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RichardErkhov/
MaziyarPanahi_-_calme-2.3-qwen2-72b-gguf

Name: MaziyarPanahi_-_calme-2.3-qwen2-72b-gguf
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calme-2.3-qwen2-72b - GGUF

Model creator: https://huggingface.co/MaziyarPanahi/
Original model: https://huggingface.co/MaziyarPanahi/calme-2.3-qwen2-72b/

Name	Quant method	Size
calme-2.3-qwen2-72b.Q2_K.gguf	Q2_K	27.76GB
calme-2.3-qwen2-72b.Q3_K_S.gguf	Q3_K_S	32.12GB
calme-2.3-qwen2-72b.Q3_K.gguf	Q3_K	35.11GB
calme-2.3-qwen2-72b.Q3_K_M.gguf	Q3_K_M	35.11GB
calme-2.3-qwen2-72b.Q3_K_L.gguf	Q3_K_L	36.79GB
calme-2.3-qwen2-72b.IQ4_XS.gguf	IQ4_XS	37.4GB
calme-2.3-qwen2-72b.Q4_0.gguf	Q4_0	38.4GB
calme-2.3-qwen2-72b.IQ4_NL.gguf	IQ4_NL	38.9GB
calme-2.3-qwen2-72b.Q4_K_S.gguf	Q4_K_S	40.88GB
calme-2.3-qwen2-72b.Q4_K.gguf	Q4_K	44.16GB
calme-2.3-qwen2-72b.Q4_K_M.gguf	Q4_K_M	44.16GB
calme-2.3-qwen2-72b.Q4_1.gguf	Q4_1	42.56GB
calme-2.3-qwen2-72b.Q5_0.gguf	Q5_0	46.72GB
calme-2.3-qwen2-72b.Q5_K_S.gguf	Q5_K_S	47.85GB
calme-2.3-qwen2-72b.Q5_K.gguf	Q5_K	50.71GB
calme-2.3-qwen2-72b.Q5_K_M.gguf	Q5_K_M	50.71GB
calme-2.3-qwen2-72b.Q5_1.gguf	Q5_1	50.88GB
calme-2.3-qwen2-72b.Q6_K.gguf	Q6_K	59.93GB
calme-2.3-qwen2-72b.Q8_0.gguf	Q8_0	71.96GB

Original model description:

language:

en license: other library_name: transformers tags:
chat
qwen
qwen2
calme
calme2
finetune
chatml base_model: Qwen/Qwen2-72B license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2-72B/blob/main/LICENSE pipeline_tag: text-generation inference: false model_creator: MaziyarPanahi quantized_by: MaziyarPanahi model-index:
name: calme-2.3-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: 38.5 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.3-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: 51.23 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.3-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: 14.73 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.3-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: 16.22 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.3-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: 11.24 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.3-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: 49.1 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=MaziyarPanahi/calme-2.3-qwen2-72b name: Open LLM Leaderboard

MaziyarPanahi/calme-2.3-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

Coming soon

🏆 Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric	Value
Avg.	30.17
IFEval (0-Shot)	38.50
BBH (3-Shot)	51.23
MATH Lvl 5 (4-Shot)	14.73
GPQA (0-shot)	16.22
MuSR (0-shot)	11.24
MMLU-PRO (5-shot)	49.10

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

python

# 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-2.3-qwen2-72b")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.3-qwen2-72b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.3-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.

RichardErkhov/MaziyarPanahi_-_calme-2.3-qwen2-72b-gguf