Teruhiro Komaki

日々の暮らし、技術的な学び、そして仕事の記録

JetBrains AI 関連機能のライセンス変更に伴い、AI クレジットの消費が早くなった

公開日： 更新日：

JetBrains AI アシスタント

以前も、引用しましたが、JetBrains AIサブスクリプション関連に修正が入りました。

要約: AI サブスクリプションの管理をシンプルで分かりやすくするため、2025 年 8 月 25 日より透明性のある法定通貨表記の新しい AI 利用枠体系が導入されました。

また、付与された利用枠を超えてシームレスに使用量を拡張できるようになります。

まずは個人と組織を対象に適用を開始し、その後 AI Enterprise の更新が行われます。

AI 利用枠に関する価格体系の単純化と透明化 | JetBrains のブログ

先日、ウィジェットでAI クレジットの消費を確認しました。気づいたら、AI クレジットの消費に関する修正が入っていたようで、今までと比較すると、明らかに消費量が増えておりました。

今までは、一ヶ月終わる頃に確認しても、3/2程度の消費量でした。現在では、残り18日で以下のスクショの状況です。

JetBrains AI アシスタントのウィジェット
JetBrains AI アシスタントのウィジェット

もしかしたら、今まで使える枠が大きかっただけなのかもしれません。

JetBrainsのIDEが大好きなので、どうにか商売が続いてほしいので、この点はしょうがないのでしょうね…

とはいえ、私は許容できないので、ローカルLLMを使うことを検討しています。また、コミットメッセージの下書きについてはtigとGeminiを使って下書きを作成するように運用を見直しました。

ローカルLLM

以下のツールを使ってみまして、概念はある程度は理解できました。しかし、手元で持っているマシンで快適に動かすことは難しく、現実的ではないと思いました。

手元である程度、まともにレスポンスを返せそうなモデルとして、gemma-3-4b-it-GGUFを選択してみました。

Gemma 3 - a ggml-org Collection

llama-serverを起動してみる

llama-serverを実行してみます。本当はマシンに合わせてパラメーターを適切に指定するべきですが、ひとまず動作確認です。

llama-server -hf ggml-org/gemma-3-4b-it-GGUF
llama-serverコマンドでサーバーを起動

以下は、実行時のログです。無事起動できましたので、curlでリクエストします。

MacBook-Pro-14:~ teruhiro$ llama-server -hf ggml-org/gemma-3-4b-it-GGUF
common_download_file_single: no previous model file found /Users/teruhiro/Library/Caches/llama.cpp/ggml-org_gemma-3-4b-it-GGUF_gemma-3-4b-it-Q4_K_M.gguf
curl_perform_with_retry: HEAD https://huggingface.co/ggml-org/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-Q4_K_M.gguf (attempt 1 of 1)...
common_download_file_single: trying to download model from https://huggingface.co/ggml-org/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-Q4_K_M.gguf to /Users/teruhiro/Library/Caches/llama.cpp/ggml-org_gemma-3-4b-it-GGUF_gemma-3-4b-it-Q4_K_M.gguf (server_etag:"708878072fd6ad447564132426948a5d97e8cdb7105b081f68330d7e51ab3a93", server_last_modified:)...
curl_perform_with_retry: GET https://huggingface.co/ggml-org/gemma-3-4b-it-GGUF/resolve/main/gemma-3-4b-it-Q4_K_M.gguf (attempt 1 of 3)...
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  1335  100  1335    0     0   7644      0 --:--:-- --:--:-- --:--:--  7644
100 2374M  100 2374M    0     0  64.9M      0  0:00:36  0:00:36 --:--:-- 69.1M
common_download_file_single: no previous model file found /Users/teruhiro/Library/Caches/llama.cpp/ggml-org_gemma-3-4b-it-GGUF_mmproj-model-f16.gguf
curl_perform_with_retry: HEAD https://huggingface.co/ggml-org/gemma-3-4b-it-GGUF/resolve/main/mmproj-model-f16.gguf (attempt 1 of 1)...
common_download_file_single: trying to download model from https://huggingface.co/ggml-org/gemma-3-4b-it-GGUF/resolve/main/mmproj-model-f16.gguf to /Users/teruhiro/Library/Caches/llama.cpp/ggml-org_gemma-3-4b-it-GGUF_mmproj-model-f16.gguf (server_etag:"4f9a08744613d05f86991776e31a3385d8f0c4c8b33fb105f22edb5e24863c1e", server_last_modified:)...
curl_perform_with_retry: GET https://huggingface.co/ggml-org/gemma-3-4b-it-GGUF/resolve/main/mmproj-model-f16.gguf (attempt 1 of 3)...
  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  1333  100  1333    0     0   7814      0 --:--:-- --:--:-- --:--:--  7814
100  811M  100  811M    0     0  32.8M      0  0:00:24  0:00:24 --:--:-- 34.6M
build: 6390 (a8128382) with Apple clang version 16.0.0 (clang-1600.0.26.6) for arm64-apple-darwin23.6.0
system info: n_threads = 6, n_threads_batch = 6, total_threads = 8

