mwitiderrick/open_llama_3b_code_instruct_0.1-GGUF

Quantized GGUF model files for open_llama_3b_code_instruct_0.1 from mwitiderrick

Name	Quant method	Size
open_llama_3b_code_instruct_0.1.fp16.gguf	fp16	6.86 GB
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	q3_k_m	2.27 GB
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	q5_k_m	2.76 GB
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	q8_0	3.64 GB

Original Model Card:

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

Detailed 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|