metadata
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 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
| 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|