system_info: n_threads = 6 (n_threads_batch = 6) / 8 | Metal : EMBED_LIBRARY = 1 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | DOTPROD = 1 | LLAMAFILE = 1 | ACCELERATE = 1 | REPACK = 1 |

main: binding port with default address family
main: HTTP server is listening, hostname: 127.0.0.1, port: 8080, http threads: 7
main: loading model
srv    load_model: loading model '/Users/teruhiro/Library/Caches/llama.cpp/ggml-org_gemma-3-4b-it-GGUF_gemma-3-4b-it-Q4_K_M.gguf'
llama_model_load_from_file_impl: using device Metal (Apple M1 Pro) - 21845 MiB free
llama_model_loader: loaded meta data with 40 key-value pairs and 444 tensors from /Users/teruhiro/Library/Caches/llama.cpp/ggml-org_gemma-3-4b-it-GGUF_gemma-3-4b-it-Q4_K_M.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              = gemma3
llama_model_loader: - kv   1:                               general.type str              = model
llama_model_loader: - kv   2:                               general.name str              = Gemma 3 4b It
llama_model_loader: - kv   3:                           general.finetune str              = it
llama_model_loader: - kv   4:                           general.basename str              = gemma-3
llama_model_loader: - kv   5:                         general.size_label str              = 4B
llama_model_loader: - kv   6:                            general.license str              = gemma
llama_model_loader: - kv   7:                   general.base_model.count u32              = 1
llama_model_loader: - kv   8:                  general.base_model.0.name str              = Gemma 3 4b Pt
llama_model_loader: - kv   9:          general.base_model.0.organization str              = Google
llama_model_loader: - kv  10:              general.base_model.0.repo_url str              = https://huggingface.co/google/gemma-3...
llama_model_loader: - kv  11:                               general.tags arr[str,1]       = ["image-text-to-text"]
llama_model_loader: - kv  12:                      gemma3.context_length u32              = 131072
llama_model_loader: - kv  13:                    gemma3.embedding_length u32              = 2560
llama_model_loader: - kv  14:                         gemma3.block_count u32              = 34
llama_model_loader: - kv  15:                 gemma3.feed_forward_length u32              = 10240
llama_model_loader: - kv  16:                gemma3.attention.head_count u32              = 8
llama_model_loader: - kv  17:    gemma3.attention.layer_norm_rms_epsilon f32              = 0.000001
llama_model_loader: - kv  18:                gemma3.attention.key_length u32              = 256
llama_model_loader: - kv  19:              gemma3.attention.value_length u32              = 256
llama_model_loader: - kv  20:                      gemma3.rope.freq_base f32              = 1000000.000000
llama_model_loader: - kv  21:            gemma3.attention.sliding_window u32              = 1024
llama_model_loader: - kv  22:             gemma3.attention.head_count_kv u32              = 4
llama_model_loader: - kv  23:                   gemma3.rope.scaling.type str              = linear
llama_model_loader: - kv  24:                 gemma3.rope.scaling.factor f32              = 8.000000
llama_model_loader: - kv  25:                       tokenizer.ggml.model str              = llama
llama_model_loader: - kv  26:                         tokenizer.ggml.pre str              = default
llama_model_loader: - kv  27:                      tokenizer.ggml.tokens arr[str,262144]  = ["<pad>", "<eos>", "<bos>", "<unk>", ...
llama_model_loader: - kv  28:                      tokenizer.ggml.scores arr[f32,262144]  = [-1000.000000, -1000.000000, -1000.00...
llama_model_loader: - kv  29:                  tokenizer.ggml.token_type arr[i32,262144]  = [3, 3, 3, 3, 3, 4, 3, 3, 3, 3, 3, 3, ...
llama_model_loader: - kv  30:                tokenizer.ggml.bos_token_id u32              = 2
llama_model_loader: - kv  31:                tokenizer.ggml.eos_token_id u32              = 1
llama_model_loader: - kv  32:            tokenizer.ggml.unknown_token_id u32              = 3
llama_model_loader: - kv  33:            tokenizer.ggml.padding_token_id u32              = 0
llama_model_loader: - kv  34:               tokenizer.ggml.add_bos_token bool             = true
llama_model_loader: - kv  35:               tokenizer.ggml.add_eos_token bool             = false
llama_model_loader: - kv  36:                    tokenizer.chat_template str              = {{ bos_token }}\n{%- if messages[0]['r...
llama_model_loader: - kv  37:            tokenizer.ggml.add_space_prefix bool             = false
llama_model_loader: - kv  38:               general.quantization_version u32              = 2
llama_model_loader: - kv  39:                          general.file_type u32              = 15
llama_model_loader: - type  f32:  205 tensors
llama_model_loader: - type q4_K:  204 tensors
llama_model_loader: - type q6_K:   35 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type   = Q4_K - Medium
print_info: file size   = 2.31 GiB (5.12 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load:   - 1 ('<eos>')
load:   - 106 ('<end_of_turn>')
load: special tokens cache size = 6414
load: token to piece cache size = 1.9446 MB
print_info: arch             = gemma3
print_info: vocab_only       = 0
print_info: n_ctx_train      = 131072
print_info: n_embd           = 2560
print_info: n_layer          = 34
print_info: n_head           = 8
print_info: n_head_kv        = 4
