From 416b3e29fc94c560d4b1e15d6efb18c63ee77662 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Tue, 5 May 2026 17:11:45 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: HuggingFaceTB/SmolLM3-3B Source: Original Platform --- .gitattributes | 49 + README.md | 392 ++ chat_template.jinja | 94 + config.json | 109 + configuration.json | 1 + generation_config.json | 9 + model-00001-of-00002.safetensors | 3 + model-00002-of-00002.safetensors | 3 + model.safetensors.index.json | 334 ++ notebook.ipynb | 8586 ++++++++++++++++++++++++++++++ special_tokens_map.json | 16 + tokenizer.json | 3 + tokenizer_config.json | 2064 +++++++ 13 files changed, 11663 insertions(+) create mode 100644 .gitattributes create mode 100644 README.md create mode 100644 chat_template.jinja create mode 100644 config.json create mode 100644 configuration.json create mode 100644 generation_config.json create mode 100644 model-00001-of-00002.safetensors create mode 100644 model-00002-of-00002.safetensors create mode 100644 model.safetensors.index.json create mode 100644 notebook.ipynb create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..21b3632 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*.tfevents* filter=lfs diff=lfs merge=lfs -text +*.db* filter=lfs diff=lfs merge=lfs -text +*.ark* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*data* filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.meta filter=lfs diff=lfs merge=lfs -text +**/*ckpt*.index filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.gguf* filter=lfs diff=lfs merge=lfs -text +*.ggml filter=lfs diff=lfs merge=lfs -text +*.llamafile* filter=lfs diff=lfs merge=lfs -text +*.pt2 filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text + +tokenizer.json filter=lfs diff=lfs merge=lfs -text \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..4877f42 --- /dev/null +++ b/README.md @@ -0,0 +1,392 @@ +--- +library_name: transformers +license: apache-2.0 +language: +- en +- fr +- es +- it +- pt +- zh +- ar +- ru +base_model: + - HuggingFaceTB/SmolLM3-3B-Base +--- + + +# SmolLM3 + + +![image/png](https://cdn-uploads.huggingface.co/production/uploads/61c141342aac764ce1654e43/zy0dqTCCt5IHmuzwoqtJ9.png) + + +## Table of Contents + +1. [Model Summary](#model-summary) +2. [How to use](#how-to-use) +3. [Evaluation](#evaluation) +4. [Training](#training) +5. [Limitations](#limitations) +6. [License](#license) + +## Model Summary + +SmolLM3 is a 3B parameter language model designed to push the boundaries of small models. It supports dual mode reasoning, 6 languages and long context. SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale. + +![image/png](https://cdn-uploads.huggingface.co/production/uploads/6200d0a443eb0913fa2df7cc/db3az7eGzs-Sb-8yUj-ff.png) + +The model is a decoder-only transformer using GQA and NoPE (with 3:1 ratio), it was pretrained on 11.2T tokens with a staged curriculum of web, code, math and reasoning data. Post-training included midtraining on 140B reasoning tokens followed by supervised fine-tuning and alignment via Anchored Preference Optimization (APO). + +### Key features +- Instruct model optimized for **hybrid reasoning** +- **Fully open model**: open weights + full training details including public data mixture and training configs +- **Long context:** Trained on 64k context and supports up to **128k tokens** using YARN extrapolation +- **Multilingual**: 6 natively supported (English, French, Spanish, German, Italian, and Portuguese) + +For more details refer to our blog post: https://hf.co/blog/smollm3 + +## How to use + +The modeling code for SmolLM3 is available in transformers `v4.53.0`, so make sure to upgrade your transformers version. You can also load the