Bootes-Qwen3_Coder-Reasoning

41
8
4 languages
license:apache-2.0
by
prithivMLmods
Language Model
OTHER
2309.00071B params
New
41 downloads
Early-stage
Try on Hugging FaceAdd to Compare
Edge AI:
Mobile
Laptop
Server
5162GB+ RAM
Mobile
Laptop
Server
Quick Summary

> Bootes-Qwen3\Coder-Reasoning is a fine-tuned variant of the Qwen3-4B architecture, optimized for high-accuracy code reasoning and structured logical task completion.

Device Compatibility

Mobile
4-6GB RAM
Laptop
16GB RAM
Server
GPU
Minimum Recommended
2151GB+ RAM

Code Examples

Quickstart with Transformers🤗pythontransformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Bootes-Qwen3_Coder-Reasoning"

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

prompt = "Write a Python function to check whether a number is a palindrome. Explain each step."

messages = [
    {"role": "system", "content": "You are a precise coding and reasoning assistant trained on CodeAlpaca and developer datasets."},
    {"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/Bootes-Qwen3_Coder-Reasoning"

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

prompt = "Write a Python function to check whether a number is a palindrome. Explain each step."

messages = [
    {"role": "system", "content": "You are a precise coding and reasoning assistant trained on CodeAlpaca and developer datasets."},
    {"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/Bootes-Qwen3_Coder-Reasoning"

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

prompt = "Write a Python function to check whether a number is a palindrome. Explain each step."

messages = [
    {"role": "system", "content": "You are a precise coding and reasoning assistant trained on CodeAlpaca and developer datasets."},
    {"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/Bootes-Qwen3_Coder-Reasoning"

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

prompt = "Write a Python function to check whether a number is a palindrome. Explain each step."

messages = [
    {"role": "system", "content": "You are a precise coding and reasoning assistant trained on CodeAlpaca and developer datasets."},
    {"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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