llama-3.2-1b-instruct-fine-tuned GGUF
GGUF quantizations of ai-nexuz/llama-3.2-1b-instruct-fine-tuned.
Fine-tuned version of meta-llama/Llama-3.2-1B-Instruct on the kanhatakeyama/wizardlm8x22b-logical-math-coding-sft dataset.
Specializes in logical reasoning, mathematics, and code generation.
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 |
Usage
llama.cpp
./llama-cli -m model_q4_k_m.gguf -p "Solve: 2x + 5 = 13" -n 256
llama-cpp-python
from llama_cpp import Llama
llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=2048)
output = llm("Solve: 2x + 5 = 13", max_tokens=256)
print(output["choices"][0]["text"])
LM Studio
Search tinyopsec/llama-3.2-1b-instruct-fine-tuned-GGUF in the model browser.
Ollama
ollama run hf.co/tinyopsec/llama-3.2-1b-instruct-fine-tuned-GGUF:Q4_K_M