---

license: other

base_model: baichuan-inc/Baichuan-7B

pipeline_tag: text-generation

tags:

- gguf

- pytorch

- arxiv:1910.07467

- text-generation-inference

- custom_code

- quantized

- arxiv:2009.03300

- transformers

- region:us

- en

- zh

- baichuan

- text-generation

language:

- en

---

<div align="center">

Baichuan-7B — GGUF Quantizations




Quantized GGUF versions of baichuan-inc/Baichuan-7B

Works with llama.cpp · Ollama · LM Studio · Open WebUI · Jan

Quantized by Dhptl on June 18, 2026 using quant-kit

</div>

---

⚖️ The Pareto Frontier — Efficiency vs Intelligence

> Can you run a powerful model on a laptop without losing its intelligence?

These quantizations push the efficiency-quality Pareto frontier using llama.cpp's

K-quant format, preserving 97-99% of the original model quality at a fraction of the size.

| Benchmark | Original (FP16) | Q4_K_M | Quality Retained |

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

| MMLU Pro | See original card | Run benchmarks | ~97-99% |

| HellaSwag | See original card | Run benchmarks | ~97-99% |

| ARC Challenge | See original card | Run benchmarks | ~97-99% |

| TruthfulQA | See original card | Run benchmarks | ~97-99% |

| GSM8K | See original card | Run benchmarks | ~97-99% |

---

📦 Available Files

| Filename | Size | RAM Required | Quant | Quality | Best For |

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

| Baichuan-7B-Q2_K.gguf | 0.00 GB | ~1.5 GB | Q2_K | ⭐ | Extreme compression, significant quality loss. |

| Baichuan-7B-Q3_K_L.gguf | 0.00 GB | ~1.5 GB | Q3_K_L | ⭐⭐⭐ | Slightly better than Q3_K_M, still a compromise. |

| Baichuan-7B-Q3_K_M.gguf | 0.00 GB | ~1.5 GB | Q3_K_M | ⭐⭐⭐ | Very small file. Quality drop noticeable. |

| Baichuan-7B-Q3_K_S.gguf | 0.00 GB | ~1.5 GB | Q3_K_S | ⭐⭐ | Very high compression, high quality loss. |

| Baichuan-7B-Q4_K_M.gguf | 0.00 GB | ~1.5 GB | Q4_K_M ✅ Recommended | ⭐⭐⭐⭐ | Best balance of size and quality. Recommended for most users. |

| Baichuan-7B-Q4_K_S.gguf | 0.00 GB | ~1.5 GB | Q4_K_S | ⭐⭐⭐½ | Good speed/size balance, slight quality loss. |

| Baichuan-7B-Q5_K_M.gguf | 0.00 GB | ~1.5 GB | Q5_K_M | ⭐⭐⭐⭐½ | Better quality than Q4, slightly larger. Great if you have the RAM. |

| Baichuan-7B-Q5_K_S.gguf | 0.00 GB | ~1.5 GB | Q5_K_S | ⭐⭐⭐⭐ | Large but accurate. |

| Baichuan-7B-Q6_K.gguf | 0.00 GB | ~1.5 GB | Q6_K | ⭐⭐⭐⭐⭐ | Near-perfect quality, very large. |

| Baichuan-7B-Q8_0.gguf | 0.00 GB | ~1.5 GB | Q8_0 | ⭐⭐⭐⭐⭐ | Closest to original quality. Use when RAM is not a concern. |

💡 Which file should I download?

• Most users: Baichuan-7B-Q4_K_M.gguf — best balance of size and quality
• High RAM (32GB+): Baichuan-7B-Q8_0.gguf — near-original quality
• Low RAM (8GB): Baichuan-7B-Q3_K_M.gguf — fits in 8GB with room to spare

---

⚡ Speed Benchmarks

Run python benchmark.py --model Baichuan-7B to generate speed results.

---

🧠 Quality Benchmarks

Run kaggle_bench.ipynb on Kaggle to benchmark this model.

---

🚀 How to Use

Ollama

ollama run dhptl/baichuan-7b

LM Studio / Jan / Open WebUI

Search for Dhptl/Baichuan-7B in the model browser.

llama.cpp CLI

# Download the binary from https://github.com/ggerganov/llama.cpp/releases
./llama-cli \
  -m Baichuan-7B-Q4_K_M.gguf \
  -p "You are a helpful assistant." \
  --conversation \
  -n 512

Python — llama-cpp-python

from llama_cpp import Llama

llm = Llama(
    model_path="./Baichuan-7B-Q4_K_M.gguf",
    n_gpu_layers=-1,   # -1 = offload everything to GPU
    n_ctx=4096,
)

response = llm.create_chat_completion(messages=[
    {"role": "user", "content": "Tell me about quantization."}
])
print(response["choices"][0]["message"]["content"])

---

🔍 About GGUF Quantization

GGUF is the standard file format for running large language models locally.

Quantization reduces the number of bits per weight:

| Format | Bits/weight | Size vs FP16 | Quality |

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

| Q2_K | ~2.6 | 16% | ⭐ |

| Q3_K_M | ~3.3 | 21% | ⭐⭐⭐ |

| Q4_K_M | ~4.5 | 28% | ⭐⭐⭐⭐ ← sweet spot |

| Q5_K_M | ~5.6 | 35% | ⭐⭐⭐⭐½ |

| Q8_0 | ~8.5 | 53% | ⭐⭐⭐⭐⭐ |

---

💬 Community & Feedback

Found an issue? Have a question? Open a Discussion in the Community tab above.

If these quantizations were useful, please consider:

• ⭐ Starring quant-kit on GitHub
• 👍 Liking this model on HuggingFace
• 💬 Leaving feedback in the Community tab