README.md

---
viewer: false
tags:
- uv-script
- transformers
- continuous-batching
- gpu
- 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

```bash
# 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

```bash
# 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` | Attention backend (`paged|sdpa` or `paged\|flash_attention_2`) |
| `--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.