Instructions to use Lomesh7777/salespath-grpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lomesh7777/salespath-grpo with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen2.5-1.5B-Instruct")
model = PeftModel.from_pretrained(base_model, "Lomesh7777/salespath-grpo")

Transformers

How to use Lomesh7777/salespath-grpo with Transformers:

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

pipe = pipeline("text-generation", model="Lomesh7777/salespath-grpo")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Lomesh7777/salespath-grpo", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Lomesh7777/salespath-grpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lomesh7777/salespath-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": "Lomesh7777/salespath-grpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lomesh7777/salespath-grpo

SGLang

How to use Lomesh7777/salespath-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 "Lomesh7777/salespath-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": "Lomesh7777/salespath-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 "Lomesh7777/salespath-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": "Lomesh7777/salespath-grpo",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lomesh7777/salespath-grpo with Docker Model Runner:
```
docker model run hf.co/Lomesh7777/salespath-grpo
```

salespath-grpo / eval_results.json

Lomesh7777

Upload eval_results.json with huggingface_hub

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	{
	"base_label": "./sft_checkpoint (PEFT on unsloth/Qwen2.5-1.5B-Instruct)",
	"trained_label": "./grpo_checkpoint (PEFT on unsloth/Qwen2.5-1.5B-Instruct)",
	"seed": 1234,
	"episodes_per_level": 4,
	"base": {
	"1": {
	"violations_per_episode": 0.25,
	"ordering_rate": 0.9166666666666666,
	"close_rate": 0.75,
	"correct_disqual_rate": 0.0,
	"mean_final_reward": 0.24625000000000002,
	"mean_cum_reward": 0.9545833333333333,
	"mean_turns": 6.75,
	"n_episodes": 4,
	"n_disqual_cases": 0
	},
	"2": {
	"violations_per_episode": 1.0,
	"ordering_rate": 0.8,
	"close_rate": 0.0,
	"correct_disqual_rate": 0.0,
	"mean_final_reward": -0.08000000000000002,
	"mean_cum_reward": 0.5800000000000001,
	"mean_turns": 6.0,
	"n_episodes": 4,
	"n_disqual_cases": 0
	},
	"3": {
	"violations_per_episode": 1.0,
	"ordering_rate": 0.5714285714285714,
	"close_rate": 0.0,
	"correct_disqual_rate": 0.0,
	"mean_final_reward": -0.08000000000000002,
	"mean_cum_reward": 0.5342857142857143,
	"mean_turns": 6.0,
	"n_episodes": 4,
	"n_disqual_cases": 0
	},
	"4": {
	"violations_per_episode": 0.0,
	"ordering_rate": 0.75,
	"close_rate": 0.0,
	"correct_disqual_rate": 0.75,
	"mean_final_reward": 0.07500000000000001,
	"mean_cum_reward": -0.6749999999999996,
	"mean_turns": 9.5,
	"n_episodes": 4,
	"n_disqual_cases": 4
	}
	},
	"trained": {
	"1": {
	"violations_per_episode": 0.0,
	"ordering_rate": 1.0,
	"close_rate": 1.0,
	"correct_disqual_rate": 0.0,
	"mean_final_reward": 0.3566666666666667,
	"mean_cum_reward": 1.09,
	"mean_turns": 7.0,
	"n_episodes": 4,
	"n_disqual_cases": 0
	},
	"2": {
	"violations_per_episode": 0.75,
	"ordering_rate": 0.8500000000000001,
	"close_rate": 0.25,
	"correct_disqual_rate": 0.0,
	"mean_final_reward": 0.024999999999999994,
	"mean_cum_reward": 0.7100000000000001,
	"mean_turns": 6.25,
	"n_episodes": 4,
	"n_disqual_cases": 0
	},
	"3": {
	"violations_per_episode": 1.25,
	"ordering_rate": 0.5714285714285714,
	"close_rate": 0.0,
	"correct_disqual_rate": 0.0,
	"mean_final_reward": -0.08000000000000002,
	"mean_cum_reward": 0.5392857142857143,
	"mean_turns": 6.25,
	"n_episodes": 4,
	"n_disqual_cases": 0
	},
	"4": {
	"violations_per_episode": 0.25,
	"ordering_rate": 1.0,
	"close_rate": 0.0,
	"correct_disqual_rate": 1.0,
	"mean_final_reward": 0.2,
	"mean_cum_reward": 0.7050000000000001,
	"mean_turns": 6.25,
	"n_episodes": 4,
	"n_disqual_cases": 4
	}
	}
	}