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---
language:
- en
license: other
library_name: peft
tags:
- text-generation-inference
- sft
base_model: Qwen/Qwen1.5-1.8B-Chat
model-index:
- name: finetune_test_qwen15-1-8b-sft-lora
results:
- 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: 36.18
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/finetune_test_qwen15-1-8b-sft-lora
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: 57.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/finetune_test_qwen15-1-8b-sft-lora
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: 44.96
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/finetune_test_qwen15-1-8b-sft-lora
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: 38.0
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/finetune_test_qwen15-1-8b-sft-lora
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: 61.17
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/finetune_test_qwen15-1-8b-sft-lora
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: 21.53
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/finetune_test_qwen15-1-8b-sft-lora
name: Open LLM Leaderboard
---
Lora sft finetuned version of Qwen/Qwen1.5-1.8B-Chat
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM
config = PeftConfig.from_pretrained("eren23/finetune_test_qwen15-1-8b-sft")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen1.5-1.8B-Chat")
model = PeftModel.from_pretrained(model, "eren23/finetune_test_qwen15-1-8b-sft")
model = model.to("cuda")
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
# make prediction
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen1.5-1.8B-Chat")
prompt = "Give me a short introduction to large language model."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
### Framework versions
- PEFT 0.8.2
# [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_eren23__finetune_test_qwen15-1-8b-sft-lora)
| Metric |Value|
|---------------------------------|----:|
|Avg. |43.27|
|AI2 Reasoning Challenge (25-Shot)|36.18|
|HellaSwag (10-Shot) |57.77|
|MMLU (5-Shot) |44.96|
|TruthfulQA (0-shot) |38.00|
|Winogrande (5-shot) |61.17|
|GSM8k (5-shot) |21.53|
|