GeoScholar-QA-1.2B

32

1

1.2B

2 languages

license:cc-by-4.0

by

yasserrmd

Language Model

OTHER

1.2B params

New

32 downloads

Early-stage

Try on Hugging Face Add to Compare

Edge AI:

Mobile

Laptop

Server

3GB+ RAM

Mobile

Laptop

Server

Quick Summary

[](https://creativecommons.

Device Compatibility

Mobile

4-6GB RAM

Laptop

16GB RAM

Server

GPU

Minimum Recommended

2GB+ RAM

Code Examples

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

✅ Example Usagepythontransformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "yasserrmd/GeoScholar-QA-1.2B"

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

messages = [
    {"role": "user", "content": "How do plate tectonics explain the formation of volcanoes along subduction zones?"}
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt"
).to("cuda")

outputs = model.generate(
    **inputs,
    max_new_tokens=256,
    temperature=0.3,
    repetition_penalty=1.05
)

print(tokenizer.decode(outputs, skip_special_tokens=True))

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