---

base_model: XHToken/Spark-X2.5-1.7B

library_name: transformers

pipeline_tag: text-generation

license: apache-2.0

language:

• en
• zh

tags:

• gguf
• llama-cpp
• text-generation
• sparkx2_5
• spark

---

Spark-X2.5-1.7B GGUF

This repository contains GGUF format quants for XHToken/Spark-X2.5-1.7B.

Spark-X2.5-1.7B is an ultra-compact, high-efficiency language model featuring a hybrid attention architecture (combining sliding-window attention with full attention layers) and native context lengths up to 1M tokens.

These GGUF files were converted from the original safetensors weights using a custom Spark 2.5 build of llama.cpp at BF16 precision, then quantized into standard k-quants.

---

Available Files and Quantizations

| File | Quant Type | Size | Description / Recommendation |

| :--- | :--- | :--- | :--- |

| Spark-X2.5-1.7B-BF16.gguf | BF16 | 3.19 GB | Full precision base conversion. Highest fidelity, largest size. |

| Spark-X2.5-1.7B-Q8_0.gguf | Q8_0 | 1.70 GB | Near-lossless 8-bit quantization. Recommended for best quality. |

| Spark-X2.5-1.7B-Q6_K.gguf | Q6_K | 1.31 GB | Excellent quality retention with minimal loss. Very balanced choice. |

| Spark-X2.5-1.7B-Q5_K_M.gguf | Q5_K_M | 1.17 GB | Good balance between memory footprint and output quality. |

| Spark-X2.5-1.7B-Q4_K_M.gguf | Q4_K_M | 1.03 GB | Fast, lightweight 4-bit quant. Great default for edge and mobile devices. |

| Spark-X2.5-1.7B-Q3_K_M.gguf | Q3_K_M | 894 MB | Lower memory footprint when RAM or VRAM is tight. |

| Spark-X2.5-1.7B-Q2_K.gguf | Q2_K | 754 MB | Maximum compression. Noticeable quality degradation, best for ultra-constrained setups. |

---

Requirements

Spark-X2.5 uses the Spark2_5ForCausalLM (spark2_5) architecture. To run these files, ensure your local llama.cpp installation or downstream GUI client includes Spark 2.5 runtime support.

---

How to Use

1. With llama.cpp

Run the model directly from the command line using llama-cli:

llama-cli -m ./Spark-X2.5-1.7B-Q4_K_M.gguf \
    -p "You are a helpful and concise assistant.\nUser: Hello! What can you do?\nAssistant:" \
    -n 512 \
    -c 4096 \
    --temp 0.7

To run as a local server:

llama-server -m ./Spark-X2.5-1.7B-Q4_K_M.gguf \
    --host 127.0.0.1 \
    --port 8080 \
    -c 4096

2. With Ollama

Create a Modelfile in the same directory:

FROM ./Spark-X2.5-1.7B-Q4_K_M.gguf

TEMPLATE """<|im_start|>system
{{ .System }}<|im_end|>
<|im_start|>user
{{ .Prompt }}<|im_end|>
<|im_start|>assistant
"""

PARAMETER stop "<|im_start|>"
PARAMETER stop "<|im_end|>"
PARAMETER temperature 0.7

Then build and run the model:

ollama create spark-x2.5-1.7b -f Modelfile
ollama run spark-x2.5-1.7b

3. With LM Studio

1. Download or copy any .gguf file to your LM Studio models folder.
2. Search for Spark-X2.5-1.7B in your local models tab.
3. Load the model with default GPU offloading settings and chat.

---

Original Model Details

• Original weights: XHToken/Spark-X2.5-1.7B
• Base Architecture: Spark 2.5 (Spark2_5ForCausalLM)
• Parameters: 1.71B
• Context Length: Up to 1,048,576 tokens (1M)
• Vocabulary Size: 131,072 tokens
• License: Apache 2.0

---

Attribution & Credits

Original model weights and architecture developed by XHToken.

GGUF conversions produced by Pink Pixel.

Made with 💖 by Pink Pixel