Text Generation
Transformers
Safetensors
llama
Generated from Trainer
open-r1
dapo
trl
conversational
text-generation-inference
Instructions to use kangdawei/DAPO-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kangdawei/DAPO-8B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kangdawei/DAPO-8B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kangdawei/DAPO-8B") model = AutoModelForCausalLM.from_pretrained("kangdawei/DAPO-8B") 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-8B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kangdawei/DAPO-8B" # 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-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kangdawei/DAPO-8B
- SGLang
How to use kangdawei/DAPO-8B 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-8B" \ --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-8B", "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-8B" \ --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-8B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use kangdawei/DAPO-8B with Docker Model Runner:
docker model run hf.co/kangdawei/DAPO-8B
Training in progress, step 160
Browse files- adapter_config.json +2 -2
- adapter_model.safetensors +1 -1
- dynamic_sampling_log.txt +149 -0
- tokenizer.json +2 -2
- training_args.bin +1 -1
adapter_config.json
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"revision": null,
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"target_modules": [
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"down_proj",
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"gate_proj",
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"up_proj",
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"o_proj"
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],
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"target_parameters": null,
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"revision": null,
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"target_modules": [
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"k_proj",
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"v_proj",
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"down_proj",
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"gate_proj",
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"up_proj",
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"o_proj"
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],
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"target_parameters": null,
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adapter_model.safetensors
CHANGED
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version https://git-lfs.github.com/spec/v1
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size 335605144
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version https://git-lfs.github.com/spec/v1
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oid sha256:2b451663b0f74fae0d53a36528ab760d2b65a192a718f32817b524332befa494
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size 335605144
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dynamic_sampling_log.txt
CHANGED
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@@ -1214,3 +1214,152 @@ Step Attempts Total Prompts Valid Prompts Efficiency
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149 3 18 6 33.3 %
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tokenizer.json
CHANGED
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@@ -1,3 +1,3 @@
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version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
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| 3 |
-
size
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| 1 |
version https://git-lfs.github.com/spec/v1
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oid sha256:26c881aaf4ef935b1516ec79ad6405dd2a459f2b5d431a8a4a1399c92f3ba022
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+
size 17209711
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training_args.bin
CHANGED
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@@ -1,3 +1,3 @@
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| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
-
oid sha256:
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| 3 |
size 8760
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| 1 |
version https://git-lfs.github.com/spec/v1
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| 2 |
+
oid sha256:f226307e55b90254d38ea4c353a35911a575a170c592310101fe03a927c9dc78
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| 3 |
size 8760
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