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RichardErkhov/
MaziyarPanahi_-_calme-2.1-qwen2.5-72b-gguf

Name: MaziyarPanahi_-_calme-2.1-qwen2.5-72b-gguf
Availability: InStock

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Quantization made by Richard Erkhov.

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calme-2.1-qwen2.5-72b - GGUF

Model creator: https://huggingface.co/MaziyarPanahi/
Original model: https://huggingface.co/MaziyarPanahi/calme-2.1-qwen2.5-72b/

Name	Quant method	Size
calme-2.1-qwen2.5-72b.Q2_K.gguf	Q2_K	27.76GB
calme-2.1-qwen2.5-72b.IQ3_XS.gguf	IQ3_XS	30.58GB
calme-2.1-qwen2.5-72b.IQ3_S.gguf	IQ3_S	32.12GB
calme-2.1-qwen2.5-72b.Q3_K_S.gguf	Q3_K_S	32.12GB
calme-2.1-qwen2.5-72b.IQ3_M.gguf	IQ3_M	33.06GB
calme-2.1-qwen2.5-72b.Q3_K.gguf	Q3_K	35.11GB
calme-2.1-qwen2.5-72b.Q3_K_M.gguf	Q3_K_M	35.11GB
calme-2.1-qwen2.5-72b.Q3_K_L.gguf	Q3_K_L	12.6GB
calme-2.1-qwen2.5-72b.IQ4_XS.gguf	IQ4_XS	37.4GB
calme-2.1-qwen2.5-72b.Q4_0.gguf	Q4_0	38.4GB
calme-2.1-qwen2.5-72b.IQ4_NL.gguf	IQ4_NL	38.9GB
calme-2.1-qwen2.5-72b.Q4_K_S.gguf	Q4_K_S	40.87GB
calme-2.1-qwen2.5-72b.Q4_K.gguf	Q4_K	44.16GB
calme-2.1-qwen2.5-72b.Q4_K_M.gguf	Q4_K_M	44.16GB
calme-2.1-qwen2.5-72b.Q4_1.gguf	Q4_1	42.56GB
calme-2.1-qwen2.5-72b.Q5_0.gguf	Q5_0	46.71GB
calme-2.1-qwen2.5-72b.Q5_K_S.gguf	Q5_K_S	47.84GB
calme-2.1-qwen2.5-72b.Q5_K.gguf	Q5_K	50.7GB
calme-2.1-qwen2.5-72b.Q5_K_M.gguf	Q5_K_M	50.7GB
calme-2.1-qwen2.5-72b.Q5_1.gguf	Q5_1	50.87GB
calme-2.1-qwen2.5-72b.Q6_K.gguf	Q6_K	59.92GB
calme-2.1-qwen2.5-72b.Q8_0.gguf	Q8_0	71.95GB

Original model description:

language:

en license: other library_name: transformers license_name: tongyi-qianwen license_link: https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/LICENSE tags:
chat
qwen
qwen2
qwen2.5
finetune
chatml base_model: Qwen/Qwen2.5-72B datasets:
MaziyarPanahi/truthy-dpo-v0.1-axolotl model_name: calme-2.1-qwen2.5-72b pipeline_tag: text-generation inference: false model_creator: MaziyarPanahi

MaziyarPanahi/calme-2.1-qwen2.5-72b

This model is a fine-tuned version of the powerful Qwen/Qwen2.5-72B-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

coming soon.

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-2.1-qwen2.5-72b")
pipe(messages)


# Load model directly

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MaziyarPanahi/calme-2.1-qwen2.5-72b")
model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-qwen2.5-72b")

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-2.1-qwen2.5-72b-gguf