Poseidon-Reasoning-1.7B
Poseidon-Reasoning-1.7B is a general-purpose, high-efficiency reasoning model fine-tuned on Qwen3-1.7B using the Poseidon-Reasoning-5M dataset (first 70K entries). Designed for mathematical, scientific, and code-related reasoning, this model strikes a balance between structured logic and contextual fluency—ideal for domains demanding symbolic precision and algorithmic thought.
[!note] GGUF: https://huggingface.co/prithivMLmods/Poseidon-Reasoning-1.7B-GGUF
Key Features
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Versatile Reasoning Model Finely tuned for multi-domain reasoning tasks, including mathematics, scientific computation, and code logic—capable of navigating structured problem-solving and analytic workflows.
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Qwen3-1.7B Foundation Built upon Qwen3-1.7B, providing multilingual reasoning capability, efficient token handling, and strong alignment with instruction-following tasks.
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Powered by Poseidon-Reasoning-5M (70K Sample Subset) Trained on a carefully selected subset of 70K entries from the Poseidon-Reasoning-5M dataset—focusing on tasks that emphasize symbolic accuracy, step-by-step thinking, and STEM-relevant clarity.
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Balanced Thinking Mode Supports structured, guided thinking without excessive hallucination or unnecessary verbosity. Ideal for prompt-driven logic tasks with moderate complexity.
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Rich Format Output Outputs include Markdown, Python, LaTeX, and tabular structures—helpful for notebooks, scientific documentation, and programmatic outputs.
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1.7B Parameter Footprint Lightweight enough to run on mid-tier GPUs or CPU-only environments, while offering scalable reasoning power for research, teaching, and light automation.
Quickstart with Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "prithivMLmods/Poseidon-Reasoning-1.7B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "Solve: What is the derivative of sin(x) * ln(x)?"
messages = [
{"role": "system", "content": "You are a structured reasoning assistant for math, science, and code."},
{"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=256
)
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
- General-purpose symbolic reasoning
- Math and science tutoring, theorem solving, and computational guidance
- Structured coding under constraints or STEM-based tasks
- Lightweight environments where interpretability and precision matter
- Prompt-driven reasoning with deterministic steps
Limitations
- Not designed for broad open-domain conversation
- May underperform on creative writing or emotional expression
- Best results occur with clear problem statements and goal-directed prompts
- Less suitable for speculative or abstract reasoning without structure