113 lines
3.9 KiB
Markdown
113 lines
3.9 KiB
Markdown
|
---
|
|||
|
|
license: apache-2.0
|
|||
|
|
language:
|
|||
|
|
- en
|
|||
|
|
base_model:
|
|||
|
|
- prithivMLmods/Kepler-Qwen3-4B-Super-Thinking
|
|||
|
|
pipeline_tag: text-generation
|
|||
|
|
library_name: transformers
|
|||
|
|
tags:
|
|||
|
|
- 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](https://huggingface.co/prithivMLmods/Gliese-4B-OSS-0410-GGUF)
|
|||
|
|
|
|||
|
|
---
|
|||
|
|
|
|||
|
|
## 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.
|
|||
|
|
|
|||
|
|
---
|
|||
|
|
|
|||
|
|
## Quickstart with Transformers
|
|||
|
|
|
|||
|
|
```python
|
|||
|
|
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)
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
---
|
|||
|
|
|
|||
|
|
## 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
|
|||
|
|
|
|||
|
|
---
|
|||
|
|
|
|||
|
|
## 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**
|