Pyxidis-Manim-CodeGen-1.7B

85
3
1.7B
2 languages
license:apache-2.0
by
prithivMLmods
Language Model
OTHER
1.7B params
New
85 downloads
Early-stage
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Edge AI:
Mobile
Laptop
Server
4GB+ RAM
Mobile
Laptop
Server
Quick Summary

AI model with specialized capabilities.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2GB+ RAM

Training Data Analysis

🔵 Good (7.0/10)

Researched training datasets used by Pyxidis-Manim-CodeGen-1.7B with quality assessment

Specialized For

code

Training Datasets (1)

the stack
🔵 7/10
code
Key Strengths
  • •Legal Clarity: Permissive licenses eliminate licensing concerns
  • •Comprehensive: 358 languages provide broad coverage
  • •Well-Documented: Transparent preprocessing and filtering

Explore our comprehensive training dataset analysis

View All Datasets

Code Examples

**Quickstart with Transformers**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Pyxidis-Manim-CodeGen-1.7B"

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

prompt = "Write a Manim script to animate the Pythagorean theorem using squares on the triangle's sides."

messages = [
    {"role": "system", "content": "You are a Python coding assistant specialized in Manim-based math animations."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
**Quickstart with Transformers**pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Pyxidis-Manim-CodeGen-1.7B"

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

prompt = "Write a Manim script to animate the Pythagorean theorem using squares on the triangle's sides."

messages = [
    {"role": "system", "content": "You are a Python coding assistant specialized in Manim-based math animations."},
    {"role": "user", "content": prompt}
]

text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

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