Instructions to use dev7a/Laguna-XS-2.1-DFlash-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use dev7a/Laguna-XS-2.1-DFlash-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 dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
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 dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
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 dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
Use Docker
docker model run hf.co/dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use dev7a/Laguna-XS-2.1-DFlash-GGUF with Ollama:
ollama run hf.co/dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
- Unsloth Studio
How to use dev7a/Laguna-XS-2.1-DFlash-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for dev7a/Laguna-XS-2.1-DFlash-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for dev7a/Laguna-XS-2.1-DFlash-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for dev7a/Laguna-XS-2.1-DFlash-GGUF to start chatting
- Pi
How to use dev7a/Laguna-XS-2.1-DFlash-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0" } ] } } }Run Pi
# Start Pi in your project directory: pi
- OpenClaw new
How to use dev7a/Laguna-XS-2.1-DFlash-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
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 "dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0" \ --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"
- Docker Model Runner
How to use dev7a/Laguna-XS-2.1-DFlash-GGUF with Docker Model Runner:
docker model run hf.co/dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
- Lemonade
How to use dev7a/Laguna-XS-2.1-DFlash-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
Run and chat with the model
lemonade run user.Laguna-XS-2.1-DFlash-GGUF-Q8_0
List all available models
lemonade list
- Hermes Agent
How to use dev7a/Laguna-XS-2.1-DFlash-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 dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
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 dev7a/Laguna-XS-2.1-DFlash-GGUF:Q8_0
Run Hermes
hermes
- Atomic Chat
File size: 4,550 Bytes
335be33 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 | #!/usr/bin/env python3
"""Verify the release checksum and the required GGUF schema without dependencies."""
from __future__ import annotations
import argparse
import hashlib
import json
import struct
from collections import Counter
from pathlib import Path
from typing import BinaryIO
SCALAR_FORMATS = {
0: "<B",
1: "<b",
2: "<H",
3: "<h",
4: "<I",
5: "<i",
6: "<f",
7: "<?",
10: "<Q",
11: "<q",
12: "<d",
}
def read_exact(stream: BinaryIO, size: int) -> bytes:
value = stream.read(size)
if len(value) != size:
raise ValueError("unexpected end of GGUF file")
return value
def read_scalar(stream: BinaryIO, value_type: int):
fmt = SCALAR_FORMATS[value_type]
return struct.unpack(fmt, read_exact(stream, struct.calcsize(fmt)))[0]
def read_string(stream: BinaryIO, keep: bool = True):
size = read_scalar(stream, 10)
value = read_exact(stream, size)
return value.decode("utf-8") if keep else None
def read_value(stream: BinaryIO, value_type: int, keep: bool = True):
if value_type in SCALAR_FORMATS:
value = read_scalar(stream, value_type)
return value if keep else None
if value_type == 8:
return read_string(stream, keep)
if value_type == 9:
element_type = read_scalar(stream, 4)
count = read_scalar(stream, 10)
values = [read_value(stream, element_type, keep) for _ in range(count)]
return values if keep else None
raise ValueError(f"unsupported GGUF metadata type {value_type}")
def sha256(path: Path) -> str:
digest = hashlib.sha256()
with path.open("rb") as stream:
for chunk in iter(lambda: stream.read(8 * 1024 * 1024), b""):
digest.update(chunk)
return digest.hexdigest()
def inspect(path: Path, expected_keys: set[str]) -> dict:
with path.open("rb") as stream:
if read_exact(stream, 4) != b"GGUF":
raise ValueError("invalid GGUF magic")
version = read_scalar(stream, 4)
tensor_count = read_scalar(stream, 10)
metadata_count = read_scalar(stream, 10)
metadata = {}
for _ in range(metadata_count):
key = read_string(stream)
value_type = read_scalar(stream, 4)
value = read_value(stream, value_type, key in expected_keys)
if key in expected_keys:
metadata[key] = value
tensor_types = Counter()
offsets = []
for _ in range(tensor_count):
read_string(stream, keep=False)
dimensions = read_scalar(stream, 4)
for _ in range(dimensions):
read_scalar(stream, 10)
tensor_types[str(read_scalar(stream, 4))] += 1
offsets.append(read_scalar(stream, 10))
return {
"version": version,
"tensor_count": tensor_count,
"metadata": metadata,
"tensor_type_counts": dict(tensor_types),
"aligned": all(offset % 32 == 0 for offset in offsets),
}
def main() -> int:
parser = argparse.ArgumentParser()
parser.add_argument("artifact", nargs="?", type=Path)
parser.add_argument("--manifest", type=Path, default=Path("manifest/release.json"))
args = parser.parse_args()
expected = json.loads(args.manifest.read_text())
artifact = args.artifact or Path(expected["artifact"]["file"])
failures = []
actual_size = artifact.stat().st_size
actual_hash = sha256(artifact)
if actual_size != expected["artifact"]["size"]:
failures.append(f"size: expected {expected['artifact']['size']}, got {actual_size}")
if actual_hash != expected["artifact"]["sha256"]:
failures.append(f"sha256: expected {expected['artifact']['sha256']}, got {actual_hash}")
gguf_expected = expected["gguf"]
actual = inspect(artifact, set(gguf_expected["metadata"]))
for key in ("version", "tensor_count", "tensor_type_counts", "aligned"):
if actual[key] != gguf_expected[key]:
failures.append(f"{key}: expected {gguf_expected[key]!r}, got {actual[key]!r}")
for key, value in gguf_expected["metadata"].items():
if actual["metadata"].get(key) != value:
failures.append(
f"metadata {key}: expected {value!r}, got {actual['metadata'].get(key)!r}"
)
if failures:
raise SystemExit("verification failed:\n- " + "\n- ".join(failures))
print(f"verified {artifact}: {actual_size} bytes {actual_hash}")
return 0
if __name__ == "__main__":
raise SystemExit(main())
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