uv-scripts/transformers-inference

Transformers Continuous Batching Scripts

GPU inference scripts using transformers' native continuous batching (CB). No vLLM dependency required.

Why transformers CB?

• Instant new model support - works with any model supported by transformers, including newly released architectures. No waiting for vLLM to add support.
• No dependency headaches - no vLLM, flashinfer, or custom wheel indexes. Just transformers + accelerate.
• Simple HF Jobs setup - no Docker image needed. Just hf jobs uv run.
• ~95% of vLLM throughput - uses PagedAttention and continuous scheduling for near-vLLM performance.

Available Scripts

generate-responses.py

Generate responses for prompts in a dataset. Supports chat messages and plain text prompts.

Quick Start

# Local (requires GPU)
uv run generate-responses.py \
    username/input-dataset \
    username/output-dataset \
    --prompt-column question

# HF Jobs (single GPU)
hf jobs uv run --flavor l4x1 -s HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/transformers-inference/raw/main/generate-responses.py \
    username/input-dataset \
    username/output-dataset \
    --prompt-column question \
    --max-tokens 1024

# HF Jobs (multi-GPU for larger models)
hf jobs uv run --flavor l4x4 -s HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/transformers-inference/raw/main/generate-responses.py \
    username/input-dataset \
    username/output-dataset \
    --model-id Qwen/Qwen3-30B-A3B-Instruct-2507 \
    --messages-column messages \
    --max-batch-tokens 2048 \
    --max-tokens 4096

Example with SmolTalk2

# Generate responses for SmolTalk2 chat data
hf jobs uv run --flavor l4x1 -s HF_TOKEN \
    https://huggingface.co/datasets/uv-scripts/transformers-inference/raw/main/generate-responses.py \
    HuggingFaceTB/smoltalk2 username/smoltalk2-responses \
    --subset SFT \
    --split OpenHermes_2.5_no_think \
    --messages-column messages \
    --max-tokens 256

Parameters

Parameter	Default	Description
--model-id	Qwen/Qwen3-4B-Instruct-2507	Any HF causal LM model
--messages-column	messages	Column with chat messages
--prompt-column	-	Column with plain text prompts (alternative to messages)
--output-column	response	Name for the generated response column
--temperature	0.7	Sampling temperature
--top-p	0.8	Top-p (nucleus) sampling
--top-k	20	Top-k sampling
--max-tokens	4096	Maximum tokens to generate per response
--repetition-penalty	1.0	Repetition penalty
--max-batch-tokens	512	Token budget per scheduling step (see below)
--dtype	bfloat16	Model precision (bfloat16, float16, float32)
--attn-implementation	`paged	sdpa`
--max-samples	all	Limit to N samples (useful for testing)
--hf-token	-	HF token (or use HF_TOKEN env var)
--skip-long-prompts	True	Skip prompts exceeding context length

Tuning --max-batch-tokens

This is the key performance parameter. It controls how many tokens the continuous batching scheduler processes per step:

• Too low (e.g., 128): GPU underutilized, slow throughput
• Too high (e.g., 8192): May cause out-of-memory errors
• Default 512: Conservative, works on most GPUs
• Recommended for A100/H100: 2048-4096
• Recommended for L4: 512-1024

If you hit OOM errors, reduce this value or switch to --dtype float16.

Current Limitations

• Single GPU only - device_map="auto" (pipeline parallelism) doesn't work with CB's PagedAttention cache. Transformers does have tensor parallelism (tp_plan="auto") for supported models, but it requires torchrun and is undocumented with CB. For now, use a model that fits on one GPU (e.g., 8B in bf16 on A10G/L4 with 24GB).
• Text-only - no vision-language model support yet.

When to use this vs vLLM

	Transformers CB	vLLM
Best for	New/niche models, simple setup, avoiding dependency issues	Maximum throughput, production serving
Model support	Any transformers model, immediately	Popular models, may lag on new architectures
Dependencies	transformers + accelerate	vllm + flashinfer + custom indexes
Docker image	Not needed	vllm/vllm-openai recommended
Multi-GPU	Single GPU only (for now)	Tensor parallelism
Performance	~95% of vLLM for text generation	Fastest for supported models
VLM support	Not yet	Yes

Rule of thumb: Use transformers CB when you want simplicity and broad model support. Use vLLM when you need maximum throughput with well-supported models.