--- language: - en license: apache-2.0 library_name: transformers tags: - transformers datasets: - mwitiderrick/AlpacaCode base_model: openlm-research/open_llama_3b inference: true model_type: llama prompt_template: '### Instruction:\n {prompt} ### Response: ' created_by: mwitiderrick pipeline_tag: text-generation model-index: - name: mwitiderrick/open_llama_3b_instruct_v_0.2 results: - task: type: text-generation dataset: name: hellaswag type: hellaswag metrics: - type: hellaswag (0-Shot) value: 0.6581 name: hellaswag(0-Shot) - task: type: text-generation dataset: name: winogrande type: winogrande metrics: - type: winogrande (0-Shot) value: 0.6267 name: winogrande(0-Shot) - task: type: text-generation dataset: name: arc_challenge type: arc_challenge metrics: - type: arc_challenge (0-Shot) value: 0.3712 name: arc_challenge(0-Shot) source: url: https://huggingface.co/mwitiderrick/open_llama_3b_instruct_v_0.2 name: open_llama_3b_instruct_v_0.2 model card - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 41.21 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 66.96 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 27.82 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 35.01 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 65.43 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1 name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 1.9 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=mwitiderrick/open_llama_3b_code_instruct_0.1 name: Open LLM Leaderboard --- # 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| ``` # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_mwitiderrick__open_llama_3b_code_instruct_0.1) | Metric |Value| |---------------------------------|----:| |Avg. |39.72| |AI2 Reasoning Challenge (25-Shot)|41.21| |HellaSwag (10-Shot) |66.96| |MMLU (5-Shot) |27.82| |TruthfulQA (0-shot) |35.01| |Winogrande (5-shot) |65.43| |GSM8k (5-shot) | 1.90|