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open_llama_3b_code_instruct_0.1 - GGUF
- Model creator: https://huggingface.co/mwitiderrick/
- Original model: https://huggingface.co/mwitiderrick/open_llama_3b_code_instruct_0.1/
Original model description:
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 that has been fine-tuned on 1 epoch of the AlpacaCode dataset (122K rows).
Prompt Template
### Instruction:
{query}
### Response:
<Leave new line for model to respond>
Usage
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
| 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
Detailed results can be found here
| 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 |
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