Shoolife/Qwen3-1.7B-TensorRT-LLM-Checkpoint-FP16

Qwen3-1.7B TensorRT-LLM Checkpoint (FP16)

This repository contains a community-converted TensorRT-LLM checkpoint for `Qwen/Qwen3-1.7B`.

It is a TensorRT-LLM checkpoint-format repository, not a prebuilt engine. The intent is to let you download the checkpoint from Hugging Face and build an engine locally for your own GPU and TensorRT-LLM version.

Who This Repo Is For

This repository is for users who already work with TensorRT-LLM and want a ready-made TensorRT-LLM checkpoint that they can turn into a local engine for their own GPU.

It is not:

• —a prebuilt TensorRT engine
• —a plain Transformers checkpoint
• —an Ollama model
• —a one-click chat model that can be run directly after download

How to Use

1. 1.Download this repository from Hugging Face.
2. 2.Build a local engine with trtllm-build for your own GPU and TensorRT-LLM version.
3. 3.Run inference with the engine you built.

The Build Example section below shows the validated local command used for the benchmark snapshot in this README.

Model Characteristics

• —Base model: Qwen/Qwen3-1.7B
• —License: apache-2.0
• —Architecture: Qwen3ForCausalLM
• —Upstream maximum context length (max_position_embeddings): 40960
• —Hidden size: 2048
• —Intermediate size: 6144
• —Layers: 28
• —Attention heads: 16
• —KV heads: 8
• —Vocabulary size: 151936

These values come from the upstream model/checkpoint configuration. They describe the model family itself, not a specific locally built TensorRT engine.

Checkpoint Details

• —TensorRT-LLM version used for conversion: 1.2.0rc6
• —Checkpoint dtype: float16
• —Quantization: none
• —KV cache quantization: none
• —Tensor parallel size: 1
• —Checkpoint files:
• —config.json
• —rank0.safetensors
• —tokenizer and generation files copied from the upstream Hugging Face model

For this FP16 checkpoint, float16 is the primary checkpoint dtype and there is no separate low-bit quantization recipe applied.

Files

• —config.json: TensorRT-LLM checkpoint config
• —rank0.safetensors: TensorRT-LLM checkpoint weights
• —generation_config.json: upstream generation config
• —tokenizer.json: upstream tokenizer
• —tokenizer_config.json: upstream tokenizer config
• —merges.txt: upstream merges file
• —vocab.json: upstream vocabulary

Build Example

The following command is the validated local engine build used for the benchmarks in this README. These values are build-time/runtime settings for one local engine, not limits of the checkpoint itself.

Build an engine locally with TensorRT-LLM:

huggingface-cli download Shoolife/Qwen3-1.7B-TensorRT-LLM-Checkpoint-FP16 --local-dir ./checkpoint

trtllm-build \
  --checkpoint_dir ./checkpoint \
  --output_dir ./engine \
  --gemm_plugin auto \
  --gpt_attention_plugin auto \
  --max_batch_size 1 \
  --max_input_len 512 \
  --max_seq_len 1024 \
  --max_num_tokens 256 \
  --workers 1 \
  --monitor_memory

If you rebuild the engine with different limits, memory usage and supported request shapes will change accordingly.

Conversion

This checkpoint was produced from the upstream model with TensorRT-LLM Qwen conversion tooling:

python convert_checkpoint.py \
  --model_dir ./Qwen3-1.7B \
  --output_dir ./checkpoint_fp16 \
  --dtype float16

Validation

The checkpoint was validated by building a local engine and running inference on:

• —GPU: NVIDIA GeForce RTX 5070 Laptop GPU
• —Runtime: TensorRT-LLM 1.2.0rc6

Validated Local Engine Characteristics

Local build and runtime characteristics from the validated engine used for the benchmark snapshot below:

Important: the 1024 / 256 limits above belong only to this particular local engine build. They are not the intrinsic maximum context or generation limits of Qwen3-1.7B itself.

These values are specific to the local engine build used for validation and will change if you rebuild with different TensorRT-LLM settings and memory budgets.

Benchmark Snapshot

Local single-GPU measurements from the validated local engine on RTX 5070 Laptop GPU, using TensorRT-LLM synthetic fixed-length requests, 20 requests per profile, 2 warmup requests, and concurrency=1.

These numbers are local measurements from one machine and should be treated as reference values, not portability guarantees.

Quick Parity Check

A quick parity check was run on ARC-Challenge (20 examples) and OpenBookQA (20 examples) to verify that the TensorRT-LLM FP16 engine produces the same answers as the upstream Hugging Face model.

The TRT FP16 engine matches the HF baseline with 100% agreement on this subset.

Local Comparison

The table below compares locally validated TensorRT-LLM variants built for the same GPU family and the same local engine limits (max_batch_size=1, max_seq_len=1024, max_num_tokens=256).

This comparison is intentionally local and narrow. It should not be treated as a universal benchmark across all prompts, datasets, GPUs, or TensorRT-LLM versions.

Notes

• —This is not an official Qwen or NVIDIA release.
• —This repository does not include a prebuilt TensorRT engine.
• —Engine compatibility and performance depend on your GPU, driver, CUDA, TensorRT, and TensorRT-LLM versions.