Instructions to use darcar0/quotebound-27b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use darcar0/quotebound-27b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="darcar0/quotebound-27b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("darcar0/quotebound-27b", device_map="auto")

PEFT
How to use darcar0/quotebound-27b with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use darcar0/quotebound-27b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "darcar0/quotebound-27b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darcar0/quotebound-27b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/darcar0/quotebound-27b

SGLang

How to use darcar0/quotebound-27b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "darcar0/quotebound-27b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darcar0/quotebound-27b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "darcar0/quotebound-27b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "darcar0/quotebound-27b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use darcar0/quotebound-27b with Docker Model Runner:
```
docker model run hf.co/darcar0/quotebound-27b
```

darcar0 commited on Apr 26

Commit

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Upload release asset technical_note_evidence_faithful_reasoning.md

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@@ -108,7 +108,7 @@ Frozen held-out `probe_v0` progression:
 | Bridge + `deterministic_v2` | 1.0000 | 0.4091 | 0.8844 | 0.5773 |
 | **Bridge + `deterministic_v3`** | **1.0000** | **1.0000** | **1.0000** | **1.0000** |
-See [Figure 1: benchmark progression](./benchmark_progression.svg).
 The full final-artifact frozen `probe_v0` score is task `1.0000`, strict
 grounded success `1.0000`, evidence F1 `1.0000`, quote F1 `1.0000`, verify
@@ -151,7 +151,7 @@ dev, and `probe_v0` row.
 | Bridge + `deterministic_v3` | 0.8611 | 0.5833 | 0.8815 | 0.8815 |
 | **Quotebound + `deterministic_v3`** | **0.8889** | **0.5833** | **0.9093** | **0.9093** |
-See [Figure 2: standalone holdout comparison](./standalone_holdout_comparison.svg).
 Quotebound 27B beats the prior bridge model on task accuracy, evidence F1,
 and quote F1 in both raw and normalized form, ties normalized strict, and

 | Bridge + `deterministic_v2` | 1.0000 | 0.4091 | 0.8844 | 0.5773 |
 | **Bridge + `deterministic_v3`** | **1.0000** | **1.0000** | **1.0000** | **1.0000** |
+See [Figure 1: benchmark progression](./assets/benchmark_progression.svg).
 The full final-artifact frozen `probe_v0` score is task `1.0000`, strict
 grounded success `1.0000`, evidence F1 `1.0000`, quote F1 `1.0000`, verify
 | Bridge + `deterministic_v3` | 0.8611 | 0.5833 | 0.8815 | 0.8815 |
 | **Quotebound + `deterministic_v3`** | **0.8889** | **0.5833** | **0.9093** | **0.9093** |
+See [Figure 2: standalone holdout comparison](./assets/standalone_holdout_comparison.svg).
 Quotebound 27B beats the prior bridge model on task accuracy, evidence F1,
 and quote F1 in both raw and normalized form, ties normalized strict, and