Model reference · open weights
SmolLM is an open-weight language model from HuggingFaceTB, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.
About
SmolLM Table of Contents 1. Model Summary 2. Limitations 3. Training 4. License 5. Citation Model Summary SmolLM is a series of state-of-the-art small language models available in three sizes: 135M, 360M, and 1.7B parameters. These models are built on Cosmo-Corpus, a meticulously curated high-quality training dataset. Cosmo-Corpus includes Cosmopedia v2 (28B tokens of synthetic textbooks and stories generated by Mixtral), Python-Edu (4B tokens of educational Python samples from The Stack), and FineWeb-Edu (220B tokens of deduplicated educational web samples from FineWeb). SmolLM models have shown promising results when compared to other models in their size categories across various benchmarks testing common sense reasoning and world knowledge. For detailed information on training, benchmarks and performance, please refer to our full blog post. This is the SmolLM-135M Generation Running the model on CPU/GPU/multi GPU Using full precision Using torch.bfloat16 Quantized Versions through bitsandbytes Using 8-bit precision (int8) Limitations While SmolLM models have been trained on a diverse dataset including educational content and synthetic texts, they have limitations. The models primarily understand and generate content in English. They can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content. For a more comprehensive discussion of the models' capabilities and limitations, please refer to our full blog post.. This repository contains a converted version of our latest trained model. We've noticed a small performance difference between this converted checkpoint (transformers) and the original (nanotron). We're currently working to resolve this issue. Training Model - Architecture: For architecture detail, see the blog post. - Pretraining steps: 600k - Pretraining tokens: 600B - Precision: bfloat16 - Tokenizer: HuggingFaceTB/cosmo2-tokenizer Hardware - GPUs: 64
Summarised from the published model card. Read the full card on the HuggingFace links below.
Specifications
| Maker | HuggingFaceTB |
|---|---|
| Type | Language models |
| Parameters (lead) | 135M |
| Context | 2k tokens |
| Variants | 3 |
| Runs with | transformers |
| Released | 2024-07-14 |
| Popularity | 151k downloads / month |
| Likes | 267 |
| Licence | Open weights |
How it works
Variants
Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.
Using it via the API
Once AxForge deploys smollm for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (smollm below is illustrative; you get the exact model name on deployment.)
$ curl -sS https://api.axforge.ai/v1/chat/completions \
-H "Authorization: Bearer $AXFORGE_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"smollm","messages":[{"role":"user","content":"Hello"}]}'
Details
Languages
Trained / evaluated on
Tags
Licence
Open weights under apache-2.0 — commercial use is permitted. Deploy it on AxForge EU hardware on request. Read the licence ↗