Instructions to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746 with Transformers:

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

pipe = pipeline("text-generation", model="lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746")
model = AutoModelForCausalLM.from_pretrained("lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746")
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 Settings

vLLM

How to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746

SGLang

How to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746 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 "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746" \
    --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": "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746",
		"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 "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746" \
        --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": "lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746 with Docker Model Runner:
```
docker model run hf.co/lllqaq/R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746
```

R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746 / README.md

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	---
	library_name: transformers
	license: other
	tags:
	- llama-factory
	- full
	- generated_from_trainer
	model-index:
	- name: R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# R2EGym-32B-Agent-Coder-R2egym-mid-post-step1746

	This model is a fine-tuned version of the local checkpoint `v2v3_from_sample10_step1746` on the R2EGym_SFT_Trajectories dataset.

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 1e-05
	- train_batch_size: 1
	- eval_batch_size: 8
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 8
	- total_train_batch_size: 8
	- total_eval_batch_size: 64
	- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 2.0

	### Training results



	### Framework versions

	- Transformers 4.57.6
	- Pytorch 2.7.1+cu128
	- Datasets 4.0.0
	- Tokenizers 0.22.2