Instructions to use dipta007/decomposeRL-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dipta007/decomposeRL-7b with Transformers:

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

pipe = pipeline("text-generation", model="dipta007/decomposeRL-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dipta007/decomposeRL-7b")
model = AutoModelForCausalLM.from_pretrained("dipta007/decomposeRL-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use dipta007/decomposeRL-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dipta007/decomposeRL-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dipta007/decomposeRL-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dipta007/decomposeRL-7b

SGLang

How to use dipta007/decomposeRL-7b 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 "dipta007/decomposeRL-7b" \
    --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": "dipta007/decomposeRL-7b",
		"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 "dipta007/decomposeRL-7b" \
        --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": "dipta007/decomposeRL-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dipta007/decomposeRL-7b with Docker Model Runner:
```
docker model run hf.co/dipta007/decomposeRL-7b
```

dipta007 commited on 9 days ago

Commit

910375e

verified ·

1 Parent(s): 663efe3

Add in-domain baseline comparison table

Browse files

Files changed (1) hide show

README.md +18 -15

README.md CHANGED Viewed

@@ -238,21 +238,24 @@ The `--max-model-len` matches the training-time `max_seq_length=16016` (with `ma
 ## Performance
-### In-domain — balanced accuracy on 9 claim-verification benchmarks
-| Dataset | # Examples | Balanced Acc |
-|---|---:|---:|
-| ClaimDecomp | 116 | 0.9324 |
-| FEVEROUS | 2,962 | 0.9234 |
-| Ex-FEVER | 4,071 | 0.8724 |
-| Fool-Me-Twice | 1,380 | 0.8584 |
-| PubHealthFact | 985 | 0.8468 |
-| PubMedClaim | 445 | 0.8338 |
-| WiCE | 143 | 0.8138 |
-| HoVer | 4,000 | 0.7635 |
-| FEVER | 401 | 0.7304 |
-| **Micro-average** (sample-weighted) | 14,503 | **0.8445** |
-| **Macro-average** | — | **0.8417** |
 ### Out-of-domain

 ## Performance
+### In-domain — balanced accuracy (%) on 9 claim-verification benchmarks
+Compared against every same-size (Qwen-7B) baseline plus MiniCheck. *Micro* is pooled balanced accuracy across all in-domain samples; *Macro* is the uniform mean across the 9 datasets. **Bold** marks the column winner; *italic* marks the second-best.
+| Method | FEVER | ClaimDecomp | HoVer | FEVEROUS | WiCE | Ex-FEVER | PubHealth | PubMedClaim | FoolMeTwice | Micro | Macro |
+| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
+| **DecomposeRL-7B (ours)** | **74.1** | **98.6** | **76.4** | *93.1* | *86.5* | **87.6** | *87.5* | **85.5** | **87.7** | **84.4** | **86.3** |
+| Simple (7B) | *72.7* | 94.9 | 71.0 | **93.5** | 83.2 | 82.7 | 84.2 | *84.1* | *86.6* | *82.0* | *83.7* |
+| CoT (7B) | 70.0 | 95.5 | 70.9 | 92.2 | 85.6 | *83.8* | 83.8 | 83.2 | 85.0 | 81.8 | 83.3 |
+| DecomP (7B) | 65.5 | 95.3 | 69.0 | 91.9 | 85.0 | 78.0 | 85.7 | 82.5 | 84.1 | 79.3 | 81.9 |
+| HiSS (7B) | 67.7 | 92.8 | 70.2 | 92.7 | 83.6 | 82.4 | 79.2 | 77.0 | 84.5 | 80.7 | 81.1 |
+| MiniCheck | 69.9 | 77.5 | *73.8* | 89.2 | **87.2** | 82.9 | 76.3 | 83.0 | 84.5 | 81.9 | 80.5 |
+| Self-Ask (7B) | 66.5 | 92.7 | 66.9 | 91.9 | 82.5 | 71.7 | 84.2 | 82.6 | 82.8 | 76.7 | 80.2 |
+| FOLK (7B) | 65.0 | 90.8 | 68.2 | 91.0 | 83.6 | 80.2 | 80.5 | 77.8 | 83.1 | 79.0 | 80.0 |
+| QACheck (7B) | 65.4 | *97.3* | 59.1 | 92.7 | 83.0 | 65.4 | **91.0** | 78.0 | 81.6 | 73.1 | 79.3 |
+| Chen-2024 (7B) | 65.4 | 91.1 | 65.3 | 87.9 | 79.6 | 73.3 | 83.3 | 79.2 | 82.3 | 75.7 | 78.6 |
+| ProgramFC (7B) | 60.5 | 92.9 | 65.9 | 88.2 | 85.4 | 74.6 | 77.4 | 74.3 | 76.9 | 75.2 | 77.3 |
+| ClaimDecomp (7B) | 65.2 | 78.9 | 63.5 | 85.5 | 79.2 | 71.6 | 76.0 | 77.6 | 79.4 | 73.3 | 75.2 |
 ### Out-of-domain