model with the latest `vllm` which uses transformers as a backend. +```bash +pip install -U transformers +``` + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "HuggingFaceTB/SmolLM3-3B" +device = "cuda" # for GPU usage or "cpu" for CPU usage + +# load the tokenizer and the model +tokenizer = AutoTokenizer.from_pretrained(model_name) +model = AutoModelForCausalLM.from_pretrained( + model_name, +).to(device) + +# prepare the model input +prompt = "Give me a brief explanation of gravity in simple terms." +messages_think = [ + {"role": "user", "content": prompt} +] + +text = tokenizer.apply_chat_template( + messages_think, + tokenize=False, + add_generation_prompt=True, +) +model_inputs = tokenizer([text], return_tensors="pt").to(model.device) + +# Generate the output +generated_ids = model.generate(**model_inputs, max_new_tokens=32768) + +# Get and decode the output +output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :] +print(tokenizer.decode(output_ids, skip_special_tokens=True)) +``` + +>[!TIP] +> We recommend setting `temperature=0.6` and `top_p=0.95` in the sampling parameters. + +### Long context processing + +The current `config.json` is set for context length up to 65,536 tokens. To handle longer inputs (128k or 256k), we utilize YaRN you can change the `max_position_embeddings` and rope_scaling` to: +``` +{ + ..., + "rope_scaling": { + "factor": 2.0, #2x65536=131 072 + "original_max_position_embeddings": 65536, + "type": "yarn" + } +} +``` + + +### Enabling and Disabling Extended Thinking Mode + +We enable extended thinking by default, so the example above generates the output with a reasoning trace. For choosing between enabling, you can provide the `/think` and `/no_think` flags through the system prompt as shown in the snippet below for extended thinking disabled. The code for generating the response with extended thinking would be the same except that the system prompt should have `/think` instead of `/no_think`. + +```python +prompt = "Give me a brief explanation of gravity in simple terms." +messages = [ + {"role": "system", "content": "/no_think"}, + {"role": "user", "content": prompt} +] + +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, +) +``` + +We also provide the option of specifying the whether to use extended thinking through the `enable_thinking` kwarg as in the example below. You do not need to set the `/no_think` or `/think` flags through the system prompt if using the kwarg, but keep in mind that the flag in the system prompt overwrites the setting in the kwarg. + +```python +prompt = "Give me a brief explanation of gravity in simple terms." +messages = [ + {"role": "user", "content": prompt} +] + +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + enable_thinking=False +) +``` + +### Agentic Usage + +SmolLM3 supports tool calling! +Just pass your list of tools: +- Under the argument `xml_tools` for standard tool-calling: these tools will be called as JSON blobs within XML tags, like `{"name": "get_weather", "arguments": {"city": "Copenhagen"}}` +- Or under `python_tools`: then the model will call tools like python functions in a `` snippet, like `get_weather(city="Copenhagen")` + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer + +checkpoint = "HuggingFaceTB/SmolLM3-3B" + +tokenizer = AutoTokenizer.from_pretrained(checkpoint) +model = AutoModelForCausalLM.from_pretrained(checkpoint) + +tools = [ + { + "name": "get_weather", + "description": "Get the weather in a city", + "parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "The city to get the weather for"}}}} +] + +messages = [ + { + "role": "user", + "content": "Hello! How is the weather today in Copenhagen?" + } +] + +inputs = tokenizer.apply_chat_template( + messages, + enable_thinking=False, # True works as well, your choice! + xml_tools=tools, + add_generation_prompt=True, + tokenize=True, + return_tensors="pt" +) + +outputs = model.generate(inputs) +print(tokenizer.decode(outputs[0])) +``` + +### Using Custom System Instructions. + +You can specify custom instruction through the system prompt while controlling whether to use extended thinking. For example, the snippet below shows how to make the model speak like a pirate while enabling extended thinking. + +```python +prompt = "Give me a brief explanation of gravity in simple terms." +messages = [ + {"role": "system", "content": "Speak like a pirate./think"}, + {"role": "user", "content": prompt} +] + +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, +) +``` + +For local inference, you can use `llama.cpp`, `ONNX`, `MLX`, `MLC` and `ExecuTorch`. You can find quantized checkpoints in this collection (https://huggingface.co/collections/HuggingFaceTB/smollm3-686d33c1fdffe8e635317e23) + +### vLLM and SGLang + +You can use vLLM and SGLang to deploy the model in an API compatible with OpenAI format. + +#### SGLang + +```bash +python -m sglang.launch_server --model-path HuggingFaceTB/SmolLM3-3B +``` + +#### vLLM + +```bash +vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes +``` + +#### Setting `chat_template_kwargs` + +You can specify `chat_template_kwargs` such as `enable_thinking` to a deployed model by passing the `chat_template_kwargs` parameter in the API request. + +```bash +curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{ + "model": "HuggingFaceTB/SmolLM3-3B", + "messages": [ + {"role": "user", "content": "Give me a brief explanation of gravity in simple terms."} + ], + "temperature": 0.6, + "top_p": 0.95, + "max_tokens": 16384, + "chat_template_kwargs": {"enable_thinking": false} +}' +``` + +## Evaluation + +In this section, we report the evaluation results of SmolLM3 model. All evaluations are zero-shot unless stated otherwise, and we use [lighteval](https://github.com/huggingface/lighteval) to run them. + +We highlight the best score in bold and underline the second-best score. + +### Instruction Model + +#### No Extended Thinking +Evaluation results of non reasoning models and reasoning models in no thinking mode. We highlight the best and second-best scores in bold. +| Category | Metric | SmoLLM3-3B | Qwen2.5-3B | Llama3.1-3B | Qwen3-1.7B | Qwen3-4B | +|---------|--------|------------|------------|-------------|------------|----------| +| High school math competition | AIME 2025 | 9.3 | 2.9 | 0.3 | 8.0 | **17.1** | +| Math problem-solving | GSM-Plus | 72.8 | 74.1 | 59.2 | 68.3 | **82.1** | +| Competitive programming | LiveCodeBench v4 | 15.2 | 10.5 | 3.4 | 15.0 | **24.9** | +| Graduate-level reasoning | GPQA Diamond | 35.7 | 32.2 | 29.4 | 31.8 | **44.4** | +| Instruction following | IFEval | **76.7** | 65.6 | 71.6 | 74.0 | 68.9 | +| Alignment | MixEval Hard | 26.9 | 27.6 | 24.9 | 24.3 | **31.6** | +| Tool Calling | BFCL| 92.3 | - | 92.3 * | 89.5 | **95.0** | +| Multilingual Q&A | Global MMLU | 53.5 | 50.54 | 46.8 | 49.5 | **65.1** | + +(*): this is a tool calling finetune + +#### Extended Thinking +Evaluation results in reasoning mode for SmolLM3 and Qwen3 models: +| Category | Metric | SmoLLM3-3B | Qwen3-1.7B | Qwen3-4B | +|---------|--------|------------|------------|----------| +| High school math competition | AIME 2025 | 36.7 | 30.7 | **58.8** | +| Math problem-solving | GSM-Plus | 83.4 | 79.4 | **88.2** | +| Competitive programming | LiveCodeBench v4 | 30.0 | 34.4 | **52.9** | +| Graduate-level reasoning | GPQA Diamond | 41.7 | 39.9 | **55.3** | +| Instruction following | IFEval | 71.2 | 74.2 | **85.4** | +| Alignment | MixEval Hard | 30.8 | 33.9 | **38.0** | +| Tool Calling | BFCL | 88.8 | 88.8 | **95.5** | +| Multilingual Q&A | Global MMLU | 64.1 | 62.3 | **73.3** | + + +### Base Pre-Trained Model + +#### English benchmarks +Note: All evaluations are zero-shot unless stated otherwise. For Ruler 64k evaluation, we apply YaRN to the Qwen models with 32k context to extrapolate the context length. + +| Category | Metric | SmolLM3-3B | Qwen2.5-3B | Llama3-3.2B | Qwen3-1.7B-Base | Qwen3-4B-Base | +|---------|--------|---------------------|------------|--------------|------------------|---------------| +| Reasoning & Commonsense| HellaSwag | **76.15** | 74.19 |75.52 | 60.52 | 74.37 | +| | ARC-CF (Average) | **65.61** | 59.81 | 58.58 | 55.88 | 62.11 | +| | Winogrande | 58.88 | **61.41** | 58.72 | 57.06 | 59.59 | +| | CommonsenseQA | 55.28 | 49.14 | **60.60** | 48.98 | 52.99 | +| Knowledge & Understanding | MMLU-CF (Average) | 44.13 | 42.93 | 41.32 | 39.11 | **47.65** | +| | MMLU Pro CF | 19.61 | 16.66 | 16.42 | 18.04 | **24.92** | +| | MMLU Pro MCF | 32.70 | 31.32 | 25.07 | 30.39 | **41.07** | +| | PIQA | **78.89** | 78.35 | 78.51 | 75.35 | 77.58 | +| | OpenBookQA | 40.60 | 40.20 | 42.00 | 36.40 | **42.40** | +| | BoolQ | **78.99** | 73.61 | 75.33 | 74.46 | 74.28 | +| **Math & Code** | | | | | | | +| Coding & math | HumanEval+ | 30.48 | 34.14| 25.00 | 43.29| **54.87** | +| | MBPP+ | 52.91 | 52.11 | 38.88| 59.25 | **63.75** | +| | MATH (4-shot) | 46.10 | 40.10 | 7.44 | 41.64 | **51.20** | +| | GSM8k (5-shot) | 67.63 | 70.13 | 25.92 | 65.88 | **74.14** | +| **Long context** | | | | | | | +| | Ruler 32k | 76.35 | 75.93 | 77.58 | 70.63 | **83.98** | +| | Ruler 64k | 67.85 | 64.90 | **72.93** | 57.18 | 60.29 | +| | Ruler 128k | 61.03 | 62.23 | **71.30** | 43.03 | 47.23 | + +#### Multilingual benchmarks + + +| Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base | +|---------|--------|---------------------|------------|--------------|------------------|---------------| +| Main supported languages | | | | | | | | +| French| MLMM Hellaswag | **63.94** | 57.47 | 57.66 | 51.26 | 61.00 | +| | Belebele | 51.00 | 51.55 | 49.22 |49.44| **55.00** | +| | Global MMLU (CF) | 38.37 | 34.22 | 33.71 | 34.94 |**41.80** | +| | Flores-200 (5-shot) | 62.85| 61.38| 62.89 | 58.68 | **65.76** | +| Spanish| MLMM Hellaswag | **65.85** | 58.25 | 59.39 | 52.40 | 61.85 | +| | Belebele | 47.00 | 48.88 | 47.00 | 47.56 | **50.33** | +| | Global MMLU (CF) | 38.51 | 35.84 | 35.60 | 34.79 |**41.22** | +| | Flores-200 (5-shot) | 48.25| 50.00| 44.45 | 46.93 | **50.16** | +| German| MLMM Hellaswag | **59.56** | 49.99| 53.19|46.10| 56.43| +| | Belebele | 48.44 | 47.88 | 46.22 | 48.00 | **53.44**| +| | Global MMLU (CF) | 35.10 | 33.19 | 32.60 | 32.73 |**38.70** | +| | Flores-200 (5-shot) | **56.60**| 50.63| 54.95 | 52.58 | 50.48 | +| Italian| MLMM Hellaswag | **62.49** | 53.21 | 54.96 | 48.72 | 58.76 | +| | Belebele | 46.44 | 44.77 | 43.88 | 44.00 | **48.78** | 44.88 | +| | Global MMLU (CF) | 36.99 | 33.91 | 32.79 | 35.37 |**39.26** | +| | Flores-200 (5-shot) | 52.65| **54.87**| 48.83 | 48.37 | 49.11 | +| Portuguese| MLMM Hellaswag | **63.22** | 57.38 | 56.84 | 50.73 | 59.89 | +| | Belebele | 47.67 | **49.22** | 45.00 | 44.00 | 50.00 | 49.00 | +| | Global MMLU (CF) | 36.88 | 34.72 | 33.05 | 35.26 |**40.66** | +| | Flores-200 (5-shot) | 60.93 |57.68| 54.28 | 56.58 | **63.43** | + +The model has also been trained on Arabic (standard), Chinese and Russian data, but has seen fewer tokens in these languages compared to the 6 above. We report the performance on these langages for information. +| Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base | +|---------|--------|---------------------|------------|--------------|------------------|---------------| +| Other supported languages | | | | | | | | +| Arabic| Belebele | 40.22 | 44.22 | 45.33 | 42.33 | **51.78** | +| | Global MMLU (CF) | 28.57 | 28.81 | 27.67 | 29.37 | **31.85** | +| | Flores-200 (5-shot) | 40.22 | 39.44 | **44.43** | 35.82 | 39.76 | +| Chinese| Belebele | 43.78 | 44.56 | 49.56 | 48.78 | **53.22** | +| | Global MMLU (CF) | 36.16 | 33.79 | 39.57 | 38.56 | **44.55** | +| | Flores-200 (5-shot) | 29.17 | **33.21** | 31.89 | 25.70 | 32.50 | +| Russian| Belebele | 47.44 | 45.89 | 47.44 | 45.22 | **51.44** | +| | Global MMLU (CF) | 36.51 | 32.47 | 34.52 | 34.83 | **38.80** | +| | Flores-200 (5-shot) | 47.13 | 48.74 | 50.74 | 54.70 | **60.53** | + +## Training + +### Model + +- **Architecture:** Transformer decoder +- **Pretraining tokens:** 11T +- **Precision:** bfloat16 + +### Software & hardware + +- **GPUs:** 384 H100 +- **Training Framework:** [nanotron](https://github.com/huggingface/nanotron/tree/smollm3) +- **Data processing framework:** [datatrove](https://github.com/huggingface/datatrove) +- **Evaluation framework:** [lighteval](https://github.com/huggingface/lighteval) +- **Post-training Framework:** [TRL](https://github.com/huggingface/trl) + +### Open resources +Here is an infographic with all the training details +- The datasets used for pretraining can be found in this [collection](https://huggingface.co/collections/HuggingFaceTB/smollm3-pretraining-datasets-685a7353fdc01aecde51b1d9) and those used in mid-training and post-training will be uploaded later +- The training and evaluation configs and code can be found in the [huggingface/smollm](https://github.com/huggingface/smollm) repository. +- The training intermediate checkpoints (including the mid-training and SFT checkpoints) are available at [HuggingFaceTB/SmolLM3-3B-checkpoints](https://huggingface.co/HuggingFaceTB/SmolLM3-3B-checkpoints) +![image/png](https://cdn-uploads.huggingface.co/production/uploads/651e96991b97c9f33d26bde6/qiE5ZYr9SD1CIAtfEfuC8.png) + +### EU Summary of Public Content + +The EU AI Act requires all GPAI models to provide a Public Summary of Training Content according to a [given template](https://digital-strategy.ec.europa.eu/en/library/explanatory-notice-and-template-public-summary-training-content-general-purpose-ai-models). +You can find the summary for this model below, as well as in its [development Space](https://huggingface.co/spaces/hfmlsoc/smollm3-eu-data-transparency). + + + + +## Limitations + +SmolLM3 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. + +## License +[Apache 2.0](https://www.apache.org/licenses/LICENSE-2.0) + +## Citation +```bash +@misc{bakouch2025smollm3, + title={{SmolLM3: smol, multilingual, long-context reasoner}}, + author={Bakouch, Elie and Ben Allal, Loubna and Lozhkov, Anton and Tazi, Nouamane and Tunstall, Lewis and Patiño, Carlos Miguel and Beeching, Edward and Roucher, Aymeric and Reedi, Aksel Joonas and Gallouédec, Quentin and Rasul, Kashif and Habib, Nathan and Fourrier, Clémentine and Kydlicek, Hynek and Penedo, Guilherme and Larcher, Hugo and Morlon, Mathieu and Srivastav, Vaibhav and Lochner, Joshua and Nguyen, Xuan-Son and Raffel, Colin and von Werra, Leandro and Wolf, Thomas}, + year={2025}, + howpublished={\url{https://huggingface.co/blog/smollm3}} +} +``` \ No newline at end of file diff --git a/chat_template.jinja b/chat_template.jinja new file mode 100644 index 0000000..e01e3a1 --- /dev/null +++ b/chat_template.jinja @@ -0,0 +1,94 @@ +{# ───── defaults ───── #} +{%- if enable_thinking is not defined -%} +{%- set enable_thinking = true -%} +{%- endif -%} + +{# ───── reasoning mode ───── #} +{%- if enable_thinking -%} + {%- set