Instructions to use darioooooo0o/Spark-X2.5-1.7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use darioooooo0o/Spark-X2.5-1.7B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use darioooooo0o/Spark-X2.5-1.7B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darioooooo0o/Spark-X2.5-1.7B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darioooooo0o/Spark-X2.5-1.7B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Ollama
How to use darioooooo0o/Spark-X2.5-1.7B-GGUF with Ollama:
```
ollama run hf.co/darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use darioooooo0o/Spark-X2.5-1.7B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use darioooooo0o/Spark-X2.5-1.7B-GGUF with Docker Model Runner:
```
docker model run hf.co/darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M
```

Lemonade

How to use darioooooo0o/Spark-X2.5-1.7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Spark-X2.5-1.7B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use darioooooo0o/Spark-X2.5-1.7B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use darioooooo0o/Spark-X2.5-1.7B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "darioooooo0o/Spark-X2.5-1.7B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Spark-X2.5-1.7B Community Quants (imatrix GGUF)

Requests, questions or suggestions? Message me on X: https://x.com/imdariotoo

Community GGUF quantizations of XHToken/Spark-X2.5-1.7B, a 1.7B hybrid-attention (3:1 sliding-window:full) model with native 1M context. Quantized with an importance matrix, targeting fully GPU-resident inference from 4GB cards up.

Unlike larger models in this family, the 1.7B is small enough that no lossy layer-mixes are needed: even a 4GB card fits Q8_0 at 128K context or Q6_K at 256K. Card tiering is done with context length instead of precision cuts.

Why no layer-aware mixes (unlike the 4B)

The 4B quants needed layer-aware mixes at 8GB/4GB because the VRAM budget forced FFN tensors below Q4. The 1.7B has 28 layers with only 7 full-attention layers (2 KV heads x 256 dim), so its KV cache is ~4.0 KB/token at q4_0 - a third of the 4B's. The budget never forces sub-Q4 weights, and the 4B experiments showed mixes only help when forced. So these are clean uniform quants.

Available quants

file	size	BPW	4GB card	8GB card	12GB+ card
`Spark-X2.5-1.7B-Q8_0.gguf`	1.7 GB	8.50	128K ctx	1M ctx	1M ctx
`Spark-X2.5-1.7B-Q6_K.gguf`	1.4 GB	6.56	256K ctx	1M ctx	1M ctx
`Spark-X2.5-1.7B-Q4_K_M.gguf`	1.1 GB	5.16	256K ctx	1M ctx	1M ctx
`Spark-X2.5-1.7B-IQ4_XS.gguf`	1.0 GB	4.74	256K+ ctx	1M ctx	1M ctx

Context figures assume q4_0 KV cache, ~1.0 GB CUDA/compute overhead, one slot, -ngl 99, flash attention on. The 1M native context actually fits on an 8GB card: Q8_0 weights (1.8 GB) + 1M-token q4_0 KV (4.2 GB) + overhead = ~7 GB.

Measured quality (perplexity, calibration corpus, 512 ctx, CUDA, RTX 3060 12GB)

quant	PPL	delta vs Q8_0
Q8_0	3.854	-
Q6_K	3.855	+0.001 (noise)
Q4_K_M	4.093	+6.2%
IQ4_XS	4.081	+5.9%

IQ4_XS measures slightly better than Q4_K_M despite being smaller - it is the best pick under 1 GB.

Measured speed (RTX 3060 12GB, llama.cpp XHToken fork, p512/n128, -ngl 99, -fa on, f16 KV, 3 reps)

quant	prefill tok/s	decode tok/s	TTFT ms
Q8_0	7856	149.6	71.9
Q6_K	6819	166.4	81.1
Q4_K_M	7289	206.1	75.1
IQ4_XS	7951	207.3	69.2

Roughly 3.5x the decode throughput of the 4B quants on the same card.

Tool-calling verification (safetensors on vLLM 0.28 + Spark-plugin)

The base safetensors was verified for agentic use before quantization: simple tool calls, complex nested/array schemas, multi-turn tool-result loops, 3 parallel tool calls in a single response, and Hermes-style schemas with enums - all passed with clean finish_reason: tool_calls and valid JSON arguments. Decode 81.5 tok/s at 32K ctx on vLLM.

Serve with vLLM (requires XHToken/Spark-plugin) or the XHToken/llama.cpp fork for these GGUFs (the spark2_5 architecture is not in upstream llama.cpp).

Quantization method

Importance matrix computed with llama-imatrix on 200 chunks of an 8 MB calibration corpus (system documentation, Python sources, technical prose), from the BF16 GGUF.
Uniform quants: Q8_0 / Q6_K / Q4_K_M / IQ4_XS passes with --imatrix.
Each variant validated with llama-perplexity on the same corpus, sequentially on one RTX 3060 12GB.

Recommended launch (llama.cpp)

llama-server \
  -m Spark-X2.5-1.7B-Q4_K_M.gguf \
  --chat-template-file chat_template.jinja \
  -c 262144 -ngl 99 -fa auto \
  -ctk q4_0 -ctv q4_0 \
  --temp 1.0 --top-p 0.95 --top-k -1 \
  --jinja --reasoning-format deepseek

Sampling per the model card: temperature 1.0, top_p 0.95, top_k -1. Thinking mode on by default; <tool_call>/<arg_key>/<arg_value> tool-call format handled by llama.cpp's auto parser.

Known model limitation: reasoning loops on open-ended prompts

This is a model-level behavior, not a quantization artifact - it reproduces identically on unquantized BF16 with f16 KV cache. On vague open-ended comparative questions (e.g. "from 10 euros, which currency would today give me the most paper notes in their smallest denomination"), the model can enter an unbounded reasoning loop: it generates thinking tokens until the generation limit with empty visible content. Observed across Q4_K_M + q4_0 KV, Q8_0 + f16 KV, Q6_K + f16 KV, BF16, temp 1.0 and 0.6, with and without tools.

Mitigations:

set a server-side reasoning budget: --reasoning-budget 2000
use agent timeouts / stall detection for unattended runs
rephrasing prompts to be more concrete reduces (but does not eliminate) the risk
simple factual, math, and tool-concrete prompts are unaffected

Agentic tool-use reliability (HermesBench 6-task subset, Q4_K_M, RTX 3060 12GB, 64K+ ctx)

Run through a real Hermes Agent harness: file/terminal, memory, todo, code_execution, session_search, and cronjob tool classes, graded on task correctness AND use of the required tool classes.

result	score	notes
effective pass	5/6	hb2-eval-007 counted as miss: work completed correctly via the execute_code tool, but the grader requires the code_execution toolset name

All tasks: raw_task_score = 1.0 (correct work), zero false-dones, zero timeouts
Tool classes exercised: search_files, read_file, write_file, terminal, memory (+honcho), todo, session_search, cronjob, execute_code
Median task wall: ~21s; slowest 61s (memory task with 11 tool calls)

Known model limitation (documented below) applies mainly to open-ended research-style prompts; tool-concrete tasks like these complete reliably.

License

Apache 2.0, matching the base model. Quantizations are derivative works of XHToken/Spark-X2.5-1.7B.

Downloads last month: 225

GGUF

Model size

2B params

Architecture

spark2_5

Hardware compatibility

4-bit

6-bit

8-bit

Model tree for darioooooo0o/Spark-X2.5-1.7B-GGUF

Base model

XHToken/Spark-X2.5-1.7B-Base

Finetuned

XHToken/Spark-X2.5-1.7B

Quantized

(15)

this model