Transformers now runs llama.cpp quants

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Transformers now runs llama.cpp quants

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HuggingFace

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https://huggingface.co/blog/transformers-llama-cpp-quants

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Back to Articles Transformers now runs llama.cpp quants Published September 22, 2026 Update on GitHub Upvote 32 +26 Marc Sun marcsun13 Follow Arthur Zucker ArthurZ Follow Lysandre lysandre Follow We're adding support for running GGUF models efficiently in transformers , so you can use checkpoints sized for your laptop's memory through the familiar transformers APIs. Pick a GGUF from the Hub, load it with from_pretrained , and start generating on your own machine. Running AI models on your laptop has become much easier, and llama.cpp has been a big part of that. Its inference engine powers local AI tools such as Ollama, LM Studio, and Jan. Alongside projects like MLX , it has helped make local inference a practical option for everyday use. A recent example of what local AI can feel like: This is where we are right now. And i’m not gonna lie it feels pretty magical 🧙‍♀️ Qwen3.6 27B running inside of Pi coding agent via Llama.cpp on the MacBook Pro For non-trivial tasks on the @huggingface codebases, this feels very, very close to hitting the latest Opus in Claude… pic.twitter.com/lsIxLoUneU — Julien Chaumond (@julien_c) April 24, 2026 GGUF , developed by the llama.cpp team, is a widely used format for local inference. The team also shares quantized checkpoints under ggml-org on the Hub . Publishers such as Unsloth , LM Studio Community , and bartowski also provide ready-to-use GGUF checkpoints in a range of quantizations, so users can pick the version that fits their machine. GGUF models have been downloaded millions of times. We want to make it easier to run these models locally with transformers, too. Compatibility is only useful if the model is pleasant to run. To bring performance close to llama.cpp, we're reusing its underlying ggml kernels through the kernels library, and reducing overhead in generate . Our initial focus is local inference on Apple Silicon, starting with the Qwen3.5 architecture. What is the GGUF file format? GGUF packages model weights and metadata, including tokenizer information and an optional chat template, in one file. It supports different quantization levels, letting you trade some precision for a smaller memory footprint. Variants such as Q4_K_M mix tensor precisions, using mostly 4-bit weights while keeping sensitive tensors at higher precision. Here's how quantization changes the file size of Unsloth's Qwen3.5-4B : GGUF variant File size Tradeoff BF16 8.42 GB Unquantized reference Q6_K 3.53 GB More precision than the smaller variants Q5_K_M 3.14 GB A middle ground between size and precision Q4_K_M 2.74 GB A practical starting point for local inference We suggest starting with Q4_K_M , then trying Q5_K_M or Q6_K if you have more memory available. More aggressive quantization can help larger models fit, but the quality tradeoff depends on the model and the task. Evaluate it on the work you actually want the model to do. The Hub's GGUF documentation describes the available quantization types. Load GGUF with transformers To get started, you need: An Apple Silicon Mac . A PyTorch version supported by the published ggml-quantization kernel builds , usually the two latest PyTorch releases. The latest version of transformers (main for now, until the next release) and a compatible version of kernels . pip install -U "git+https://github.com/huggingface/transformers.git" kernels To load a GGUF model, pass its Hub model_id and filename as gguf_file to from_pretrained . No extra configuration is needed: when the weights stay packed on Metal, transformers automatically loads the compatible ggml/Metal layer kernels and uses ggml-org/ggml-attn as the attention implementation. If that kernel cannot be fetched, the model falls back to "sdpa" with a warning, and you can always force "sdpa" by passing attn_implementation="sdpa" explicitly. See the GGUF documentation for more loading options. import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id = "unsloth/Qwen3.5-4B-GGUF" filename = "Qwen3.5-4B-Q4_K_M.gguf" tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename) model = AutoModelForCausalLM.from_pretrained( model_id, gguf_file=filename ) That is the only GGUF-specific step. Everything after it is the standard transformers API: messages = [{ "role" : "user" , "content" : "Explain why the sky is blue in a few sentences." }] inputs = tokenizer.apply_chat_template( messages, tokenize= True , add_generation_prompt= True , return_dict= True , return_tensors= "pt" , ).to(model.device) with torch.inference_mode(): outputs = model.generate(**inputs, max_new_tokens= 256 ) print (tokenizer.decode(outputs[ 0 ], skip_special_tokens= True )) Without a compatible quantization kernel, the loader falls back to dequantizing the model and uses more memory. Serve GGUF with your preferred interface You can also use the same checkpoint with transformers serve , which exposes an OpenAI-compatible API: pip install -U "transformers[serving] @ git+https://github.com/huggingface/transformers.git" kernels transformers serve "unsloth/Qwen3.5-4B-GGUF:Qwen3.5-4B-Q4_K_M.gguf" The model argument uses <model_id>:<filename>.gguf : before the colon is the Hub repository ( unsloth/Qwen3.5-4B-GGUF ), and after it is the file to load ( Qwen3.5-4B-Q4_K_M.gguf ). This selects a specific quantization from a repository that may contain several. For models whose chat template supports thinking, add --reasoning off to skip it or --reasoning on to enable it. The default, --reasoning auto , follows the chat template’s default. See the reasoning options for details. You can connect a client such as Jan or Pi by adding a custom OpenAI-compatible provider with these settings: Setting Value Base URL http://localhost:8000/v1 Model ID unsloth/Qwen3.5-4B-GGUF:Qwen3.5-4B-Q4_K_M.gguf transformers runs the model on your Mac, while the client provides the conversation interface. The same endpoint can be used by other clients that support this API. Benchmarking against llama.cpp Our reference for local inference performance is llama.cpp. The comparison below focuses on three GGUF checkpoints: a small dense model, a larger dense model, and a mixture-of-experts model. The llama.cpp column comes from the llama-bench tool (build 5f55650a7 , release b10200, Metal backend from ggml 0.18.0), run as llama-bench -m <file> -p 0 -n 128 -r 3 , which reports tg128 : the token-generation rate over 128 decoded tokens, averaged across three repetitions, with prompt processing excluded. The transformers column is generate producing the same 128 tokens from a 12-token prompt, best of three warmed runs, and it includes prefill. Measured on a MacBook Pro M2 Max, 32 GB unified memory, macOS 26.6, PyTorch 2.12.1, kernels 0.17.0, plugged in. The benchmark script import time import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_id, filename = "unsloth/Qwen3.5-4B-GGUF" , "Qwen3.5-4B-Q4_K_M.gguf" model = AutoModelForCausalLM.from_pretrained(model_id, gguf_file=filename) tokenizer = AutoTokenizer.from_pretrained(model_id, gguf_file=filename) inputs = tokenizer( "The capital of France is Paris. The capital of Germany is" , return_tensors= "pt" ) inputs = inputs.to(model.device) with torch.inference_mode(): model.generate(**inputs, max_new_tokens= 8 , min_new_tokens= 8 , do_sample= False ) # warm up torch.mps.synchronize() for _ in range ( 3 ): time.sleep( 90 ) # let the machine cool: back-to-back runs decay by 10% or more start = time.perf_counter() model.generate(**inputs, max_new_tokens= 128 , min_new_tokens= 128 , do_sample= False ) torch.mps.synchronize() print ( f" { 128 / (time.perf_counter() - start): .1 f} tok/s" ) For the other column: llama-bench -hf unsloth/Qwen3.5-4B-GGUF:Q4_K_M -p 0 -n 128 -r 3 Transformers is close to llama.cpp across all three checkpoints.