flan-t5-large-grammar-synthesis-gguf

107
2
license:apache-2.0
by
pszemraj
Language Model
OTHER
New
107 downloads
Early-stage
Edge AI:
Mobile
Laptop
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Unknown
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Training Data Analysis

🔵 Good (6.0/10)

Researched training datasets used by flan-t5-large-grammar-synthesis-gguf with quality assessment

Specialized For

general
multilingual

Training Datasets (1)

c4
🔵 6/10
general
multilingual
Key Strengths
  • Scale and Accessibility: 750GB of publicly available, filtered text
  • Systematic Filtering: Documented heuristics enable reproducibility
  • Language Diversity: Despite English-only, captures diverse writing styles
Considerations
  • English-Only: Limits multilingual applications
  • Filtering Limitations: Offensive content and low-quality text remain despite filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

text
system_info: n_threads = 4 / 8 | AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 1 | AVX512_VBMI = 1 | AVX512_VNNI = 1 | 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 |
sampling:
        repeat_last_n = 64, repeat_penalty = 1.000, 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 = 0.000
        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 = 8192, n_batch = 2048, n_predict = -1, n_keep = 0


 The car broke down so they had to take a ride to school. [end of text]


llama_print_timings:        load time =     782.21 ms
llama_print_timings:      sample time =       0.23 ms /    16 runs   (    0.01 ms per token, 68376.07 tokens per second)
llama_print_timings: prompt eval time =      85.08 ms /    19 tokens (    4.48 ms per token,   223.33 tokens per second)
llama_print_timings:        eval time =     341.74 ms /    15 runs   (   22.78 ms per token,    43.89 tokens per second)
llama_print_timings:       total time =     456.56 ms /    34 tokens
Log end

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