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Upload folder using huggingface_hub

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.gitattributes CHANGED
@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
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  *.zip filter=lfs diff=lfs merge=lfs -text
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  *.zst filter=lfs diff=lfs merge=lfs -text
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  *tfevents* filter=lfs diff=lfs merge=lfs -text
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+ Kodify_Nano.gguf filter=lfs diff=lfs merge=lfs -text
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+ Kodify_Nano_q4_k_s.gguf filter=lfs diff=lfs merge=lfs -text
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+ Kodify_Nano_q8_0.gguf filter=lfs diff=lfs merge=lfs -text
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README.md ADDED
@@ -0,0 +1,106 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ---
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+ library_name: ggml
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+ language:
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+ - ru
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+ - en
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+ pipeline_tag: text-generation
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+ license: apache-2.0
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+ license_name: apache-2.0
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+ license_link: https://huggingface.co/MTSAIR/Kodify-Nano-GGUF/blob/main/Apache%20License%20MTS%20AI.docx
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+ ---
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+
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+ # Kodify-Nano-GGUF 🤖
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+
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+ Kodify-Nano-GGUF - 4-битная квантизированная GGUF версия модели [MTSAIR/Kodify-Nano](https://huggingface.co/MTSAIR/Kodify-Nano), оптимизированная для CPU-инференса. Легковесная LLM для задач разработки кода с минимальными ресурсами.
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+
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+ Kodify-Nano-GGUF - 4-bit quantized GGUF version of [MTSAIR/Kodify-Nano](https://huggingface.co/MTSAIR/Kodify-Nano), optimized for CPU inference. Lightweight LLM for code development tasks with minimal resource requirements.
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+
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+ ## Download Models
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+
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+ Available quantization variants:
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+ - Kodify_Nano_q4_k_s.gguf (balanced)
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+ - Kodify_Nano_q8_0.gguf (high quality)
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+ - Kodify_Nano.gguf (best quality, unquantized)
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+
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+ Download using huggingface_hub:
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+
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+ ```bash
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+ pip install huggingface-hub
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+ python -c "from huggingface_hub import hf_hub_download; hf_hub_download(repo_id='MTSAIR/Kodify-Nano-GGUF', filename='Kodify_Nano_q4_k_s.gguf', local_dir='./models')"
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+ ```
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+
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+ ## Using with Ollama
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+
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+ 1. Install Ollama:
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+ https://ollama.com/download
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+
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+ 2. Create Modelfile:
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+
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+ ```
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+ FROM ./models/Kodify_Nano_q4_k_s.gguf
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+ PARAMETER temperature 0.4
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+ PARAMETER top_p 0.8
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+ PARAMETER num_ctx 8192
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+ TEMPLATE """<s>[INST] {{ .System }} {{ .Prompt }} [/INST]"""
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+ ```
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+
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+ 3. Create and run model:
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+ ollama create kodify-nano -f Modelfile
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+ ollama run kodify-nano "Write a Python function to check prime numbers"
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+
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+ ## Python Integration
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+
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+ Install Ollama Python library:
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+
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+ ```bash
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+ pip install ollama
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+ ```
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+
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+ Example code:
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+
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+ ```python
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+ import ollama
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+
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+ response = ollama.generate(
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+ model="kodify-nano",
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+ prompt="Write a Python function to calculate factorial",
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+ options={
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+ "temperature": 0.4,
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+ "top_p": 0.8,
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+ "num_ctx": 8192
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+ }
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+ )
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+
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+ print(response['response'])
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+ ```
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+
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+ ## Usage Examples
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+
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+ ### Code Generation
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+
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+ ```python
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+ response = ollama.generate(
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+ model="kodify-nano",
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+ prompt="""<s>[INST]
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+ Write a Python function that:
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+ 1. Accepts a list of numbers
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+ 2. Returns the median value
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+ [/INST]""",
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+ options={"max_tokens": 512}
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+ )
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+
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+ ### Code Refactoring
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+ response = ollama.generate(
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+ model="kodify-nano",
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+ prompt="""<s>[INST]
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+ Refactor this Python code:
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+
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+ def calc(a,b):
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+ s = a + b
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+ d = a - b
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+ p = a * b
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+ return s, d, p
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+ [/INST]""",
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+ options={"temperature": 0.3}
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+ )
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+ ```