llm.create_chat_completion(
messages = "No input example has been defined for this model task."
)YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Quantization made by Richard Erkhov.
Maral - GGUF
- Model creator: https://huggingface.co/MadShift/
- Original model: https://huggingface.co/MadShift/Maral/
| Name | Quant method | Size |
|---|---|---|
| Maral.Q2_K.gguf | Q2_K | 2.96GB |
| Maral.IQ3_XS.gguf | IQ3_XS | 3.28GB |
| Maral.IQ3_S.gguf | IQ3_S | 3.43GB |
| Maral.Q3_K_S.gguf | Q3_K_S | 3.41GB |
| Maral.IQ3_M.gguf | IQ3_M | 3.52GB |
| Maral.Q3_K.gguf | Q3_K | 3.74GB |
| Maral.Q3_K_M.gguf | Q3_K_M | 3.74GB |
| Maral.Q3_K_L.gguf | Q3_K_L | 4.03GB |
| Maral.IQ4_XS.gguf | IQ4_XS | 4.18GB |
| Maral.Q4_0.gguf | Q4_0 | 4.34GB |
| Maral.IQ4_NL.gguf | IQ4_NL | 4.38GB |
| Maral.Q4_K_S.gguf | Q4_K_S | 4.37GB |
| Maral.Q4_K.gguf | Q4_K | 4.58GB |
| Maral.Q4_K_M.gguf | Q4_K_M | 4.58GB |
| Maral.Q4_1.gguf | Q4_1 | 4.78GB |
| Maral.Q5_0.gguf | Q5_0 | 5.21GB |
| Maral.Q5_K_S.gguf | Q5_K_S | 5.21GB |
| Maral.Q5_K.gguf | Q5_K | 5.34GB |
| Maral.Q5_K_M.gguf | Q5_K_M | 5.34GB |
| Maral.Q5_1.gguf | Q5_1 | 5.65GB |
| Maral.Q6_K.gguf | Q6_K | 6.14GB |
| Maral.Q8_0.gguf | Q8_0 | 7.95GB |
Original model description:
language: - ru license: apache-2.0 pipeline_tag: text-generation library_name: transformers
Maral
Description
Maral is a general-purpose generative language model that demonstrates excellent performance in tasks such as summarization and question-answering, specifically in the Russian language. Its advanced capabilities allow it to generate coherent and contextually accurate responses, making it highly effective for a wide range of natural language processing applications.
๐จโ๐ป Examples of usage
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("MadShift/Maral")
model = AutoModelForCausalLM.from_pretrained("MadShift/Maral", device_map="auto")
input_text = "ะะฒะตะดะธัะต ัะฒะพะน ัะตะบัั ะทะดะตัั"
input_ids = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**input_ids, max_new_tokens=256)
print(tokenizer.decode(outputs[0]))
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# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="RichardErkhov/MadShift_-_Maral-gguf", filename="", )