saucam
/

Phind-Codefuse-34B-gguf

+---
+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 /<path to model>/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]   = ["<unk>", "<s>", "</s>", "<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 '<s>'
+llm_load_print_meta: EOS token        = 2 '</s>'
+llm_load_print_meta: UNK token        = 0 '<unk>'
+llm_load_print_meta: PAD token        = 2 '</s>'
+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
+```