metadata
language:
- en
license: other
library_name: transformers
tags:
- chat
- qwen
- qwen2
- finetune
- chatml
base_model: Qwen/Qwen2-72B-Instruct
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen2-72B-Instruct/blob/main/LICENSE
pipeline_tag: text-generation
inference: false
model_creator: MaziyarPanahi
quantized_by: MaziyarPanahi
model-index:
- name: calme-2.1-qwen2-72b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 81.63
name: strict accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-qwen2-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 57.33
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-qwen2-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 36.03
name: exact match
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-qwen2-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 17.45
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-qwen2-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 20.15
name: acc_norm
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-qwen2-72b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 49.05
name: accuracy
source:
url: >-
https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.1-qwen2-72b
name: Open LLM Leaderboard
![Qwen2 fine-tune](/MaziyarPanahi/calme-2.1-qwen2-72b/resolve/main/qwen2-fine-tunes-maziyar-panahi.webp)
MaziyarPanahi/calme-2.1-qwen2-72b
This model is a fine-tuned version of the powerful Qwen/Qwen2-72B-Instruct
, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.
Use Cases
This model is suitable for a wide range of applications, including but not limited to:
- Advanced question-answering systems
- Intelligent chatbots and virtual assistants
- Content generation and summarization
- Code generation and analysis
- Complex problem-solving and decision support
⚡ Quantized GGUF
All GGUF models are available here: MaziyarPanahi/calme-2.1-qwen2-72b-GGUF
🏆 Open LLM Leaderboard Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 43.61 |
IFEval (0-Shot) | 81.63 |
BBH (3-Shot) | 57.33 |
MATH Lvl 5 (4-Shot) | 36.03 |
GPQA (0-shot) | 17.45 |
MuSR (0-shot) | 20.15 |
MMLU-PRO (5-shot) | 49.05 |
Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
---|---|---|---|---|---|---|---|
truthfulqa_mc2 | 2 | none | 0 | acc | 0.6761 | ± | 0.0148 |
Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
---|---|---|---|---|---|---|---|
winogrande | 1 | none | 5 | acc | 0.8248 | ± | 0.0107 |
Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
---|---|---|---|---|---|---|---|
arc_challenge | 1 | none | 25 | acc | 0.6852 | ± | 0.0136 |
none | 25 | acc_norm | 0.7184 | ± | 0.0131 |
Tasks | Version | Filter | n-shot | Metric | Value | Stderr | |
---|---|---|---|---|---|---|---|
gsm8k | 3 | strict-match | 5 | exact_match | 0.8582 | ± | 0.0096 |
flexible-extract | 5 | exact_match | 0.8893 | ± | 0.0086 |
Prompt Template
This model uses ChatML
prompt template:
<|im_start|>system
{System}
<|im_end|>
<|im_start|>user
{User}
<|im_end|>
<|im_start|>assistant
{Assistant}
How to use
# Use a pipeline as a high-level helper
from transformers import pipeline
messages = [
{"role": "user", "content": "Who are you?"},
]
pipe = pipeline("text-generation", model="MaziyarPanahi/calme-2.1-qwen2-72b")
pipe(messages)
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-72b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-72b")
Ethical Considerations
As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.