Text Generation
Transformers
Safetensors
qwen2
Generated from Trainer
open-r1
dapo
trl
conversational
text-generation-inference
Instructions to use kangdawei/DAPO-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kangdawei/DAPO-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kangdawei/DAPO-7B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kangdawei/DAPO-7B") model = AutoModelForCausalLM.from_pretrained("kangdawei/DAPO-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]:])) - Inference
- Local Apps Settings
- vLLM
How to use kangdawei/DAPO-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kangdawei/DAPO-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": "kangdawei/DAPO-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kangdawei/DAPO-7B
- SGLang
How to use kangdawei/DAPO-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 "kangdawei/DAPO-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": "kangdawei/DAPO-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 "kangdawei/DAPO-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": "kangdawei/DAPO-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use kangdawei/DAPO-7B with Docker Model Runner:
docker model run hf.co/kangdawei/DAPO-7B
Training in progress, step 80
Browse files- adapter_model.safetensors +1 -1
- dynamic_sampling_log.txt +80 -0
adapter_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 323014560
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:bffb47daaf8b258a0fee8b83cca970c428d15021cd8894d085e6d620b1dbe9e0
|
| 3 |
size 323014560
|
dynamic_sampling_log.txt
CHANGED
|
@@ -563,3 +563,83 @@ Step Attempts Total Prompts Valid Prompts Efficiency
|
|
| 563 |
69 3 18 6 33.3 %
|
| 564 |
69 3 18 6 33.3 %
|
| 565 |
69 6 36 6 16.7 %
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 563 |
69 3 18 6 33.3 %
|
| 564 |
69 3 18 6 33.3 %
|
| 565 |
69 6 36 6 16.7 %
|
| 566 |
+
70 2 12 6 50.0 %
|
| 567 |
+
70 3 18 6 33.3 %
|
| 568 |
+
70 5 30 6 20.0 %
|
| 569 |
+
70 1 6 6 100.0 %
|
| 570 |
+
70 2 12 6 50.0 %
|
| 571 |
+
70 5 30 6 20.0 %
|
| 572 |
+
70 3 18 6 33.3 %
|
| 573 |
+
70 3 18 6 33.3 %
|
| 574 |
+
71 5 30 6 20.0 %
|
| 575 |
+
71 3 18 6 33.3 %
|
| 576 |
+
71 3 18 6 33.3 %
|
| 577 |
+
71 3 18 6 33.3 %
|
| 578 |
+
71 2 12 6 50.0 %
|
| 579 |
+
71 2 12 6 50.0 %
|
| 580 |
+
71 1 6 6 100.0 %
|
| 581 |
+
71 3 18 6 33.3 %
|
| 582 |
+
72 2 12 6 50.0 %
|
| 583 |
+
72 5 30 6 20.0 %
|
| 584 |
+
72 5 30 6 20.0 %
|
| 585 |
+
72 8 48 6 12.5 %
|
| 586 |
+
72 6 36 6 16.7 %
|
| 587 |
+
72 4 24 6 25.0 %
|
| 588 |
+
72 2 12 6 50.0 %
|
| 589 |
+
72 1 6 6 100.0 %
|
| 590 |
+
73 6 36 6 16.7 %
|
| 591 |
+
73 2 12 6 50.0 %
|
| 592 |
+
73 2 12 6 50.0 %
|
| 593 |
+
73 2 12 6 50.0 %
|
| 594 |
+
73 2 12 6 50.0 %
|
| 595 |
+
73 3 18 6 33.3 %
|
| 596 |
+
73 2 12 6 50.0 %
|
| 597 |
+
73 4 24 6 25.0 %
|
| 598 |
+
74 5 30 6 20.0 %
|
| 599 |
+
74 2 12 6 50.0 %
|
| 600 |
+
74 3 18 6 33.3 %
|
| 601 |
+
74 2 12 6 50.0 %
|
| 602 |
+
74 2 12 6 50.0 %
|
| 603 |
+
74 2 12 6 50.0 %
|
| 604 |
+
74 4 24 6 25.0 %
|
| 605 |
+
74 6 36 6 16.7 %
|
| 606 |
+
75 6 36 6 16.7 %
|
| 607 |
+
75 3 18 6 33.3 %
|
| 608 |
+
75 3 18 6 33.3 %
|
| 609 |
+
75 2 12 6 50.0 %
|
| 610 |
+
75 6 36 6 16.7 %
|
| 611 |
+
75 1 6 6 100.0 %
|
| 612 |
+
75 2 12 6 50.0 %
|
| 613 |
+
75 2 12 6 50.0 %
|
| 614 |
+
76 2 12 6 50.0 %
|
| 615 |
+
76 3 18 6 33.3 %
|
| 616 |
+
76 3 18 6 33.3 %
|
| 617 |
+
76 2 12 6 50.0 %
|
| 618 |
+
76 1 6 6 100.0 %
|
| 619 |
+
76 2 12 6 50.0 %
|
| 620 |
+
76 3 18 6 33.3 %
|
| 621 |
+
76 8 48 6 12.5 %
|
| 622 |
+
77 3 18 6 33.3 %
|
| 623 |
+
77 2 12 6 50.0 %
|
| 624 |
+
77 2 12 6 50.0 %
|
| 625 |
+
77 1 6 6 100.0 %
|
| 626 |
+
77 2 12 6 50.0 %
|
| 627 |
+
77 2 12 6 50.0 %
|
| 628 |
+
77 2 12 6 50.0 %
|
| 629 |
+
77 3 18 6 33.3 %
|
| 630 |
+
78 3 18 6 33.3 %
|
| 631 |
+
78 2 12 6 50.0 %
|
| 632 |
+
78 2 12 6 50.0 %
|
| 633 |
+
78 4 24 6 25.0 %
|
| 634 |
+
78 3 18 6 33.3 %
|
| 635 |
+
78 7 42 6 14.3 %
|
| 636 |
+
78 7 42 6 14.3 %
|
| 637 |
+
78 3 18 6 33.3 %
|
| 638 |
+
79 6 36 6 16.7 %
|
| 639 |
+
79 4 24 6 25.0 %
|
| 640 |
+
79 4 24 6 25.0 %
|
| 641 |
+
79 1 6 6 100.0 %
|
| 642 |
+
79 1 6 6 100.0 %
|
| 643 |
+
79 3 18 6 33.3 %
|
| 644 |
+
79 1 6 6 100.0 %
|
| 645 |
+
79 2 12 6 50.0 %
|