103 lines
3.0 KiB
Markdown
103 lines
3.0 KiB
Markdown
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---
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inference: false
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language: en
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license: llama2
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model_type: llama
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datasets:
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- mlabonne/CodeLlama-2-20k
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pipeline_tag: text-generation
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tags:
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- llama-2
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---
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# CRIA v1.3
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💡 [Article](https://walterteng.com/cria) |
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💻 [Github](https://github.com/davzoku/cria) |
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📔 Colab [1](https://colab.research.google.com/drive/1rYTs3qWJerrYwihf1j0f00cnzzcpAfYe),[2](https://colab.research.google.com/drive/1Wjs2I1VHjs6zT_GE42iEXsLtYh6VqiJU)
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## What is CRIA?
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> krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.
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<p align="center">
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<img src="https://raw.githubusercontent.com/davzoku/cria/main/assets/icon-512x512.png" width="300" height="300" alt="Cria Logo"> <br>
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<i>or what ChatGPT suggests, <b>"Crafting a Rapid prototype of an Intelligent llm App using open source resources"</b>.</i>
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</p>
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The initial objective of the CRIA project is to develop a comprehensive end-to-end chatbot system, starting from the instruction-tuning of a large language model and extending to its deployment on the web using frameworks such as Next.js.
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Specifically, we have fine-tuned the `llama-2-7b-chat-hf` model with QLoRA (4-bit precision) using the [mlabonne/CodeLlama-2-20k](https://huggingface.co/datasets/mlabonne/CodeLlama-2-20k) dataset. This fine-tuned model serves as the backbone for the [CRIA chat](https://chat.walterteng.com) platform.
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## 📦 Model Release
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CRIA v1.3 comes with several variants.
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- [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3): Merged Model
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- [davzoku/cria-llama2-7b-v1.3-GGML](https://huggingface.co/davzoku/cria-llama2-7b-v1.3-GGML): Quantized Merged Model
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- [davzoku/cria-llama2-7b-v1.3_peft](https://huggingface.co/davzoku/cria-llama2-7b-v1.3_peft): PEFT adapter
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## 🔧 Training
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It was trained on a Google Colab notebook with a T4 GPU and high RAM.
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### Training procedure
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The following `bitsandbytes` quantization config was used during training:
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- load_in_8bit: False
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- load_in_4bit: True
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- llm_int8_threshold: 6.0
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- llm_int8_skip_modules: None
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- llm_int8_enable_fp32_cpu_offload: False
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- llm_int8_has_fp16_weight: False
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- bnb_4bit_quant_type: nf4
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- bnb_4bit_use_double_quant: False
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- bnb_4bit_compute_dtype: float16
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### Framework versions
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- PEFT 0.4.0
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## 💻 Usage
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```python
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# pip install transformers accelerate
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from transformers import AutoTokenizer
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import transformers
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import torch
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model = "davzoku/cria-llama2-7b-v1.3"
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prompt = "What is a cria?"
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tokenizer = AutoTokenizer.from_pretrained(model)
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pipeline = transformers.pipeline(
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"text-generation",
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model=model,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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sequences = pipeline(
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f'<s>[INST] {prompt} [/INST]',
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do_sample=True,
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top_k=10,
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num_return_sequences=1,
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eos_token_id=tokenizer.eos_token_id,
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max_length=200,
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)
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for seq in sequences:
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print(f"Result: {seq['generated_text']}")
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```
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## References
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We'd like to thank:
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- [mlabonne](https://huggingface.co/mlabonne) for his article and resources on implementation of instruction tuning
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- [TheBloke](https://huggingface.co/TheBloke) for his script for LLM quantization.
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