print_info: n_rot            = 256
print_info: n_swa            = 1024
print_info: is_swa_any       = 1
print_info: n_embd_head_k    = 256
print_info: n_embd_head_v    = 256
print_info: n_gqa            = 2
print_info: n_embd_k_gqa     = 1024
print_info: n_embd_v_gqa     = 1024
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     = 6.2e-02
print_info: n_ff             = 10240
print_info: n_expert         = 0
print_info: n_expert_used    = 0
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  = 1000000.0
print_info: freq_scale_train = 0.125
print_info: n_ctx_orig_yarn  = 131072
print_info: rope_finetuned   = unknown
print_info: model type       = 4B
print_info: model params     = 3.88 B
print_info: general.name     = Gemma 3 4b It
print_info: vocab type       = SPM
print_info: n_vocab          = 262144
print_info: n_merges         = 0
print_info: BOS token        = 2 '<bos>'
print_info: EOS token        = 1 '<eos>'
print_info: EOT token        = 106 '<end_of_turn>'
print_info: UNK token        = 3 '<unk>'
print_info: PAD token        = 0 '<pad>'
print_info: LF token         = 248 '<0x0A>'
print_info: EOG token        = 1 '<eos>'
print_info: EOG token        = 106 '<end_of_turn>'
print_info: max token length = 48
load_tensors: loading model tensors, this can take a while... (mmap = true)
load_tensors: offloading 34 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 35/35 layers to GPU
load_tensors:   CPU_Mapped model buffer size =   525.00 MiB
load_tensors: Metal_Mapped model buffer size =  2368.18 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     = 1000000.0
llama_context: freq_scale    = 0.125
llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1 Pro
ggml_metal_init: picking default device: Apple M1 Pro
ggml_metal_load_library: using embedded metal library
ggml_metal_init: GPU name:   Apple M1 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple7  (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_init: simdgroup reduction   = true
ggml_metal_init: simdgroup matrix mul. = true
ggml_metal_init: has residency sets    = false
ggml_metal_init: has bfloat            = true
ggml_metal_init: use bfloat            = false
ggml_metal_init: hasUnifiedMemory      = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 22906.50 MB
ggml_metal_init: skipping kernel_get_rows_bf16                     (not supported)
ggml_metal_init: skipping kernel_set_rows_bf16                     (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_c4                (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_1row              (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_l4                (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_bf16                  (not supported)
ggml_metal_init: skipping kernel_mul_mv_id_bf16_f32                (not supported)
ggml_metal_init: skipping kernel_mul_mm_bf16_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mm_id_bf16_f16                (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h40           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h64           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h80           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h96           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h112          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h128          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h192          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_hk192_hv128   (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h256          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_hk576_hv512   (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h64       (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h96       (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h128      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h192      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_hk192_hv128 (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h256      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_hk576_hv512 (not supported)
ggml_metal_init: skipping kernel_cpy_f32_bf16                      (not supported)
ggml_metal_init: skipping kernel_cpy_bf16_f32                      (not supported)
ggml_metal_init: skipping kernel_cpy_bf16_bf16                     (not supported)
llama_context:        CPU  output buffer size =     1.00 MiB
llama_kv_cache_iswa: creating non-SWA KV cache, size = 4096 cells
llama_kv_cache:      Metal KV buffer size =    80.00 MiB
llama_kv_cache: size =   80.00 MiB (  4096 cells,   5 layers,  1/1 seqs), K (f16):   40.00 MiB, V (f16):   40.00 MiB
llama_kv_cache_iswa: creating     SWA KV cache, size = 1536 cells
llama_kv_cache:      Metal KV buffer size =   174.00 MiB
llama_kv_cache: size =  174.00 MiB (  1536 cells,  29 layers,  1/1 seqs), K (f16):   87.00 MiB, V (f16):   87.00 MiB
llama_context: Flash Attention was auto, set to enabled
llama_context:      Metal compute buffer size =   517.00 MiB
llama_context:        CPU compute buffer size =    16.02 MiB
llama_context: graph nodes  = 1369
llama_context: graph splits = 2
common_init_from_params: added <eos> logit bias = -inf
common_init_from_params: added <end_of_turn> 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)
clip_model_loader: model name:
clip_model_loader: description:
clip_model_loader: GGUF version: 3
clip_model_loader: alignment:    32
clip_model_loader: n_tensors:    439
clip_model_loader: n_kv:         16

