2 liners gradio chatbot with llamacpp and granite-3.1-2b-instruct-GGUF
Create a fast chatbot with 2 lines of code.
This example is good only for quick check, but it doesn't allow much control
granite3.1-gradio
├───llamacpp
│ └───model
└───venv
Clone the Repo to have already the directory structure and the llamaCPP binaries
- the model (GGUF) goes inside the subfolder
llamacpp/model - extract the content of the ZIP archive
llama-b4435-bin-win-vulkan-x64.zipinside thellamacppdirectory - in tha main project folder create a virtual environment
- install the following dependencies:
pip install --upgrade gradio tiktoken openai
In one terminal window, from the llamacpp directory start the llama-server:
llama-server.exe -m model\granite-3.1-2b-instruct-Q5_K_L.gguf -c 8192 -ngl 0 --temp 0.2 --repeat-penalty 1.45with the venv activated, in another window then run:
python .\testgradioGRANITE.pyUsed stack:
- python 3.12
- llamacpp
- granite-3.1-2b-instruct-Q5_K_L.gguf
- gradio
srv load_model: loading model '.\model\granite-3.1-2b-instruct-Q5_K_L.gguf'
llama_model_loader: loaded meta data with 44 key-value pairs and 362 tensors from .\model\granite-3.1-2b-instruct-Q5_K_L.gguf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
general.architecture str = granite
general.type str = model
general.name str = Granite 3.1 2b Instruct
general.finetune str = instruct
general.basename str = granite-3.1
general.size_label str = 2B
general.license str = apache-2.0
general.base_model.count u32 = 1
general.base_model.0.name str = Granite 3.1 2b Base
al.base_model.0.organization str = Ibm Granite
eneral.base_model.0.repo_url str = https://huggingface.co/ibm-granite/gr...
general.tags arr[str,3] = ["language", "granite-3.1", "text-gen...
granite.block_count u32 = 40
granite.context_length u32 = 131072
granite.embedding_length u32 = 2048
granite.feed_forward_length u32 = 8192
granite.attention.head_count u32 = 32
nite.attention.head_count_kv u32 = 8
granite.rope.freq_base f32 = 5000000.000000
ntion.layer_norm_rms_epsilon f32 = 0.000010
general.file_type u32 = 17
granite.vocab_size u32 = 49155
granite.rope.dimension_count u32 = 64
granite.attention.scale f32 = 0.015625
granite.embedding_scale f32 = 12.000000
granite.residual_scale f32 = 0.220000
granite.logit_scale f32 = 8.000000
tokenizer.ggml.model str = gpt2
tokenizer.ggml.pre str = refact
tokenizer.ggml.tokens arr[str,49155] = ["<|end_of_text|>", "<fim_prefix>", "...
tokenizer.ggml.token_type arr[i32,49155] = [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, ...
tokenizer.ggml.merges arr[str,48891] = ["─á ─á", "─á─á ─á─á", "─á─á─á─á ─á─á...
tokenizer.ggml.bos_token_id u32 = 0
tokenizer.ggml.eos_token_id u32 = 0
enizer.ggml.unknown_token_id u32 = 0
enizer.ggml.padding_token_id u32 = 0
tokenizer.ggml.add_bos_token bool = false
tokenizer.chat_template str = {%- if messages[0]['role'] == 'system...
enizer.ggml.add_space_prefix bool = false
general.quantization_version u32 = 2
quantize.imatrix.file str = /models_out/granite-3.1-2b-instruct-G...
quantize.imatrix.dataset str = /training_dir/calibration_datav3.txt
antize.imatrix.entries_count i32 = 280
uantize.imatrix.chunks_count i32 = 152
llama_model_loader: - type f32: 81 tensors
llama_model_loader: - type q8_0: 1 tensors
llama_model_loader: - type q5_K: 240 tensors
llama_model_loader: - type q6_K: 40 tensors
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 = 0.2826 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = granite
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 49155
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 2048
llm_load_print_meta: n_layer = 40
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_ctx_orig_yarn = 131072
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: model type = 3B
llm_load_print_meta: model ftype = Q5_K - Medium
llm_load_print_meta: model params = 2.53 B
llm_load_print_meta: model size = 1.70 GiB (5.77 BPW)
llm_load_print_meta: general.name = Granite 3.1 2b Instruct
llm_load_print_meta: BOS token = 0 '<|end_of_text|>'
llm_load_print_meta: EOS token = 0 '<|end_of_text|>'
llm_load_print_meta: UNK token = 0 '<|end_of_text|>'
llm_load_print_meta: PAD token = 0 '<|end_of_text|>'
llm_load_print_meta: LF token = 145 'Ä'
llm_load_print_meta: EOG token = 0 '<|end_of_text|>'
llm_load_print_meta: max token length = 512
llm_load_print_meta: f_embedding_scale = 12.000000
llm_load_print_meta: f_residual_scale = 0.220000
llm_load_print_meta: f_attention_scale = 0.015625
ggml_vulkan: Compiling shaders...................................................Done!
llm_load_tensors: offloading 0 repeating layers to GPU
llm_load_tensors: offloaded 0/41 layers to GPU
llm_load_tensors: CPU_Mapped model buffer size = 1742.80 MiB
................................................................................................
llama_new_context_with_model: n_seq_max = 1
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_ctx_per_seq = 8192
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 = 5000000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: n_ctx_per_seq (8192) < 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 = 40, can_shift = 1
llama_kv_cache_init: CPU KV buffer size = 640.00 MiB
llama_new_context_with_model: KV self size = 640.00 MiB, K (f16): 320.00 MiB, V (f16): 320.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.19 MiB
llama_new_context_with_model: Vulkan0 compute buffer size = 562.75 MiB
llama_new_context_with_model: Vulkan_Host compute buffer size = 20.01 MiB
llama_new_context_with_model: graph nodes = 1368
llama_new_context_with_model: graph splits = 444 (with bs=512), 1 (with bs=1)
common_init_from_params: setting dry_penalty_last_n to ctx_size = 8192
common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable)
srv init: initializing slots, n_slots = 1
slot init: id 0 | task -1 | new slot n_ctx_slot = 8192
main: model loaded
main: chat template, chat_template: (built-in), example_format: '<|start_of_role|>system<|end_of_role|>You are a helpful assistant<|end_of_text|>
<|start_of_role|>user<|end_of_role|>Hello<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>Hi there<|end_of_text|>
<|start_of_role|>user<|end_of_role|>How are you?<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>
'
main: server is listening on http://127.0.0.1:8080 - starting the main loop
srv update_slots: all slots are idle