Downloads · 30 days
32
7% of all-time downloads
RichardErkhov/Eurdem_-_megatron_v3_2x7B-gguf
Eurdem_-_megatron_v3_2x7B-gguf is a machine learning model from RichardErkhov. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
Downloads · 30 days
32
7% of all-time downloads
All-time downloads
430
Public
Repo size
146 GB
Likes
0
Public
Click a slice to open those files.
.gguf169 GB · 100%
From the Hugging Face model README
Quantization made by Richard Erkhov.
megatron_v3_2x7B - GGUF
| Name | Quant method | Size |
|---|---|---|
| megatron_v3_2x7B.Q2_K.gguf | Q2_K | 4.43GB |
| megatron_v3_2x7B.IQ3_XS.gguf | IQ3_XS | 4.95GB |
| megatron_v3_2x7B.IQ3_S.gguf | IQ3_S | 5.22GB |
| megatron_v3_2x7B.Q3_K_S.gguf | Q3_K_S | 5.2GB |
| megatron_v3_2x7B.IQ3_M.gguf | IQ3_M | 5.35GB |
| megatron_v3_2x7B.Q3_K.gguf | Q3_K | 5.78GB |
| megatron_v3_2x7B.Q3_K_M.gguf | Q3_K_M | 5.78GB |
| megatron_v3_2x7B.Q3_K_L.gguf | Q3_K_L | 6.27GB |
| megatron_v3_2x7B.IQ4_XS.gguf | IQ4_XS | 6.5GB |
| megatron_v3_2x7B.Q4_0.gguf | Q4_0 | 6.78GB |
| megatron_v3_2x7B.IQ4_NL.gguf | IQ4_NL | 6.85GB |
| megatron_v3_2x7B.Q4_K_S.gguf | Q4_K_S | 6.84GB |
| megatron_v3_2x7B.Q4_K.gguf | Q4_K | 7.25GB |
| megatron_v3_2x7B.Q4_K_M.gguf | Q4_K_M | 7.25GB |
| megatron_v3_2x7B.Q4_1.gguf | Q4_1 | 7.52GB |
| megatron_v3_2x7B.Q5_0.gguf | Q5_0 | 8.26GB |
| megatron_v3_2x7B.Q5_K_S.gguf | Q5_K_S | 8.26GB |
| megatron_v3_2x7B.Q5_K.gguf | Q5_K | 8.51GB |
| megatron_v3_2x7B.Q5_K_M.gguf | Q5_K_M | 8.51GB |
| megatron_v3_2x7B.Q5_1.gguf | Q5_1 | 9.01GB |
| megatron_v3_2x7B.Q6_K.gguf | Q6_K | 9.84GB |
| megatron_v3_2x7B.Q8_0.gguf | Q8_0 | 12.75GB |
license: apache-2.0 tags:
Megatron_v3_2x7B is a bilingual Mixure of Experts (MoE) which can comprehend and speak English/Turkish. Megatron, MoE mimarisine sahip Türkçe ve İngilizce talimatları anlayan ve cevap veren bir modeldir.
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Eurdem/Megatron_v3_2x7B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Tell me about AI"}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=1024, do_sample=True, temperature=0.7, top_k=500, top_p=0.95)
print(outputs[0]["generated_text"])