Instructions to use lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu with Transformers:

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

pipe = pipeline("text-generation", model="lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu")
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-Instruct-r2egym_32768_8gpu")
model = AutoModelForCausalLM.from_pretrained("lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu")
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

vLLM

How to use lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu 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-Instruct-r2egym_32768_8gpu"
# 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-Instruct-r2egym_32768_8gpu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu

SGLang

How to use lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu 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-Instruct-r2egym_32768_8gpu" \
    --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-Instruct-r2egym_32768_8gpu",
		"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-Instruct-r2egym_32768_8gpu" \
        --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-Instruct-r2egym_32768_8gpu",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu with Docker Model Runner:
```
docker model run hf.co/lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu
```

R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu

This model is a fine-tuned version of Qwen/Qwen2.5-Coder-32B-Instruct 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 4.57.1
Pytorch 2.6.0+cu124
Datasets 4.0.0
Tokenizers 0.22.2

Downloads last month: 7

Safetensors

Model size

1.12M params

Tensor type

BF16

Model tree for lllqaq/R2EGym-32B-Agent-Coder-Instruct-r2egym_32768_8gpu

Base model

Qwen/Qwen2.5-32B

Finetuned

Qwen/Qwen2.5-Coder-32B

Finetuned

Qwen/Qwen2.5-Coder-32B-Instruct

Finetuned

(128)

this model