clip_model_loader: has vision encoder
ggml_metal_init: allocating
ggml_metal_init: found device: Apple M1 Pro
ggml_metal_init: picking default device: Apple M1 Pro
ggml_metal_init: GPU name:   Apple M1 Pro
ggml_metal_init: GPU family: MTLGPUFamilyApple7  (1007)
ggml_metal_init: GPU family: MTLGPUFamilyCommon3 (3003)
ggml_metal_init: GPU family: MTLGPUFamilyMetal3  (5001)
ggml_metal_init: simdgroup reduction   = true
ggml_metal_init: simdgroup matrix mul. = true
ggml_metal_init: has residency sets    = false
ggml_metal_init: has bfloat            = true
ggml_metal_init: use bfloat            = false
ggml_metal_init: hasUnifiedMemory      = true
ggml_metal_init: recommendedMaxWorkingSetSize  = 22906.50 MB
ggml_metal_init: skipping kernel_get_rows_bf16                     (not supported)
ggml_metal_init: skipping kernel_set_rows_bf16                     (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_c4                (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_1row              (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_f32_l4                (not supported)
ggml_metal_init: skipping kernel_mul_mv_bf16_bf16                  (not supported)
ggml_metal_init: skipping kernel_mul_mv_id_bf16_f32                (not supported)
ggml_metal_init: skipping kernel_mul_mm_bf16_f32                   (not supported)
ggml_metal_init: skipping kernel_mul_mm_id_bf16_f16                (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h40           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h64           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h80           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h96           (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h112          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h128          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h192          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_hk192_hv128   (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_h256          (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_bf16_hk576_hv512   (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h64       (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h96       (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h128      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h192      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_hk192_hv128 (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_h256      (not supported)
ggml_metal_init: skipping kernel_flash_attn_ext_vec_bf16_hk576_hv512 (not supported)
ggml_metal_init: skipping kernel_cpy_f32_bf16                      (not supported)
ggml_metal_init: skipping kernel_cpy_bf16_f32                      (not supported)
ggml_metal_init: skipping kernel_cpy_bf16_bf16                     (not supported)
clip_ctx: CLIP using Metal backend
load_hparams: projector:          gemma3
load_hparams: n_embd:             1152
load_hparams: n_head:             16
load_hparams: n_ff:               4304
load_hparams: n_layer:            27
load_hparams: ffn_op:             gelu
load_hparams: projection_dim:     2560

