peft safetensors text-generation-inference sft en base_model:Qwen/Qwen1.5-1.8B-Chat license:other model-index region:us
---
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|

