Fanar-1-9B

353
20
9.0B
2 languages
license:apache-2.0
by
QCRI
Language Model
OTHER
9B params
New
353 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
21GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
9GB+ RAM

Code Examples

Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Getting Startedpythontransformers
from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "QCRI/Fanar-1-9B"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

# prompt may be in Arabic or English
prompt = "ما هي عاصمة قطر؟"
inputs = tokenizer(prompt, return_tensors="pt", return_token_type_ids=False)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

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