# tuned-SmolLM2-135M-Instruct-test-s by Zied Ben hadj Amor Source: https://savrn.com/models/tuned-smollm2-135m-instruct-test-s Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve tuned-SmolLM2-135M-Instruct-test-s (135M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.3 GB | 0.3 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 9, 2026. [tuned-SmolLM2-135M-Instruct-test-s on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/tuned-smollm2-135m-instruct-test-s/gpus) ## Model Card By Zied Ben hadj Amor, published under apache-2.0, revision e4c37d7e5765. This model is a fine-tuned version of HuggingFaceTB/SmolLM2-135M-Instruct on an unknown dataset. The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 1 - evalbatchsize: 1 - gradientaccumulationsteps: 4 - totaltrainbatchsize: 4 - lrschedulertype: linear - numepochs: 1 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 Read Zied Ben hadj Amor's full model card This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M-Instruct](https://savrn.com/models/smollm2-135m-instruct) on an unknown dataset. ### Model description More information needed ### Intended uses & limitations More information needed ### Training and evaluation data More information needed ### Training procedure #### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 1 #### Training results | Training Loss | Epoch | Step | Validation Loss | | --- | --- | --- | --- | | No log | 1.0 | 45 | 1.2672 | #### Framework versions - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 4.8.5 - Tokenizers 0.23.2 ## Configuration Architecture LlamaForCausalLM Context length (tokens) 8,192 Layers 30 Hidden size 576 Feed-forward size 1,536 Attention heads 9 Key/value heads 3 Head dimension 64 Vocabulary size 49,152 Model type llama ## Identity and Version Repository mrbhazied/tuned-SmolLM2-135M-Instruct-test-s Publisher Zied Ben hadj Amor Task Text generation Modality Text Library transformers Parameters 135M parameters Languages Not stated by the source Revision e4c37d7e57656840a22a24efe0e856811033ba51 First published 2026-10-03 Last updated 2026-10-04 ## Files and Weights 9 files, 272.6 MB in total. The weights are 2 files totalling 269.1 MB in bin, safetensors. Weights2 files · 269.1 MB Configuration2 files · 1.0 KB Tokenizer2 files · 3.5 MB Documentation1 file · 1.5 KB Other1 file · 368 B Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 269.1 MB | c2875c071901 | | training_args.bin | Weights | 5.3 KB | 665a3068d3e7 | | config.json | Configuration | 907 B | — | | generation_config.json | Configuration | 142 B | — | | README.md | Documentation | 1.5 KB | — | | chat_template.jinja | Other | 368 B | — | | .gitattributes | Repository | 1.5 KB | — | | tokenizer.json | Tokenizer | 3.5 MB | — | | tokenizer_config.json | Tokenizer | 453 B | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 269.1 MB [Download from Zied Ben hadj Amor](https://huggingface.co/mrbhazied/tuned-SmolLM2-135M-Instruct-test-s) Released by Zied Ben hadj Amor through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [HuggingFaceTB/SmolLM2-135M-Instruct](https://savrn.com/models/smollm2-135m-instruct) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 269.1 MB | | 16-bit | 0.3 GB | | 8-bit | 0.1 GB | | 4-bit | 0.1 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About tuned-SmolLM2-135M-Instruct-test-s ### How much GPU memory does tuned-SmolLM2-135M-Instruct-test-s need? About 0.3 GB at 16-bit and 0.1 GB at 4-bit: the weights (135M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run tuned-SmolLM2-135M-Instruct-test-s on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use tuned-SmolLM2-135M-Instruct-test-s commercially? Yes. tuned-SmolLM2-135M-Instruct-test-s is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ### What is tuned-SmolLM2-135M-Instruct-test-s's context length? 8,192 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text generation ### [SmolLM2-135M](https://savrn.com/models/smollm2-135m) [Hugging Face Smol Models Research](https://savrn.com/model-publishers/huggingfacetb) SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. They are capable of solving a wide range of tasks while being lightweight enough to run on-device. More details in our paper: https://arxiv.org/abs/2502.02737 SmolLM2 demonstrates significant advances over its predecessor SmolLM1, particularly in instruction following, knowledge, reasoning. The 135M model was trained on 2 trillion tokens using a diverse dataset combination: FineWeb-Edu, DCLM, The Stack, along with new filtered datasets we curated and will release soon. We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our… Open weights apache-2.0 135M parameters 8,192 tokens transformers [View model](https://savrn.com/models/smollm2-135m) Model · Text generation ### [SmolLM2-135M-Instruct](https://savrn.com/models/smollm2-135m-instruct) [Hugging Face Smol Models Research](https://savrn.com/model-publishers/huggingfacetb) SmolLM2 is a family of compact language models available in three size: 135M, 360M, and 1.7B parameters. 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We developed the instruct version through supervised fine-tuning (SFT) using a combination of public datasets and our own… Open weights apache-2.0 135M parameters 8,192 tokens transformers [View model](https://savrn.com/models/smollm2-135m-instruct) Model · Text generation ### [gpt2](https://savrn.com/models/gpt2) [OpenAI community](https://savrn.com/model-publishers/openai-community) Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in and first released at this page. model. Content from this model card has been written by the Hugging Face team to complete the information they provided and give specific examples of bias. GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. 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The same models re-evaluated on FLORES+ devtest (released 2026; zero overlap with dev): devtest sentences do not overlap with dev.… Open weights apache-2.0 143M parameters 4,096 tokens [View model](https://savrn.com/models/haidass-translate-143m) ## Zied Ben hadj Amor [All models and datasets](https://savrn.com/model-publishers/mrbhazied) ## Versions - [e4c37d7e5765](https://savrn.com/models/tuned-smollm2-135m-instruct-test-s/versions/e4c37d7e5765) · current 2026-10-04 - [a96501db5ee8](https://savrn.com/models/tuned-smollm2-135m-instruct-test-s/versions/a96501db5ee8) 2026-10-03 ## Explore More - [All text generation models](https://savrn.com/models/tasks/text-generation) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-04. - [Hugging Face record](https://huggingface.co/mrbhazied/tuned-SmolLM2-135M-Instruct-test-s) - [How the hub is built](https://savrn.com/model-hub/methodology)