--- license: mit language: - en pipeline_tag: text-generation --- # Phind-Codefuse-34B-gguf Phind-Codefuse-34B-gguf is an 8-bit quantized version of [Phind-Codefuse-34B](saucam/Phind-Codefuse-34B) which is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing): * [Phind/Phind-CodeLlama-34B-v2](https://huggingface.co/Phind/Phind-CodeLlama-34B-v2) * [codefuse-ai/CodeFuse-CodeLlama-34B](https://huggingface.co/codefuse-ai/CodeFuse-CodeLlama-34B) ## Usage Use llama.cpp directly or any of the supported UIs over it. ``` ./main -m //Phind-Codefuse-34B.gguf -p "Write a function to print first n fibonacci numbers in python\n" -n 400 -e Log start main: build = 2382 (621e86b3) main: built with cc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 for x86_64-linux-gnu main: seed = 1710249100 llama_model_loader: loaded meta data with 22 key-value pairs and 435 tensors from /home/ydatta/Downloads/Phind-Codefuse-34B.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 = mergekit llama_model_loader: - kv 2: llama.context_length u32 = 16384 llama_model_loader: - kv 3: llama.embedding_length u32 = 8192 llama_model_loader: - kv 4: llama.block_count u32 = 48 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 22016 llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 7: llama.attention.head_count u32 = 64 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 = 1000000.000000 llama_model_loader: - kv 11: general.file_type u32 = 7 llama_model_loader: - kv 12: tokenizer.ggml.model str = llama llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["", "~~", "~~", "<0x00>", "<... llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 19: tokenizer.ggml.padding_token_id u32 = 2 llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - type f32: 97 tensors llama_model_loader: - type q8_0: 338 tensors llm_load_vocab: special tokens definition check successful ( 259/32000 ). 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 = 32000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 16384 llm_load_print_meta: n_embd = 8192 llm_load_print_meta: n_head = 64 llm_load_print_meta: n_head_kv = 8 llm_load_print_meta: n_layer = 48 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 = 8 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: n_ff = 22016 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 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 = 1000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 16384 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 = 34B llm_load_print_meta: model ftype = Q8_0 llm_load_print_meta: model params = 33.74 B llm_load_print_meta: model size = 33.39 GiB (8.50 BPW) llm_load_print_meta: general.name = mergekit llm_load_print_meta: BOS token = 1 '~~' llm_load_print_meta: EOS token = 2 '~~' llm_load_print_meta: UNK token = 0 '' llm_load_print_meta: PAD token = 2 '' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0.17 MiB llm_load_tensors: CPU buffer size = 34194.28 MiB .................................................................................................... llama_new_context_with_model: n_ctx = 512 llama_new_context_with_model: freq_base = 1000000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CPU KV buffer size = 96.00 MiB llama_new_context_with_model: KV self size = 96.00 MiB, K (f16): 48.00 MiB, V (f16): 48.00 MiB llama_new_context_with_model: CPU input buffer size = 18.01 MiB llama_new_context_with_model: CPU compute buffer size = 128.00 MiB llama_new_context_with_model: graph splits (measure): 1 system_info: n_threads = 16 / 32 | AVX = 1 | AVX_VNNI = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | sampling: repeat_last_n = 64, repeat_penalty = 1.100, frequency_penalty = 0.000, presence_penalty = 0.000 top_k = 40, tfs_z = 1.000, top_p = 0.950, min_p = 0.050, typical_p = 1.000, temp = 0.800 mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 sampling order: CFG -> Penalties -> top_k -> tfs_z -> typical_p -> top_p -> min_p -> temperature generate: n_ctx = 512, n_batch = 512, n_predict = 400, n_keep = 1 Write a function to print first n fibonacci numbers in python Here is a simple Python function that prints the first `n` Fibonacci numbers: ```python def print_fibonacci(n): a, b = 0, 1 for _ in range(n): print(a) a, b = b, a + b print_fibonacci(10) # prints first 10 Fibonacci numbers ``` This function starts with `a` and `b` as the first two Fibonacci numbers (0 and 1), then it enters a loop that runs `n` times. In each iteration, it prints the current value of `a`, then updates `a` and `b` to be the next two Fibonacci numbers (`b` and the sum of `a` and `b`). [end of text] llama_print_timings: load time = 1427.82 ms llama_print_timings: sample time = 29.32 ms / 186 runs ( 0.16 ms per token, 6342.71 tokens per second) llama_print_timings: prompt eval time = 2306.73 ms / 15 tokens ( 153.78 ms per token, 6.50 tokens per second) llama_print_timings: eval time = 134618.75 ms / 185 runs ( 727.67 ms per token, 1.37 tokens per second) llama_print_timings: total time = 137001.23 ms / 200 tokens Log end ```