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I am geeting the following for Llama-LLM
2024-06-28 21:57:20 INFO openai - message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=15 request_id=req_5533598d1e5469fd213c359055ce074d response_code=200 llama_model_loader: loaded meta data with 23 key-value pairs and 543 tensors from models/llava-v1.6-34b.Q6_K.gguf (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.name str = LLaMA v2 llama_model_loader: - kv 2: llama.context_length u32 = 4096 llama_model_loader: - kv 3: llama.embedding_length u32 = 7168 llama_model_loader: - kv 4: llama.block_count u32 = 60 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 20480 llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 7: llama.attention.head_count u32 = 56 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 10: llama.rope.freq_base f32 = 5000000.000000 llama_model_loader: - kv 11: general.file_type u32 = 18 llama_model_loader: - kv 12: tokenizer.ggml.model str = llama llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,64000] = ["<unk>", "<|startoftext|>", "<|endof... llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,64000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,64000] = [2, 3, 3, 3, 3, 3, 1, 1, 1, 3, 3, 3, ... llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 7 llama_model_loader: - kv 18: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 19: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 20: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 21: tokenizer.chat_template str = {% for message in messages %}{{'<|im_... llama_model_loader: - kv 22: general.quantization_version u32 = 2 llama_model_loader: - type f32: 121 tensors llama_model_loader: - type q6_K: 422 tensors llm_load_vocab: special tokens cache size = 267 llm_load_vocab: token to piece cache size = 0.3834 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 64000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 4096 llm_load_print_meta: n_embd = 7168 llm_load_print_meta: n_head = 56 llm_load_print_meta: n_head_kv = 8 llm_load_print_meta: n_layer = 60 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_embd_head_k = 128 llm_load_print_meta: n_embd_head_v = 128 llm_load_print_meta: n_gqa = 7 llm_load_print_meta: n_embd_k_gqa = 1024 llm_load_print_meta: n_embd_v_gqa = 1024 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 20480 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 1 llm_load_print_meta: pooling type = 0 llm_load_print_meta: rope type = 0 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 5000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 4096 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: ssm_d_conv = 0 llm_load_print_meta: ssm_d_inner = 0 llm_load_print_meta: ssm_d_state = 0 llm_load_print_meta: ssm_dt_rank = 0 llm_load_print_meta: model type = 30B llm_load_print_meta: model ftype = Q6_K llm_load_print_meta: model params = 34.39 B llm_load_print_meta: model size = 26.27 GiB (6.56 BPW) llm_load_print_meta: general.name = LLaMA v2 llm_load_print_meta: BOS token = 1 '<|startoftext|>' llm_load_print_meta: EOS token = 7 '<|im_end|>' llm_load_print_meta: UNK token = 0 '<unk>' llm_load_print_meta: PAD token = 0 '<unk>' llm_load_print_meta: LF token = 315 '<0x0A>' llm_load_print_meta: EOT token = 2 '<|endoftext|>' llm_load_tensors: ggml ctx size = 0.28 MiB llm_load_tensors: CPU buffer size = 26905.46 MiB .................................................................................................... llama_new_context_with_model: n_batch is less than GGML_KQ_MASK_PAD - increasing to 32 llama_new_context_with_model: n_ctx = 512 llama_new_context_with_model: n_batch = 32 llama_new_context_with_model: n_ubatch = 32 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_kv_cache_init: CPU KV buffer size = 120.00 MiB llama_new_context_with_model: KV self size = 120.00 MiB, K (f16): 60.00 MiB, V (f16): 60.00 MiB llama_new_context_with_model: CPU output buffer size = 0.24 MiB llama_new_context_with_model: CPU compute buffer size = 8.69 MiB llama_new_context_with_model: graph nodes = 1926 llama_new_context_with_model: graph splits = 1 AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | Model metadata: {'tokenizer.chat_template': "{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}", 'tokenizer.ggml.add_eos_token': 'false', 'tokenizer.ggml.padding_token_id': '0', 'tokenizer.ggml.eos_token_id': '7', 'general.architecture': 'llama', 'llama.rope.freq_base': '5000000.000000', 'llama.context_length': '4096', 'general.name': 'LLaMA v2', 'tokenizer.ggml.add_bos_token': 'false', 'llama.embedding_length': '7168', 'llama.feed_forward_length': '20480', 'llama.attention.layer_norm_rms_epsilon': '0.000010', 'llama.rope.dimension_count': '128', 'tokenizer.ggml.bos_token_id': '1', 'llama.attention.head_count': '56', 'llama.block_count': '60', 'llama.attention.head_count_kv': '8', 'general.quantization_version': '2', 'tokenizer.ggml.model': 'llama', 'general.file_type': '18'} Available chat formats from metadata: chat_template.default Guessed chat format: chatml 2024-06-28 21:57:56 ERROR rasa.dialogue_understanding.generator.llm_command_generator - [error ] llm_command_generator.llm.error error=ValueError('Requested tokens (804) exceed context window of 512') /home/hamza/PycharmProjects/G6-Voice-Assistant/.venv/lib/python3.10/site-packages/sanic/server/websockets/impl.py:521: DeprecationWarning: The explicit passing of coroutine objects to asyncio.wait() is deprecated since Python 3.8, and scheduled for removal in Python 3.11.
My config.yml is as follows
config.yml
recipe: default.v1 language: en pipeline: - name: LLMCommandGenerator llm: type: llamacpp model_path: "models/llava-v1.6-34b.Q6_K.gguf" chunk_size: 16 model_kwargs: device: "gpu" # llm: # model_name: gpt-4 policies: - name: FlowPolicy # - name: EnterpriseSearchPolicy # - name: RulePolicy assistant_id: 20240627-152245-khaki-isotope
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I am geeting the following for Llama-LLM
My
config.ymlis as followsThe text was updated successfully, but these errors were encountered: