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| Name | Quant method | Size |
|---|---|---|
| hf-ar-134000.Q2_K.gguf | Q2_K | 0.73GB |
| hf-ar-134000.IQ3_XS.gguf | IQ3_XS | 0.76GB |
| hf-ar-134000.IQ3_S.gguf | IQ3_S | 0.78GB |
| hf-ar-134000.Q3_K_S.gguf | Q3_K_S | 0.78GB |
| hf-ar-134000.IQ3_M.gguf | IQ3_M | 0.8GB |
| hf-ar-134000.Q3_K.gguf | Q3_K | 0.83GB |
| hf-ar-134000.Q3_K_M.gguf | Q3_K_M | 0.83GB |
| hf-ar-134000.Q3_K_L.gguf | Q3_K_L | 0.87GB |
| hf-ar-134000.IQ4_XS.gguf | IQ4_XS | 0.87GB |
| hf-ar-134000.Q4_0.gguf | Q4_0 | 0.9GB |
| hf-ar-134000.IQ4_NL.gguf | IQ4_NL | 0.9GB |
| hf-ar-134000.Q4_K_S.gguf | Q4_K_S | 0.9GB |
| hf-ar-134000.Q4_K.gguf | Q4_K | 0.93GB |
| hf-ar-134000.Q4_K_M.gguf | Q4_K_M | 0.93GB |
| hf-ar-134000.Q4_1.gguf | Q4_1 | 0.95GB |
| hf-ar-134000.Q5_0.gguf | Q5_0 | 1.01GB |
| hf-ar-134000.Q5_K_S.gguf | Q5_K_S | 1.01GB |
| hf-ar-134000.Q5_K.gguf | Q5_K | 1.02GB |
| hf-ar-134000.Q5_K_M.gguf | Q5_K_M | 1.02GB |
| hf-ar-134000.Q5_1.gguf | Q5_1 | 1.06GB |
| hf-ar-134000.Q6_K.gguf | Q6_K | 1.12GB |
| hf-ar-134000.Q8_0.gguf | Q8_0 | 1.45GB |
google/gemma-7b1from transformers import AutoTokenizer, AutoModelForCausalLM
2# Initialize model and tokenizer
3TEST_PROMPT = "الزرادشتية هي ديانة انتشرت في بلاد"
4save_path = "nouamanetazi/hf-ar-134000"
5tokenizer = AutoTokenizer.from_pretrained(save_path)
6input_ids = tokenizer(TEST_PROMPT, return_tensors="pt")["input_ids"].cuda() # google/gemma-7b
7print("Input prompt:", tokenizer.batch_decode(input_ids)[0])
8
9model = AutoModelForCausalLM.from_pretrained(save_path, device="cuda", dtype=torch.bfloat16)
10outputs = model.generate(input_ids, max_new_tokens=100)
11print("Generated text:", tokenizer.batch_decode(outputs)[0])