Instructions to use XcaliburMoon/XcaliburBiz-1.8B-report with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use XcaliburMoon/XcaliburBiz-1.8B-report with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use XcaliburMoon/XcaliburBiz-1.8B-report 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 XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf XcaliburMoon/XcaliburBiz-1.8B-report: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 XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf XcaliburMoon/XcaliburBiz-1.8B-report: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 XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M

Use Docker

docker model run hf.co/XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M

LM Studio
Jan

vLLM

How to use XcaliburMoon/XcaliburBiz-1.8B-report with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "XcaliburMoon/XcaliburBiz-1.8B-report"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "XcaliburMoon/XcaliburBiz-1.8B-report",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M

Ollama
How to use XcaliburMoon/XcaliburBiz-1.8B-report with Ollama:
```
ollama run hf.co/XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use XcaliburMoon/XcaliburBiz-1.8B-report with Docker Model Runner:
```
docker model run hf.co/XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M
```

Lemonade

How to use XcaliburMoon/XcaliburBiz-1.8B-report with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XcaliburMoon/XcaliburBiz-1.8B-report:Q4_K_M

Run and chat with the model

lemonade run user.XcaliburBiz-1.8B-report-Q4_K_M

List all available models

lemonade list

Atomic Chat

XcaliburBiz

A lightweight report writer. A compact data summary goes in and a professional report comes out.

What it is

Base checkpoint: Qwen/Qwen1.5-1.8B-Chat
Supervised pairs: 658
Preference pairs: 1699
Adapter rank: 16

License and attribution

This model is a derivative work based on Qwen1.5-1.8B-Chat by the Qwen Team, modified via Supervised Fine-Tuning and Direct Preference Optimization (DPO) by XcaliburMoon.

License: Apache License 2.0 (https://www.apache.org/licenses/LICENSE-2.0), the license of the base checkpoint, carried forward unchanged.
Publisher: XcaliburMoon
Retained copies: LICENSE and NOTICE in this repository.
ONNX conversion reference: Xenova/Qwen1.5-1.8B-Chat.

Modifications

Supervised fine-tuning of a low rank adapter over section pairs built from the house reports.
Direct preference optimization from the supervised adapter.
Merging the aligned adapter into the base weights at full precision.
Quantization of the merged weights into the GGUF forms and one ONNX export.

Apache License 2.0 grants no right to the trade names of the licensor beyond the use that describes the origin of the work. The name Qwen and the names of the Qwen Team belong to their owners. The name appears in this repository to state where the base checkpoint came from, and it states no endorsement of this work.

House rules the model is trained to keep

No em dash and no en dash.
No hyphen with spaces around it used as a dash.
No emoji.
Every contraction written out in full.
No worn prepositional phrase.

Measured on held-out material

Form	Style clean	Figures kept	Passed	Seconds per report
base	0.925	0.3372	0.225	10.826
posttrain	0.975	0.1244	0.075	4.697
xcaliburbiz-Q2_K	0.95	0.0798	0.075	0.485
xcaliburbiz-Q3_K_M	1.0	0.1084	0.1	0.38
xcaliburbiz-Q4_K_M	0.975	0.1011	0.075	0.683
xcaliburbiz-Q5_K_M	0.925	0.1423	0.125	1.004
xcaliburbiz-Q6_K	1.0	0.1203	0.05	1.002
xcaliburbiz-Q8_0	0.975	0.1098	0.075	0.925

Files

LICENSE 11357 bytes
NOTICE 1298 bytes
adapter/README.md 1506 bytes
adapter/adapter_config.json 1101 bytes
adapter/adapter_model.safetensors 12595704 bytes
adapter/added_tokens.json 80 bytes
adapter/chat_template.jinja 328 bytes
adapter/merges.txt 1671853 bytes
adapter/special_tokens_map.json 367 bytes
adapter/tokenizer.json 11418266 bytes
adapter/tokenizer_config.json 973 bytes
adapter/vocab.json 2776833 bytes
adapter-sft/README.md 1513 bytes
adapter-sft/adapter_config.json 1101 bytes
adapter-sft/adapter_model.safetensors 12595704 bytes
adapter-sft/added_tokens.json 80 bytes
adapter-sft/chat_template.jinja 328 bytes
adapter-sft/merges.txt 1671853 bytes
adapter-sft/special_tokens_map.json 367 bytes
adapter-sft/tokenizer.json 11418266 bytes
adapter-sft/tokenizer_config.json 973 bytes
adapter-sft/vocab.json 2776833 bytes
merged-bf16/README.md 1566 bytes
merged-bf16/added_tokens.json 80 bytes
merged-bf16/chat_template.jinja 328 bytes
merged-bf16/config.json 1230 bytes
merged-bf16/generation_config.json 205 bytes
merged-bf16/merges.txt 1671853 bytes
merged-bf16/model.safetensors 3673690696 bytes
merged-bf16/special_tokens_map.json 367 bytes
merged-bf16/tokenizer.json 11418266 bytes
merged-bf16/tokenizer_config.json 973 bytes
merged-bf16/vocab.json 2776833 bytes
gguf/quantize_record.json 1101 bytes
gguf/xcaliburbiz-Q2_K.gguf 846570240 bytes
gguf/xcaliburbiz-Q3_K_M.gguf 1015880448 bytes
gguf/xcaliburbiz-Q4_K_M.gguf 1217753856 bytes
gguf/xcaliburbiz-Q5_K_M.gguf 1376776960 bytes
gguf/xcaliburbiz-Q6_K.gguf 1579027200 bytes
gguf/xcaliburbiz-Q8_0.gguf 1958304512 bytes
bench.json 284243 bytes
sft_record.json 530 bytes
dpo_record.json 5586 bytes
merge_record.json 187 bytes
quantize_record.json 1101 bytes
data/sft.jsonl 2738739 bytes
data/dpo.jsonl 8982824 bytes
data/eval.jsonl 248519 bytes
data/manifest.json 6204 bytes

The ONNX folder under gguf/ sits beside the quants for the in-browser engine.

Data

The pairs were built from the house reports. The supervised pairs carry a data summary in and a report section out; the preference pairs carry a good answer and a faulty one. The validation split is held out by report rather than by pair, so no measured answer was trained on.

Downloads last month: 557

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for XcaliburMoon/XcaliburBiz-1.8B-report

Base model

Qwen/Qwen1.5-1.8B-Chat

Finetuned

(13)

this model