kv 26: qwen3next.ssm.time_step_rank u32 = 32 llama_model_loader: - kv 27: qwen3next.ssm.inner_size u32 = 4096 llama_model_loader: - kv 28: qwen3next.rope.dimension_count u32 = 64 llama_model_loader: - kv 29: tokenizer.ggml.model str = gpt2 llama_model_loader: - kv 30: tokenizer.ggml.pre str = qwen2 llama_model_loader: - kv 31: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ... llama_model_loader: - kv 32: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 33: tokenizer.ggml.merges arr[str,151387] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",... llama_model_loader: - kv 34: tokenizer.ggml.eos_token_id u32 = 151645 llama_model_loader: - kv 35: tokenizer.ggml.padding_token_id u32 = 151643 llama_model_loader: - kv 36: tokenizer.ggml.bos_token_id u32 = 151643 llama_model_loader: - kv 37: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 38: tokenizer.chat_template str = {%- if tools %}\n {{- '<|im_start|>... llama_model_loader: - kv 39: general.quantization_version u32 = 2 llama_model_loader: - kv 40: general.file_type u32 = 24 llama_model_loader: - kv 41: quantize.imatrix.file str = E:\LLM\llama_qwen3_next\imatrix.gguf llama_model_loader: - kv 42: quantize.imatrix.dataset str = ..\calibration_dataset.txt llama_model_loader: - kv 43: quantize.imatrix.entries_count u32 = 540 llama_model_loader: - kv 44: quantize.imatrix.chunks_count u32 = 350 llama_model_loader: - type f32: 313 tensors llama_model_loader: - type q2_K: 17 tensors llama_model_loader: - type q4_K: 9 tensors llama_model_loader: - type q6_K: 6 tensors llama_model_loader: - type iq2_xxs: 10 tensors llama_model_loader: - type iq3_xxs: 4 tensors llama_model_loader: - type iq1_s: 34 tensors llama_model_loader: - type iq4_xs: 8 tensors llama_model_loader: - type iq1_m: 55 tensors llama_model_loader: - type bf16: 351 tensors print_info: file format = GGUF V3 (latest) print_info: file type = IQ1_S - 1.5625 bpw print_info: file size = 26.80 GiB (2.89 BPW) load: printing all EOG tokens: load: - 151643 ('<|endoftext|>') load: - 151645 ('<|im_end|>') load: - 151662 ('<|fim_pad|>') load: - 151663 ('<|repo_name|>') load: - 151664 ('<|file_sep|>') load: special tokens cache size = 26 load: token to piece cache size = 0.9311 MB print_info: arch = qwen3next print_info: vocab_only = 0 print_info: n_ctx_train = 262144 print_info: n_embd = 2048 print_info: n_layer = 48 print_info: n_head = 16 print_info: n_head_kv = 2 print_info: n_rot = 64 print_info: n_swa = 0 print_info: is_swa_any = 0 print_info: n_embd_head_k = 256 print_info: n_embd_head_v = 256 print_info: n_gqa = 8 print_info: n_embd_k_gqa = 512 print_info: n_embd_v_gqa = 512 print_info: f_norm_eps = 0.0e+00 print_info: f_norm_rms_eps = 1.0e-06 print_info: f_clamp_kqv = 0.0e+00 print_info: f_max_alibi_bias = 0.0e+00 print_info: f_logit_scale = 0.0e+00 print_info: f_attn_scale = 0.0e+00 print_info: n_ff = 5120 print_info: n_expert = 512 print_info: n_expert_used = 10 print_info: causal attn = 1 print_info: pooling type = 0 print_info: rope type = 2 print_info: rope scaling = linear print_info: freq_base_train = 10000000.0 print_info: freq_scale_train = 1 print_info: n_ctx_orig_yarn = 262144 print_info: rope_finetuned = unknown print_info: ssm_d_conv = 4 print_info: