| Quantization made by Richard Erkhov. | |
| [Github](https://github.com/RichardErkhov) | |
| [Discord](https://discord.gg/pvy7H8DZMG) | |
| [Request more models](https://github.com/RichardErkhov/quant_request) | |
| Llama-2-7b-evolcodealpaca - GGUF | |
| - Model creator: https://huggingface.co/neuralmagic/ | |
| - Original model: https://huggingface.co/neuralmagic/Llama-2-7b-evolcodealpaca/ | |
| | Name | Quant method | Size | | |
| | ---- | ---- | ---- | | |
| | [Llama-2-7b-evolcodealpaca.Q2_K.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q2_K.gguf) | Q2_K | 2.36GB | | |
| | [Llama-2-7b-evolcodealpaca.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.IQ3_XS.gguf) | IQ3_XS | 2.6GB | | |
| | [Llama-2-7b-evolcodealpaca.IQ3_S.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.IQ3_S.gguf) | IQ3_S | 2.75GB | | |
| | [Llama-2-7b-evolcodealpaca.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q3_K_S.gguf) | Q3_K_S | 2.75GB | | |
| | [Llama-2-7b-evolcodealpaca.IQ3_M.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.IQ3_M.gguf) | IQ3_M | 2.9GB | | |
| | [Llama-2-7b-evolcodealpaca.Q3_K.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q3_K.gguf) | Q3_K | 3.07GB | | |
| | [Llama-2-7b-evolcodealpaca.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q3_K_M.gguf) | Q3_K_M | 3.07GB | | |
| | [Llama-2-7b-evolcodealpaca.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q3_K_L.gguf) | Q3_K_L | 3.35GB | | |
| | [Llama-2-7b-evolcodealpaca.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.IQ4_XS.gguf) | IQ4_XS | 3.4GB | | |
| | [Llama-2-7b-evolcodealpaca.Q4_0.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q4_0.gguf) | Q4_0 | 3.56GB | | |
| | [Llama-2-7b-evolcodealpaca.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.IQ4_NL.gguf) | IQ4_NL | 3.58GB | | |
| | [Llama-2-7b-evolcodealpaca.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q4_K_S.gguf) | Q4_K_S | 3.59GB | | |
| | [Llama-2-7b-evolcodealpaca.Q4_K.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q4_K.gguf) | Q4_K | 3.8GB | | |
| | [Llama-2-7b-evolcodealpaca.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q4_K_M.gguf) | Q4_K_M | 3.8GB | | |
| | [Llama-2-7b-evolcodealpaca.Q4_1.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q4_1.gguf) | Q4_1 | 3.95GB | | |
| | [Llama-2-7b-evolcodealpaca.Q5_0.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q5_0.gguf) | Q5_0 | 4.33GB | | |
| | [Llama-2-7b-evolcodealpaca.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q5_K_S.gguf) | Q5_K_S | 4.33GB | | |
| | [Llama-2-7b-evolcodealpaca.Q5_K.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q5_K.gguf) | Q5_K | 4.45GB | | |
| | [Llama-2-7b-evolcodealpaca.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q5_K_M.gguf) | Q5_K_M | 4.45GB | | |
| | [Llama-2-7b-evolcodealpaca.Q5_1.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q5_1.gguf) | Q5_1 | 4.72GB | | |
| | [Llama-2-7b-evolcodealpaca.Q6_K.gguf](https://huggingface.co/RichardErkhov/neuralmagic_-_Llama-2-7b-evolcodealpaca-gguf/blob/main/Llama-2-7b-evolcodealpaca.Q6_K.gguf) | Q6_K | 5.15GB | | |
| Original model description: | |
| --- | |
| base_model: meta-llama/Llama-2-7b-hf | |
| inference: true | |
| model_type: llama | |
| pipeline_tag: text-generation | |
| datasets: | |
| - theblackcat102/evol-codealpaca-v1 | |
| tags: | |
| - code | |
| --- | |
| # Llama-2-7b-evolcodealpaca | |
| This repo contains a [Llama 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) finetuned for code generation tasks using the [Evolved CodeAlpaca](https://huggingface.co/datasets/theblackcat102/evol-codealpaca-v1) dataset. | |
| Official model weights from [Enabling High-Sparsity Foundational Llama Models with Efficient Pretraining and Deployment](https://arxiv.org/abs/2405.03594). | |
| **Authors**: Neural Magic, Cerebras | |
| ## Usage | |
| Below we share some code snippets on how to get quickly started with running the model. | |
| ### Sparse Transfer | |
| By leveraging a pre-sparsified model's structure, you can efficiently fine-tune on new data, leading to reduced hyperparameter tuning, training times, and computational costs. Learn about this process [here](https://neuralmagic.github.io/docs-v2/get-started/transfer). | |
| ### Running the model | |
| This model may be run with the transformers library. For accelerated inference with sparsity, deploy with [nm-vllm](https://github.com/neuralmagic/nm-vllm) or [deepsparse](https://github.com/neuralmagic/deepsparse). | |
| ```python | |
| # pip install transformers accelerate | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| tokenizer = AutoTokenizer.from_pretrained("neuralmagic/Llama-2-7b-evolcodealpaca") | |
| model = AutoModelForCausalLM.from_pretrained("neuralmagic/Llama-2-7b-evolcodealpaca", device_map="auto") | |
| input_text = "def fibonacci(n):\n" | |
| input_ids = tokenizer(input_text, return_tensors="pt").to("cuda") | |
| outputs = model.generate(**input_ids) | |
| print(tokenizer.decode(outputs[0])) | |
| ``` | |
| ## Evaluation Benchmark Results | |
| Model evaluation metrics and results. | |
| | Benchmark | Metric | Llama-2-7b-evolcodealpaca | | |
| |------------------------------------------------|---------------|-------------| | |
| | [HumanEval](https://arxiv.org/abs/2107.03374) | pass@1 | 32.03 | | |
| ## Model Training Details | |
| Coming soon. | |
| ## Help | |
| For further support, and discussions on these models and AI in general, join [Neural Magic's Slack Community](https://join.slack.com/t/discuss-neuralmagic/shared_invite/zt-q1a1cnvo-YBoICSIw3L1dmQpjBeDurQ) | |