--- base_model: mwitiderrick/open_llama_3b_code_instruct_0.1 created_by: mwitiderrick datasets: - mwitiderrick/AlpacaCode inference: false language: - en library_name: transformers license: apache-2.0 model-index: - name: mwitiderrick/open_llama_3b_instruct_v_0.2 results: - dataset: name: hellaswag type: hellaswag metrics: - name: hellaswag(0-Shot) type: hellaswag (0-Shot) value: 0.6581 task: type: text-generation - dataset: name: winogrande type: winogrande metrics: - name: winogrande(0-Shot) type: winogrande (0-Shot) value: 0.6267 task: type: text-generation - dataset: name: arc_challenge type: arc_challenge metrics: - name: arc_challenge(0-Shot) type: arc_challenge (0-Shot) value: 0.3712 source: name: open_llama_3b_instruct_v_0.2 model card url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2 task: type: text-generation model_creator: mwitiderrick model_name: open_llama_3b_code_instruct_0.1 model_type: llama pipeline_tag: text-generation prompt_template: '### Instruction:\n {prompt} ### Response: ' quantized_by: afrideva tags: - transformers - gguf - ggml - quantized - q2_k - q3_k_m - q4_k_m - q5_k_m - q6_k - q8_0 --- # mwitiderrick/open_llama_3b_code_instruct_0.1-GGUF Quantized GGUF model files for [open_llama_3b_code_instruct_0.1](https://huggingface.co/mwitiderrick/open_llama_3b_code_instruct_0.1) from [mwitiderrick](https://huggingface.co/mwitiderrick) | Name | Quant method | Size | | ---- | ---- | ---- | | [open_llama_3b_code_instruct_0.1.fp16.gguf](https://huggingface.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF/resolve/main/open_llama_3b_code_instruct_0.1.fp16.gguf) | fp16 | 6.86 GB | | [open_llama_3b_code_instruct_0.1.q2_k.gguf](https://huggingface.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF/resolve/main/open_llama_3b_code_instruct_0.1.q2_k.gguf) | q2_k | 2.15 GB | | [open_llama_3b_code_instruct_0.1.q3_k_m.gguf](https://huggingface.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF/resolve/main/open_llama_3b_code_instruct_0.1.q3_k_m.gguf) | q3_k_m | 2.27 GB | | [open_llama_3b_code_instruct_0.1.q4_k_m.gguf](https://huggingface.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF/resolve/main/open_llama_3b_code_instruct_0.1.q4_k_m.gguf) | q4_k_m | 2.58 GB | | [open_llama_3b_code_instruct_0.1.q5_k_m.gguf](https://huggingface.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF/resolve/main/open_llama_3b_code_instruct_0.1.q5_k_m.gguf) | q5_k_m | 2.76 GB | | [open_llama_3b_code_instruct_0.1.q6_k.gguf](https://huggingface.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF/resolve/main/open_llama_3b_code_instruct_0.1.q6_k.gguf) | q6_k | 3.64 GB | | [open_llama_3b_code_instruct_0.1.q8_0.gguf](https://huggingface.co/afrideva/open_llama_3b_code_instruct_0.1-GGUF/resolve/main/open_llama_3b_code_instruct_0.1.q8_0.gguf) | q8_0 | 3.64 GB | ## Original Model Card: # OpenLLaMA Code Instruct: An Open Reproduction of LLaMA This is an [OpenLlama model](https://huggingface.co/openlm-research/open_llama_3b) that has been fine-tuned on 1 epoch of the [AlpacaCode](https://huggingface.co/datasets/mwitiderrick/AlpacaCode) dataset (122K rows). ## Prompt Template ``` ### Instruction: {query} ### Response: ``` ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline tokenizer = AutoTokenizer.from_pretrained("mwitiderrick/open_llama_3b_code_instruct_0.1") model = AutoModelForCausalLM.from_pretrained("mwitiderrick/open_llama_3b_code_instruct_0.1") query = "Write a quick sort algorithm in Python" text_gen = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200) output = text_gen(f"### Instruction:\n{query}\n### Response:\n") print(output[0]['generated_text']) """ ### Instruction: write a quick sort algorithm in Python ### Response: def quick_sort(arr): if len(arr) <= 1: return arr else: pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) arr = [5,2,4,3,1] print(quick_sort(arr)) """ [1, 2, 3, 4, 5] """ ``` ## Metrics [Detailed metrics](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_code_instruct_0.1) ``` | Tasks |Version|Filter|n-shot|Metric|Value | |Stderr| |----------|-------|------|-----:|------|-----:|---|-----:| |winogrande|Yaml |none | 0|acc |0.6267|± |0.0136| |hellaswag|Yaml |none | 0|acc |0.4962|± |0.0050| | | |none | 0|acc_norm|0.6581|± |0.0047| |arc_challenge|Yaml |none | 0|acc |0.3481|± |0.0139| | | |none | 0|acc_norm|0.3712|± |0.0141| |truthfulqa|N/A |none | 0|bleu_max | 24.2580|± |0.5985| | | |none | 0|bleu_acc | 0.2876|± |0.0003| | | |none | 0|bleu_diff | -8.3685|± |0.6065| | | |none | 0|rouge1_max | 49.3907|± |0.7350| | | |none | 0|rouge1_acc | 0.2558|± |0.0002| | | |none | 0|rouge1_diff|-10.6617|± |0.6450| | | |none | 0|rouge2_max | 32.4189|± |0.9587| | | |none | 0|rouge2_acc | 0.2142|± |0.0002| | | |none | 0|rouge2_diff|-12.9903|± |0.9539| | | |none | 0|rougeL_max | 46.2337|± |0.7493| | | |none | 0|rougeL_acc | 0.2424|± |0.0002| | | |none | 0|rougeL_diff|-11.0285|± |0.6576| | | |none | 0|acc | 0.3072|± |0.0405| ```