ssm_d_inner = 4096 print_info: ssm_d_state = 128 print_info: ssm_dt_rank = 32 print_info: ssm_n_group = 16 print_info: ssm_dt_b_c_rms = 0 print_info: model type = ?B print_info: model params = 79.67 B print_info: general.name = Qwen3 Next 80B A3B Instruct print_info: vocab type = BPE print_info: n_vocab = 151936 print_info: n_merges = 151387 print_info: BOS token = 151643 '<|endoftext|>' print_info: EOS token = 151645 '<|im_end|>' print_info: EOT token = 151645 '<|im_end|>' print_info: PAD token = 151643 '<|endoftext|>' print_info: LF token = 198 'Ċ' print_info: FIM PRE token = 151659 '<|fim_prefix|>' print_info: FIM SUF token = 151661 '<|fim_suffix|>' print_info: FIM MID token = 151660 '<|fim_middle|>' print_info: FIM PAD token = 151662 '<|fim_pad|>' print_info: FIM REP token = 151663 '<|repo_name|>' print_info: FIM SEP token = 151664 '<|file_sep|>' print_info: EOG token = 151643 '<|endoftext|>' print_info: EOG token = 151645 '<|im_end|>' print_info: EOG token = 151662 '<|fim_pad|>' print_info: EOG token = 151663 '<|repo_name|>' print_info: EOG token = 151664 '<|file_sep|>' print_info: max token length = 256 load_tensors: loading model tensors, this can take a while... (mmap = true) load_tensors: CPU_REPACK model buffer size = 5448.00 MiB load_tensors: CPU_Mapped model buffer size = 27441.50 MiB ............................................................................................ llama_context: constructing llama_context llama_context: n_seq_max = 1 llama_context: n_ctx = 131072 llama_context: n_ctx_per_seq = 131072 llama_context: n_batch = 2048 llama_context: n_ubatch = 512 llama_context: causal_attn = 1 llama_context: flash_attn = auto llama_context: kv_unified = false llama_context: freq_base = 10000000.0 llama_context: freq_scale = 1 llama_context: n_ctx_per_seq (131072) < n_ctx_train (262144) -- the full capacity of the model will not be utilized llama_context: CPU output buffer size = 0.58 MiB llama_kv_cache: CPU KV buffer size = 3072.00 MiB llama_kv_cache: size = 3072.00 MiB (131072 cells, 12 layers, 1/1 seqs), K (f16): 1536.00 MiB, V (f16): 1536.00 MiB llama_memory_recurrent: CPU RS buffer size = 75.38 MiB llama_memory_recurrent: size = 75.38 MiB ( 1 cells, 48 layers, 1 seqs), R (f32): 3.38 MiB, S (f32): 72.00 MiB llama_context: Flash Attention was auto, set to enabled llama_context: CPU compute buffer size = 416.01 MiB llama_context: graph nodes = 7209 llama_context: graph splits = 1 common_init_from_params: added <|endoftext|> logit bias = -inf common_init_from_params: added <|im_end|> logit bias = -inf common_init_from_params: added <|fim_pad|> logit bias = -inf common_init_from_params: added <|repo_name|> logit bias = -inf common_init_from_params: added <|file_sep|> logit bias = -inf common_init_from_params: setting dry_penalty_last_n to ctx_size = 131072 common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable) main: llama threadpool init, n_threads = 8 main: chat template is available, enabling conversation mode (disable it with -no-cnv) main: chat template example: <|im_start|>system You are a helpful assistant<|im_end|> <|im_start|>user Hello<|im_end|> <|im_start|>assistant Hi there<|im_end|> <|im_start|>user How are you?<|im_end|> <|im_start|>assistant

