llama : add Phi-4-mini support (supersede #12099)

[ngxson]

Supersede #12099 with these changes:

1. No need to check for longrope when loading tensors ==> same with LLM_ARCH_LLAMA
2. Fix convert_hf_to_gguf_update.py
3. Add missing tokenizer .inp/.out files

Test with llama-cli:

You are a helpful assistant

> hi
Hello! How can I assist you today?

> write some unicode
Sure! Unicode characters can represent a wide array of symbols from various languages, mathematical operators, and much more. Here are some examples:

- Smile face: 😊
- Heart: ❤️
- Star: ⭐
- Music notes: 🎵
- Infinity: ∞
- Copyright: ©
- Mathematical pi: π
- Mathematical integral: ∫
- Mathematical infinity: ∞
- Euro sign: €
- Square root: √
- Copyright symbol: ℧
- Greek letter alpha: α

Would you like to see more examples, or is there a specific character or symbol you're interested in?

> write chinese characters
Certainly! Chinese characters, also known as Hanzi in Mandarin, are logograms used in the Chinese writing system. Here are a few examples of Chinese characters, along with their pinyin (Romanization) and English meanings:

1. 爱 (Ài) - Love
2. 和 (Hé) - Harmony, and
3. 人 (Rén) - Person
4. 学 (Xué) - Study, learn, or education
5. 水 (Shū) - Water
6. 火 (Huǒ) - Fire
7. 木 (Mù) - Wood
8. 金 (Jīn) - Gold
9. 土 (Tǔ) - Soil
10. 气 (Qì) - Air, atmosphere

Each Chinese character can have a specific meaning and sometimes multiple meanings depending on the context. Learning Chinese characters can be quite rewarding, as it allows for a deeper understanding of Chinese culture and language nuances. Would you like more examples or help with something else?

> 
llama_perf_sampler_print:    sampling time =      34.91 ms /   219 runs   (    0.16 ms per token,  6272.38 tokens per second)
llama_perf_context_print:        load time =    2003.88 ms
llama_perf_context_print: prompt eval time =    6230.83 ms /    42 tokens (  148.35 ms per token,     6.74 tokens per second)
llama_perf_context_print:        eval time =    6517.55 ms /   343 runs   (   19.00 ms per token,    52.63 tokens per second)
llama_perf_context_print:       total time =   21252.44 ms /   385 tokens
Interrupted by user

[bartowski1182]

Using latest llama.cpp release and converting Phi 4 mini instruct gave me this error:

kv_bytes += self._pack_val(val.value, val.type, add_vtype=True)
  File "/llama.cpp/gguf-py/gguf/gguf_writer.py", line 945, in _pack_val
    raise ValueError("All items in a GGUF array should be of the same type")
ValueError: All items in a GGUF array should be of the same type