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Model: prithivMLmods/Gliese-4B-OSS-0410
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---
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
---
![1](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/xwNz8R9cHHBArUKbTKs6U.png)
# **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 SymbolicProbabilistic 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**

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{%- if tools %}
{{- '<|im_start|>system\n' }}
{%- if messages[0]['role'] == 'system' %}
{{- messages[0]['content'] }}
{%- else %}
{{- 'You are a helpful assistant.' }}
{%- endif %}
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{%- if messages[0]['role'] == 'system' %}
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
{%- else %}
{{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in messages %}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{%- if message.content %}
{{- '\n' + message.content }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{{- tool_call.arguments | tojson }}
{{- '}\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "tool" %}
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

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tokenizer_config.json Normal file
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