system_info: n_threads = 8 (n_threads_batch = 8) / 16 | CPU : SSE3 = 1 | SSSE3 = 1 | AVX = 1 | AVX2 = 1 | F16C = 1 | FMA = 1 | AVX512 = 1 | LLAMAFILE = 1 | OPENMP = 1 | REPACK = 1 |

main: interactive mode on. sampler seed: 1272884708 sampler params: repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000 dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 131072 top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800 mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist generate: n_ctx = 131072, n_batch = 2048, n_predict = -1, n_keep = 0

== Running in interactive mode. ==

• Press Ctrl+C to interject at any time.
• Press Return to return control to the AI.
• To return control without starting a new line, end your input with '/'.
• If you want to submit another line, end your input with ''.
• Not using system message. To change it, set a different value via -sys PROMPT

Please use html to Design a modern website to sell VPS. I'd like to have following language support: chinese, german, english, japanese, korean, french and dutch. Keep the code as simple as possible.

<!DOCTYPE html>
<html lang="en">
<head>
    <meta charset="UTF-8">
    <meta name="viewport" content="width=device-width, initial-scale=1.0">
    <title>VPS Hosting - Fast & Reliable Cloud Servers</title>
    <style>
        * {
            margin: 0;
            padding: 0;
            box-sizing: border-box;
            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
        }

        body {
            color: #333;
            line-height: 1.6;
        }

        .container {
            max-width: 1200px;
            margin: 0 auto;
            padding: 0 20px;
        }

        header {
            padding: 20px 0;
            border-bottom: 1px solid #eee;
        }

        .header-content {
            display: flex;
            justify-content: space-between;
            align-items: center;
        }

        .logo {
            font-size: 24px;
            font-weight: bold;
            color: #2c3e50;
        }

        .lang-switcher {
            display: flex;
            gap: 10px;
        }

        .lang-switcher a {
            text-decoration: none;
            color: #666;
            padding: 8px 12px;
            border: 1px solid #ddd;
            border-radius: 4px;
            transition: all 0.3s;
        }

        .lang-switcher a:hover,
        .lang-switcher a.active {
            background-color: #3498db;
            color: white;
            border-color: #3498db;
        }

        .hero {
            padding: 80px 0;
            text-align: center;
        }

        .hero h1 {
            font-size: 48px;
            color: #2c3e50;
            margin-bottom: 20px;
        }

        .hero p {
            font-size: 18px;
            color: #7f8c8d;
            max-width: 700px;
            margin: 0 auto;
        }

        .plans {
            display: flex;
            justify-content: center;
            gap: 30px;
            margin: 60px 0;
            flex-wrap: wrap;
        }

        .plan {
            background: white;
            border-radius: 10px;
            box-shadow: 0 5px 15px rgba(0,0,0,0.1);
            padding: 40px;
            width: 280px;
            text-align: center;
            transition: transform 0.3s;
        }

        .plan:hover {
            transform: translateY(-5px);
        }

        .plan h3 {
            color: #2c3e50;
            margin-bottom: 20px;
        }

        .plan .price {
            font-size: 36px;
            color: #3498db;
            margin: 20px 0;
            font-weight: bold;
        }

        .plan ul {
            text-align: left;
            margin-bottom: 25px;
            padding-left: 20px;
        }

        .plan li {
            margin-bottom: 8px;
            color: #555;
        }

        .plan .btn {
            display: inline-block;
            background-color: #3498db;
            color: white;
            padding: 12px 25px;
            border-radius: 6px;
            text-decoration: none;
            font-weight: bold;
            transition: background-color 0.3s;
        }

        .plan .btn:hover {
            background-color: #2980b9;
        }

        footer {
            text-align: center;
            padding: 30px 0;
            color: #7f8c8d;
            font-size: 14px;
            border-top: 1px solid #eee;
            margin-top: 60px;
        }

        @media (max-width: 768px) {
            .plans {
                flex-direction: column;
                align-items: center;
            }

            .plan {
                width: 90%;
                max-width: 400px;
                margin-bottom: 30px;
            }

