Quantization made by Richard Erkhov. [Github](https://github.com/RichardErkhov) [Discord](https://discord.gg/pvy7H8DZMG) [Request more models](https://github.com/RichardErkhov/quant_request) cria-llama2-7b-v1.3 - GGUF - Model creator: https://huggingface.co/davzoku/ - Original model: https://huggingface.co/davzoku/cria-llama2-7b-v1.3/ | Name | Quant method | Size | | ---- | ---- | ---- | | [cria-llama2-7b-v1.3.Q2_K.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q2_K.gguf) | Q2_K | 2.36GB | | [cria-llama2-7b-v1.3.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.IQ3_XS.gguf) | IQ3_XS | 2.6GB | | [cria-llama2-7b-v1.3.IQ3_S.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.IQ3_S.gguf) | IQ3_S | 2.75GB | | [cria-llama2-7b-v1.3.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q3_K_S.gguf) | Q3_K_S | 2.75GB | | [cria-llama2-7b-v1.3.IQ3_M.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.IQ3_M.gguf) | IQ3_M | 2.9GB | | [cria-llama2-7b-v1.3.Q3_K.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q3_K.gguf) | Q3_K | 3.07GB | | [cria-llama2-7b-v1.3.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q3_K_M.gguf) | Q3_K_M | 3.07GB | | [cria-llama2-7b-v1.3.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q3_K_L.gguf) | Q3_K_L | 3.35GB | | [cria-llama2-7b-v1.3.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.IQ4_XS.gguf) | IQ4_XS | 3.4GB | | [cria-llama2-7b-v1.3.Q4_0.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q4_0.gguf) | Q4_0 | 3.56GB | | [cria-llama2-7b-v1.3.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.IQ4_NL.gguf) | IQ4_NL | 3.58GB | | [cria-llama2-7b-v1.3.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q4_K_S.gguf) | Q4_K_S | 3.59GB | | [cria-llama2-7b-v1.3.Q4_K.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q4_K.gguf) | Q4_K | 3.8GB | | [cria-llama2-7b-v1.3.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q4_K_M.gguf) | Q4_K_M | 3.8GB | | [cria-llama2-7b-v1.3.Q4_1.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q4_1.gguf) | Q4_1 | 3.95GB | | [cria-llama2-7b-v1.3.Q5_0.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q5_0.gguf) | Q5_0 | 4.33GB | | [cria-llama2-7b-v1.3.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q5_K_S.gguf) | Q5_K_S | 4.33GB | | [cria-llama2-7b-v1.3.Q5_K.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q5_K.gguf) | Q5_K | 4.45GB | | [cria-llama2-7b-v1.3.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q5_K_M.gguf) | Q5_K_M | 4.45GB | | [cria-llama2-7b-v1.3.Q5_1.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q5_1.gguf) | Q5_1 | 4.72GB | | [cria-llama2-7b-v1.3.Q6_K.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q6_K.gguf) | Q6_K | 5.15GB | | [cria-llama2-7b-v1.3.Q8_0.gguf](https://huggingface.co/RichardErkhov/davzoku_-_cria-llama2-7b-v1.3-gguf/blob/main/cria-llama2-7b-v1.3.Q8_0.gguf) | Q8_0 | 6.67GB | Original model description: --- inference: false language: en license: llama2 model_type: llama datasets: - mlabonne/CodeLlama-2-20k pipeline_tag: text-generation tags: - llama-2 --- # CRIA v1.3 💡 [Article](https://walterteng.com/cria) | 💻 [Github](https://github.com/davzoku/cria) | 📔 Colab [1](https://colab.research.google.com/drive/1rYTs3qWJerrYwihf1j0f00cnzzcpAfYe),[2](https://colab.research.google.com/drive/1Wjs2I1VHjs6zT_GE42iEXsLtYh6VqiJU) ## What is CRIA? > krē-ə plural crias. : a baby llama, alpaca, vicuña, or guanaco.

Cria Logo
or what ChatGPT suggests, "Crafting a Rapid prototype of an Intelligent llm App using open source resources".

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. 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. ## 📦 Model Release CRIA v1.3 comes with several variants. - [davzoku/cria-llama2-7b-v1.3](https://huggingface.co/davzoku/cria-llama2-7b-v1.3): Merged Model - [davzoku/cria-llama2-7b-v1.3-GGML](https://huggingface.co/davzoku/cria-llama2-7b-v1.3-GGML): Quantized Merged Model - [davzoku/cria-llama2-7b-v1.3_peft](https://huggingface.co/davzoku/cria-llama2-7b-v1.3_peft): PEFT adapter ## 🔧 Training It was trained on a Google Colab notebook with a T4 GPU and high RAM. ### Training procedure The following `bitsandbytes` quantization config was used during training: - load_in_8bit: False - load_in_4bit: True - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: nf4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float16 ### Framework versions - PEFT 0.4.0 ## 💻 Usage ```python # pip install transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "davzoku/cria-llama2-7b-v1.3" prompt = "What is a cria?" tokenizer = AutoTokenizer.from_pretrained(model) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) sequences = pipeline( f'[INST] {prompt} [/INST]', do_sample=True, top_k=10, num_return_sequences=1, eos_token_id=tokenizer.eos_token_id, max_length=200, ) for seq in sequences: print(f"Result: {seq['generated_text']}") ``` ## References We'd like to thank: - [mlabonne](https://huggingface.co/mlabonne) for his article and resources on implementation of instruction tuning - [TheBloke](https://huggingface.co/TheBloke) for his script for LLM quantization.