Gliese-4B-OSS-0410/README.md

license, language, base_model, pipeline_tag, library_name, tags

license	language	base_model	pipeline_tag	library_name	tags
apache-2.0	en	prithivMLmods/Kepler-Qwen3-4B-Super-Thinking	text-generation	transformers	trl pytorch text-generation-inference thinking gpt_oss math code smoothing agent

Gliese-4B-OSS-0410

Gliese-4B-OSS-0410 is a reasoning-focused model fine-tuned on Qwen-4B for enhanced reasoning and polished token probability distributions, delivering balanced multilingual generation across mathematics and general-purpose reasoning tasks. The model is fine-tuned on curated GPT-OSS synthetic dataset entries, improving its ability to handle structured reasoning, probabilistic inference, and multilingual tasks with precision.

Note

GGUF: https://huggingface.co/prithivMLmods/Gliese-4B-OSS-0410-GGUF

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Key Features

1. Enhanced Reasoning Precision Refined token probability distributions improve reasoning quality and ensure balanced, context-aware outputs.

2. Event Simulation and Logical Analysis Capable of modeling random events, probability-driven reasoning, and structured decision-making with strong logical consistency.

3. Multilingual Mathematical and General-Purpose Problem Solving Delivers robust performance in mathematics, probability, and structured multilingual tasks, enabling broad applicability in research and education.

4. Hybrid Symbolic–Probabilistic Thinking Combines structured logic, probabilistic inference, and reasoning fluency to improve performance on uncertainty-driven tasks.

5. Structured Output Generation Generates well-formatted outputs in LaTeX, Markdown, JSON, CSV, and YAML, supporting technical workflows and data-oriented research.

6. Optimized Lightweight Footprint With 4B parameters, it runs efficiently on mid-range GPUs, offline clusters, and edge devices without compromising reasoning performance.

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Quickstart with Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Gliese-4B-OSS-0410"

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

prompt = "Simulate the probability of rolling two dice and getting a sum greater than 9. Show the reasoning."

messages = [
    {"role": "system", "content": "You are a reasoning tutor skilled in probability, logic, and multilingual problem-solving."},
    {"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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Intended Use

• Balanced multilingual reasoning and probability modeling
• Event simulation, uncertainty analysis, and structured problem solving
• Educational and research-focused reasoning tasks
• Deployment in mid-resource environments with efficient inference
• Structured technical content and data format generation

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Limitations

• Primarily focused on reasoning and mathematics; less suited for creative writing
• Despite its 4B size, extremely complex multi-hop reasoning tasks may remain challenging
• Prioritizes structured reasoning and probabilistic accuracy over conversational tone
• May produce inconsistent results with very long contexts or cross-domain multi-document inputs