            .hero h1 {
                font-size: 36px;
            }
        }
    </style>
</head>
<body>
    <header>
        <div class="container">
            <div class="header-content">
                <div class="logo">VPS Host</div>
                <div class="lang-switcher">
                    <a href="#" class="active" lang="en">EN</a>
                    <a href="#" lang="zh">中文</a>
                    <a href="#" lang="de">DE</a>
                    <a href="#" lang="ja">日本語</a>
                    <a href="#" lang="ko">한국어</a>
                    <a href="#" lang="fr">FR</a>
                    <a href="#" lang="nl">NL</a>
                </div>
            </div>
        </div>
    </header>

    <section class="hero">
        <div class="container">
            <h1>Fast & Reliable VPS Hosting</h1>
            <p>Get enterprise-grade performance with SSD storage, 99.9% uptime guarantee, and 24/7 support. Perfect for developers, businesses, and websites of all sizes.</p>
        </div>
    </section>

    <section class="plans">
        <div class="plan">
            <h3>Starter</h3>
            <div class="price">$5.99/mo</div>
            <ul>
                <li>1 CPU Core</li>
                <li>1 GB RAM</li>
                <li>20 GB SSD</li>
                <li>1 TB Bandwidth</li>
                <li>Free SSL</li>
            </ul>
            <a href="#" class="btn">Get Started</a>
        </div>

        <div class="plan">
            <h3>Professional</h3>
            <div class="price">$12.99/mo</div>
            <ul>
                <li>2 CPU Cores</li>
                <li>2 GB RAM</li>
                <li>40 GB SSD</li>
                <li>2 TB Bandwidth</li>
                <li>Free SSL</li>
                <li>Daily Backups</li>
            </ul>
            <a href="#" class="btn">Get Started</a>
        </div>

        <div class="plan">
            <h3>Enterprise</h3>
            <div class="price">$29.99/mo</div>
            <ul>
                <li>4 CPU Cores</li>
                <li>4 GB RAM</li>
                <li>80 GB SSD</li>
                <li>5 TB Bandwidth</li>
                <li>Free SSL</li>
                <li>Daily Backups</li>
                <li>Priority Support</li>
            </ul>
            <a href="#" class="btn">Get Started</a>
        </div>
    </section>

    <footer>
        <div class="container">
            <p>&copy; 2023 VPS Host. All rights reserved.</p>
        </div>
    </footer>

    <script>
        // Simple language switcher - just changes text content based on language
        document.addEventListener('DOMContentLoaded', function() {
            const langSwitcher = document.querySelectorAll('.lang-switcher a');
            const heroH1 = document.querySelector('.hero h1');
            const heroP = document.querySelector('.hero p');
            const plans = document.querySelectorAll('.plan');

