Instructions to use maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/qwen2.5-7b-instruct-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo") - Transformers
How to use maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo
- SGLang
How to use maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo 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 "maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo" \ --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": "maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo", "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 "maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo" \ --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": "maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Unsloth Studio
How to use maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo", max_seq_length=2048, ) - Docker Model Runner
How to use maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo with Docker Model Runner:
docker model run hf.co/maull04/checkpoint-vcg-qwen2.5-7b-instruct-grpo
Upload folder using huggingface_hub
Browse files- README.md +2 -2
- adapter_config.json +5 -5
- adapter_model.safetensors +1 -1
- optimizer.pt +2 -2
- rng_state.pth +1 -1
- scheduler.pt +1 -1
- trainer_state.json +2204 -4
- training_args.bin +1 -1
README.md
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|
| 2211 |
}
|
| 2212 |
],
|
| 2213 |
"logging_steps": 1,
|
| 2214 |
-
"max_steps":
|
| 2215 |
-
"num_input_tokens_seen":
|
| 2216 |
"num_train_epochs": 1,
|
| 2217 |
"save_steps": 100,
|
| 2218 |
"stateful_callbacks": {
|
|
|
|
| 2 |
"best_global_step": null,
|
| 3 |
"best_metric": null,
|
| 4 |
"best_model_checkpoint": null,
|
| 5 |
+
"epoch": 0.78125,
|
| 6 |
"eval_steps": 500,
|
| 7 |
+
"global_step": 200,
|
| 8 |
"is_hyper_param_search": false,
|
| 9 |
"is_local_process_zero": true,
|
| 10 |
"is_world_process_zero": true,
|
|
|
|
| 2208 |
"rewards/reward_function/mean": 0.25066205859184265,
|
| 2209 |
"rewards/reward_function/std": 0.5054366588592529,
|
| 2210 |
"step": 100
|
| 2211 |
+
},
|
| 2212 |
+
{
|
| 2213 |
+
"completion_length": 971.75,
|
| 2214 |
+
"completions/clipped_ratio": 0.0,
|
| 2215 |
+
"completions/max_length": 1311.0,
|
| 2216 |
+
"completions/max_terminated_length": 1311.0,
|
| 2217 |
+
"completions/mean_length": 971.75,
|
| 2218 |
+
"completions/mean_terminated_length": 971.75,
|
| 2219 |
+
"completions/min_length": 562.0,
|
| 2220 |
+
"completions/min_terminated_length": 562.0,
|
| 2221 |
+
"epoch": 0.39453125,
|
| 2222 |
+
"frac_reward_zero_std": 0.0,
|
| 2223 |
+
"grad_norm": 0.35067111253738403,
|
| 2224 |
+
"kl": 0.0005845300620421767,
|
| 2225 |
+
"learning_rate": 0.0,
|
| 2226 |
+
"loss": 0.0,
|
| 2227 |
+
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training_args.bin
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version https://git-lfs.github.com/spec/v1
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| 3 |
size 6865
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size 6865
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