vLLM

vLLM is a popular LLM inference engine. The NeMo Gym VLLMModel server wraps vLLM’s Chat Completions endpoint and converts requests and responses to NeMo Gym’s native format, the OpenAI Responses API schema.

Most open-source models use Chat Completions format, while NeMo Gym uses the Responses API natively. VLLMModel bridges this gap by converting between the two formats automatically. For background on why NeMo Gym chose the Responses API and how the two schemas differ, see responses-api-evolution.

VLLMModel provides a Responses API to Chat Completions mapping middleware layer via responses_api_models/vllm_model. It assumes you are pointing to a vLLM instance since it relies on vLLM-specific endpoints like /tokenize and vLLM-specific arguments like return_tokens_as_token_ids.

To use VLLMModel, just change the responses_api_models/openai_model/configs/openai_model.yaml in your config paths to responses_api_models/vllm_model/configs/vllm_model.yaml!

$
config_paths="resources_servers/example_multi_step/configs/example_multi_step.yaml,\
>
responses_api_models/vllm_model/configs/vllm_model.yaml"
$
ng_run "+config_paths=[$config_paths]"

Use VLLMModel

Below is an e2e example of how to spin up a NeMo Gym compatible vLLM Chat Completions OpenAI server and run rollout collection with it.

Install vLLM

Please run the steps below in a separate terminal than your NeMo Gym terminal! The installation will take a few minutes.

$
uv venv --python 3.12 --seed .venv
$
source .venv/bin/activate
$
# hf_transfer for faster model download
$
uv pip install hf_transfer vllm --torch-backend=auto

Download the model

This download will take a few minutes.

$
# Qwen/Qwen3-4B-Thinking-2507, usable in Nemo RL!
$
HF_HOME=.cache/ \
>
HF_HUB_ENABLE_HF_TRANSFER=1 \
>
    hf download Qwen/Qwen3-4B-Thinking-2507

$
HF_TOKEN=... \
>
HF_HOME=.cache/ \
>
HF_HUB_ENABLE_HF_TRANSFER=1 \
>
    hf download Qwen/Qwen3-4B-Thinking-2507

Spin up a vLLM server

vLLM server configuration

• If you want to use tools, find the appropriate vLLM arguments regarding the tool call parser to use. In this example, we use Qwen/Qwen3-4B-Thinking-2507, which is suggested to use the hermes tool call parser.
• If you are using a reasoning model, find the appropriate vLLM arguments regarding reasoning parser to use. In this example, we use Qwen/Qwen3-4B-Thinking-2507, which is suggested to use the deepseek_r1 reasoning parser.
• The example below uses --tensor-parallel-size 1 which requires 1 GPU.

The spinup step will take a few minutes.

$
HF_HOME=.cache/ \
>
HOME=. \
>
vllm serve \
>
    Qwen/Qwen3-4B-Thinking-2507 \
>
    --tensor-parallel-size 4 \
>
    --enable-auto-tool-choice --tool-call-parser hermes \
>
    --reasoning-parser deepseek_r1 \
>
    --host 0.0.0.0 \
>
    --port 10240

Configure NeMo Gym to use the local vLLM server

In a second terminal on the same GPU node that was used to spin up the vLLM server, enter the NeMo Gym Python environment, and start the NeMo Gym servers.

$
config_paths="resources_servers/example_multi_step/configs/example_multi_step.yaml,\
>
responses_api_models/vllm_model/configs/vllm_model.yaml"
$
ng_run "+config_paths=[$config_paths]" \
>
    ++policy_base_url=http://0.0.0.0:10240/v1 \
>
    ++policy_model_name=Qwen/Qwen3-4B-Thinking-2507 \
>
    ++policy_api_key=dummy_key

$
hostname -i

Run rollout collection

In a third terminal on the same GPU node that was used to spin up the vLLM server, enter the NeMo Gym Python environment, and run rollout collection.

$
ng_collect_rollouts +agent_name=example_multi_step_simple_agent \
>
    +input_jsonl_fpath=resources_servers/example_multi_step/data/example.jsonl \
>
    +output_jsonl_fpath=results/example_multi_step_rollouts.jsonl

VLLMModel configuration reference

Advanced: chat_template_kwargs

Override chat template behavior for specific models:

1
chat_template_kwargs:
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  enable_thinking: false  # Model-specific

Advanced: extra_body

Pass vLLM-specific parameters not in the standard OpenAI API:

1
extra_body:
2
  guided_json: '{"type": "object", "properties": {...}}'
3
  min_tokens: 10
4
  repetition_penalty: 1.1

Use VLLMModel with multiple replicas of a model endpoint

The vLLM model server supports multiple endpoints for horizontal scaling:

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base_url:
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  - http://gpu-node-1:8000/v1
3
  - http://gpu-node-2:8000/v1
4
  - http://gpu-node-3:8000/v1

How it works:

1. Initial assignment: New sessions are assigned to endpoints using round-robin (session 1 → endpoint 1, session 2 → endpoint 2, etc.)
2. Session affinity: Once assigned, a session always uses the same endpoint (tracked via HTTP session cookies)
3. Why affinity? Multi-turn conversations and agentic workflows that call the model multiple times in one trajectory need to hit the same model endpoint in order to hit the prefix cache, which significantly speeds up the prefill phase of model inference.

Context Length Exceeded Handling

When a conversation exceeds the vLLM model’s maximum context length (max_seq_length), VLLMModel handles the error gracefully instead of crashing the entire rollout collection.

How it works

1. vLLM rejects the request: vLLM returns an HTTP 400 error with a message like "This model's maximum context length is 32768 tokens. However, you requested 32818 tokens...".
2. VLLMModel catches the error: Instead of propagating the exception, VLLMModel returns an empty response with finish_reason: "length".
3. Responses API mapping: The finish_reason: "length" is converted to incomplete_details: { reason: "max_output_tokens" } in the Responses API response returned to the agent.

This is particularly important for multi-turn agentic rollouts where conversation length can grow unpredictably across tool-call turns.

How to detect truncated responses

Downstream consumers (agents, RL training frameworks) can check the incomplete_details field on the response:

1
response = await model_server.responses(request, body)
2
3
if response.incomplete_details and response.incomplete_details["reason"] == "max_output_tokens":
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    # The conversation exceeded the model's context window.
5
    # The response output will be empty — handle accordingly.
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    pass

Implications for training

When using NeMo Gym with NeMo RL or another training framework, responses with incomplete_details.reason == "max_output_tokens" indicate that the full conversation (prompt + prior generations) exceeded max_seq_length. Training frameworks should filter or handle these responses appropriately since they contain no generated tokens.

Training vs Offline Inference

By default, VLLMModel will not track any token IDs explicitly. However, token IDs are necessary when using NeMo Gym in conjunction with a training framework in order to train a model. For training workflows, use the training-dedicated config which enables token ID tracking:

1
# Use vllm_model_for_training.yaml
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return_token_id_information: true

This enables:

• prompt_token_ids: Token IDs for the input prompt
• generation_token_ids: Token IDs for generated text
• generation_log_probs: Log probabilities for each generated token