---

license: apache-2.0

base_model: harshwardhanjadhav/llama3.2-1b-CoT

tags:

- llama

- llama-3

- llama-3.2

- gguf

- quantized

- text-generation

- chain-of-thought

- reasoning

- unsloth

- trl

- openthoughts

language:

- en

pipeline_tag: text-generation

library_name: gguf

---

llama3.2-1b-CoT GGUF

GGUF quantizations of harshwardhanjadhav/llama3.2-1b-CoT.

Fine-tuned version of unsloth/Llama-3.2-1B-Instruct on 10,000 samples from the open-thoughts/OpenThoughts-114k dataset.

Trained to produce structured Chain-of-Thought reasoning traces using <|begin_of_thought|> and <|begin_of_solution|> blocks before delivering final answers.

Specializes in multi-step mathematical and analytical problem solving.

---

Quantization Table

| File | Bits | Size | Use Case |

|------|------|------|----------|

| model_f16.gguf | 16 | ~2.5 GB | Maximum quality, reference |

| model_q8_0.gguf | 8 | ~1.3 GB | Best quality / size tradeoff |

| model_q6_k.gguf | 6 | ~1.0 GB | High quality |

| model_q5_k_m.gguf | 5 | ~0.9 GB | Recommended |

| model_q5_k_s.gguf | 5 | ~0.85 GB | Slightly smaller Q5 |

| model_q4_k_m.gguf | 4 | ~0.75 GB | Good balance |

| model_q4_k_s.gguf | 4 | ~0.70 GB | Smaller Q4 |

| model_q3_k_l.gguf | 3 | ~0.60 GB | Low RAM, large variant |

| model_q3_k_m.gguf | 3 | ~0.57 GB | Low RAM |

| model_q3_k_s.gguf | 3 | ~0.53 GB | Minimum RAM Q3 |

| model_q2_k.gguf | 2 | ~0.45 GB | Extreme compression |

---

VRAM Requirements

| Quant | Min VRAM |

|-------|----------|

| F16 | 4 GB |

| Q8_0 | 2 GB |

| Q4_K_M | 1.5 GB |

| Q2_K | 1 GB |

---

System Prompt (required for CoT)

Your role as an assistant involves thoroughly exploring questions through a systematic long thinking process before providing the final precise and accurate solutions.
This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process.
Please structure your response into two main sections: Thought and Solution.
In the Thought section, detail your reasoning process using the specified format: <|begin_of_thought|> {thought with steps separated with '\n\n'} <|end_of_thought|>
In the Solution section, present the final solution formatted as: <|begin_of_solution|> {final formatted, precise, and clear solution} <|end_of_solution|>

Recommended Inference Parameters

| Parameter | Value | Reason |

|-----------|-------|--------|

| temperature | 0.1 | Stable CoT traces |

| repetition_penalty | 1.15 | Prevents infinite reasoning loops |

| top_p | 0.9 | Filters low-probability tokens |

| max_new_tokens | 4096 | Accommodates long reasoning chains |

---

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf \
  --temp 0.1 \
  --repeat-penalty 1.15 \
  --top-p 0.9 \
  -n 4096 \
  -p "A store offers a 20% discount on a $150 coat, then takes 10% more off. What is the final price?"

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=8192)
output = llm(
    "Solve step by step: 2x + 5 = 13",
    max_tokens=4096,
    temperature=0.1,
    repeat_penalty=1.15,
    top_p=0.9,
)
print(output["choices"][0]["text"])

LM Studio

Search tinyopsec/llama3.2-1b-CoT-GGUF in the model browser.

Ollama

ollama run hf.co/tinyopsec/llama3.2-1b-CoT-GGUF:Q4_K_M

---

Original Model

harshwardhanjadhav/llama3.2-1b-CoT