--- vision hparams ---
load_hparams: image_size:         896
load_hparams: patch_size:         14
load_hparams: has_llava_proj:     0
load_hparams: minicpmv_version:   0
load_hparams: proj_scale_factor:  4
load_hparams: n_wa_pattern:       0

load_hparams: model size:         811.79 MiB
load_hparams: metadata size:      0.15 MiB
load_tensors: ffn up/down are swapped
alloc_compute_meta:      Metal compute buffer size =  1132.00 MiB
alloc_compute_meta:        CPU compute buffer size =     9.19 MiB
srv    load_model: loaded multimodal model, '/Users/teruhiro/Library/Caches/llama.cpp/ggml-org_gemma-3-4b-it-GGUF_mmproj-model-f16.gguf'
srv          init: initializing slots, n_slots = 1
slot         init: id  0 | task -1 | new slot n_ctx_slot = 4096
main: model loaded
main: chat template, chat_template: {{ bos_token }}
{%- if messages[0]['role'] == 'system' -%}
    {%- if messages[0]['content'] is string -%}
        {%- set first_user_prefix = messages[0]['content'] + '

' -%}
    {%- else -%}
        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '

' -%}
    {%- endif -%}
    {%- set loop_messages = messages[1:] -%}
{%- else -%}
    {%- set first_user_prefix = "" -%}
    {%- set loop_messages = messages -%}
{%- endif -%}
{%- for message in loop_messages -%}
    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
        {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
    {%- endif -%}
    {%- if (message['role'] == 'assistant') -%}
        {%- set role = "model" -%}
    {%- else -%}
        {%- set role = message['role'] -%}
    {%- endif -%}
    {{ '<start_of_turn>' + role + '
' + (first_user_prefix if loop.first else "") }}
    {%- if message['content'] is string -%}
        {{ message['content'] | trim }}
    {%- elif message['content'] is iterable -%}
        {%- for item in message['content'] -%}
            {%- if item['type'] == 'image' -%}
                {{ '<start_of_image>' }}
            {%- elif item['type'] == 'text' -%}
                {{ item['text'] | trim }}
            {%- endif -%}
        {%- endfor -%}
    {%- else -%}
        {{ raise_exception("Invalid content type") }}
    {%- endif -%}
    {{ '<end_of_turn>
' }}
{%- endfor -%}
{%- if add_generation_prompt -%}
    {{'<start_of_turn>model
'}}
{%- endif -%}
, example_format: '<start_of_turn>user
You are a helpful assistant

Hello<end_of_turn>
<start_of_turn>model
Hi there<end_of_turn>
<start_of_turn>user
How are you?<end_of_turn>
<start_of_turn>model
'
main: server is listening on http://127.0.0.1:8080 - starting the main loop
srv  update_slots: all slots are idle
llama-server -hf ggml-org/gemma-3-4b-it-GGUF

curlでリクエストしてみる

llama.cppのサーバーは、OpenAIのAPIと互換性のあるインターフェイスを提供しているので、以下のcurlでリクエストしてみます。

curl http://127.0.0.1:8080/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma-3-4b-it-Q4_K_M",
    "messages": [
      {"role": "user", "content": "こんにちは！元気ですか？"}
    ]
  }'
curlでリクエスト

正常にリクエストできました。

{
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "こんにちは！私は元気ですよ！😊 あなたはいかがですか？何かお手伝いできることはありますか？\n"
      }
    }
  ],
  "created": 1757351768,
  "model": "gemma-3-4b-it-Q4_K_M",
  "system_fingerprint": "b6390-a8128382",
  "object": "chat.completion",
  "usage": {
    "completion_tokens": 23,
    "prompt_tokens": 14,
    "total_tokens": 37
  },
  "id": "chatcmpl-TUDWUdlHaQD3om8JdiZtYTBAoJsu50mo",
  "timings": {
    "prompt_n": 1,
    "prompt_ms": 140.759,
    "prompt_per_token_ms": 140.759,
    "prompt_per_second": 7.1043414630680815,
    "predicted_n": 23,
    "predicted_ms": 576.258,
    "predicted_per_token_ms": 25.054695652173915,
    "predicted_per_second": 39.91267800186722
  }
}
レスポンス

JetBrainsのローカルLLM設定

JetBrainsでは、ローカルLLMをサポートしています。

JetBrainsの設定 > AI アシスタント > モデル
JetBrainsの設定 > AI アシスタント > モデル

今回のテスト時に、ollamaは正常に機能しましたが、llama.cppのサーバーは、正常に機能しませんでした。curlでは成功したので、IDEからのPOSTしているボディの値に起因するかもしれません。

現状のマシンでは、パフォーマンスに問題があるので、JetBrainsの設定や連携について、深掘りはしていません。

まとめ

JetBrains AI クレジットの消費スピードが早くなったので、別の方法を模索しています。継続的にローカルLLMや良い落とし所を見つけたいと思います。