            // Language translations
            const translations = {
                en: {
                    title: "Fast & Reliable VPS Hosting",
                    description: "Get enterprise-grade performance with SSD storage, 99.9% uptime guarantee, and 24/7 support. Perfect for developers, businesses, and websites of all sizes.",
                    starter: "Starter",
                    professional: "Professional",
                    enterprise: "Enterprise",
                    price: "$5.99/mo",
                    price2: "$12.99/mo",
                    price3: "$29.99/mo",
                    btn: "Get Started"
                },
                zh: {
                    title: "快速可靠的VPS托管",
                    description: "享受SSD存储、99.9%正常运行时间保证和24/7技术支持。非常适合开发者、企业及各类网站。",
                    starter: "入门版",
                    professional: "专业版",
                    enterprise: "企业版",
                    price: "¥39/月",
                    price2: "¥79/月",
                    price3: "¥189/月",
                    btn: "立即购买"
                },
                de: {
                    title: "Schnelle und zuverlässige VPS-Hosting",
                    description: "Erhalten Sie Enterprise-Performance mit SSD-Speicher, 99,9% Verfügbarkeitsgarantie und 24/7-Support. Perfekt für Entwickler, Unternehmen und Websites jeder Größe.",
                    starter: "Start",
                    professional: "Professionell",
                    enterprise: "Enterprise",
                    price: "5,99 $/Monat",
                    price2: "12,99 $/Monat",
                    price3: "29,99 $/Monat",
                    btn: "Jetzt starten"
                },
                ja: {
                    title: "高速で信頼性の高いVPSホスティング",
                    description: "SSDストレージ、99.9%の稼働保証、24時間365日サポートを提供。開発者、ビジネス、あらゆる規模のウェブサイトに最適です。",
                    starter: "スタンダード",
                    professional: "プロフェッショナル",
                    enterprise: "エンタープライズ",
                    price: "5.99ドル/月",
                    price2: "12.99ドル/月",
                    price3: "29.99ドル/月",
                    btn: "開始する"
                },
                ko: {
                    title: "빠르고 신뢰할 수 있는 VPS 호스팅",
                    description: "SSD 스토리지, 99.9% 가용성 보장 및 24/7 지원을 제공합니다. 개발자, 비즈니스, 모든 규모의 웹사이트에 완벽합니다.",
                    starter: "스타터",
                    professional: "프로페셔널",
                    enterprise: "엔터프라이즈",
                    price: "5,99달러/월",
                    price2: "12,99달러/월",
                    price3: "29,99달러/월",
                    btn: "시작하기"
                },
                fr: {
                    title: "Hébergement VPS rapide et fiable",
                    description: "Obtenez des performances d'entreprise avec stockage SSD, garantie de disponibilité de 99,9 % et support 24/7. Parfait pour les développeurs, entreprises et sites web de toutes tailles.",
                    starter: "Débutant",
                    professional: "Professionnel",
                    enterprise: "Entreprise",
                    price: "5,99 $/mois",
                    price2: "12,99 $/mois",
                    price3: "29,99 $/mois",
                    btn: "Commencer"
                },
                nl: {
                    title: "Snelle en betrouwbare VPS Hosting",
                    description: "Krijg enterprise-kwaliteit met SSD-opslag, 99,9% uptime-garantie en 24/7 ondersteuning. Ideaal voor ontwikkelaars, bedrijven en websites van alle maten.",
                    starter: "Beginner",
                    professional: "Professioneel",
                    enterprise: "Enterprise",
                    price: "5,99 $/maand",
                    price2: "12,99 $/maand",
                    price3: "29,99 $/maand",
                    btn: "Start nu"
                }
            };

            // Handle language switching
            langSwitcher.forEach(link => {
                link.addEventListener('click', function(e) {
                    e.preventDefault();

                    // Remove active class from all
                    langSwitcher.forEach(l => l.classList.remove('active'));

                    // Add active to clicked
                    this.classList.add('active');

                    // Get language code
                    const lang = this.getAttribute('lang');

                    // Update content based on language
                    const t = translations[lang] || translations.en;

                    heroH1.textContent = t.title;
                    heroP.textContent = t.description;

                    // Update plan content
                    plans.forEach((plan, index) => {
                        const h3 = plan.querySelector('h3');
                        const price = plan.querySelector('.price');
                        const btn = plan.querySelector('.btn');

                        switch(index) {
                            case 0:
                                h3.textContent = t.starter;
                                price.textContent = t.price;
                                btn.textContent = t.btn;
                                break;
                            case 1:
                                h3.textContent = t.professional;
                                price.textContent = t.price2;
                                btn.textContent = t.btn;
                                break;
                            case 2:
                                h3.textContent = t.enterprise;
                                price.textContent = t.price3;
                                btn.textContent = t.btn;
                                break;
                        }
                    });
                });
            });
        });
    </script>
</body>
</html>

This simple HTML design includes:

1. A clean, modern layout with a responsive design
2. Language switcher buttons for all 7 requested languages (EN, ZH, DE, JA, KO, FR, NL)
3. Three VPS plan tiers with clear pricing and features
4. Simple JavaScript that dynamically updates content based on language selection
5. Minimal CSS with no external dependencies
6. Mobile-friendly design
7. The language switching works by changing the text content to match the selected language

The design is intentionally kept simple as requested, with no complex frameworks, animations, or excessive styling.