reasoning_mode = "/think" -%} +{%- else -%} + {%- set reasoning_mode = "/no_think" -%} +{%- endif -%} + +{# ───── header (system message) ───── #} +{{- "<|im_start|>system\n" -}} + +{%- if messages[0].role == "system" -%} + {%- set system_message = messages[0].content -%} + {%- if "/no_think" in system_message -%} + {%- set reasoning_mode = "/no_think" -%} + {%- elif "/think" in system_message -%} + {%- set reasoning_mode = "/think" -%} + {%- endif -%} + {%- set custom_instructions = system_message.replace("/no_think", "").replace("/think", "").rstrip() -%} +{%- endif -%} + +{%- if "/system_override" in system_message -%} + {{- custom_instructions.replace("/system_override", "").rstrip() -}} + {{- "<|im_end|>\n" -}} +{%- else -%} + {{- "## Metadata\n\n" -}} + {{- "Knowledge Cutoff Date: June 2025\n" -}} + {%- set today = strftime_now("%d %B %Y") -%} + {{- "Today Date: " ~ today ~ "\n" -}} + {{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}} + + {{- "## Custom Instructions\n\n" -}} + {%- if custom_instructions -%} + {{- custom_instructions + "\n\n" -}} + {%- elif reasoning_mode == "/think" -%} + {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: Thought section Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}} + {%- else -%} + {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}} + {%- endif -%} + + {%- if xml_tools or python_tools or tools -%} + {{- "### Tools\n\n" -}} + {%- if xml_tools or tools -%} + {%- if tools -%} + {%- set xml_tools = tools -%} + {%- endif -%} + {%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within XML tags:\n\n\n") -%} + {%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #} + {%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%} + {%- endfor -%} + {%- set xml_tool_string = ns.xml_tool_string + "\n\nFor each function call, return a json object with function name and arguments within XML tags:\n\n{\"name\": , \"arguments\": }\n" -%} + {{- xml_tool_string -}} + {%- endif -%} + {%- if python_tools -%} + {%- set ns = namespace(python_tool_string="When you send a message containing Python code between '' and '' tags, it will be executed in a stateful Jupyter notebook environment, and you will then be given the output to continued reasoning in an agentic loop.\n\nYou can use the following tools in your python code like regular functions:\n\n") -%} + {%- for tool in python_tools[:] -%} {# The slicing makes sure that python_tools is a list #} + {%- set ns.python_tool_string = ns.python_tool_string ~ (tool | string) ~ "\n" -%} + {%- endfor -%} + {%- set python_tool_string = ns.python_tool_string + "\n\nThe state persists between code executions: so variables that you define in one step are still available thereafter." -%} + {{- python_tool_string -}} + {%- endif -%} + {{- "\n\n" -}} + {{- "<|im_end|>\n" -}} + {%- endif -%} +{%- endif -%} +{# ───── main loop ───── #} +{%- for message in messages -%} + {%- set content = message.content if message.content is string else "" -%} + {%- if message.role == "user" -%} + {{ "<|im_start|>" + message.role + "\n" + content + "<|im_end|>\n" }} + {%- elif message.role == "assistant" -%} + {% generation %} + {%- if reasoning_mode == "/think" -%} + {{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }} + {%- else -%} + {{ "<|im_start|>assistant\n" + "\n\n\n" + content.lstrip("\n") + "<|im_end|>\n" }} + {%- endif -%} + {% endgeneration %} + {%- elif message.role == "tool" -%} + {{ "<|im_start|>" + "user\n" + content + "<|im_end|>\n" }} + {%- endif -%} +{%- endfor -%} +{# ───── generation prompt ───── #} +{%- if add_generation_prompt -%} + {%- if reasoning_mode == "/think" -%} + {{ "<|im_start|>assistant\n" }} + {%- else -%} + {{ "<|im_start|>assistant\n" + "\n\n\n" }} + {%- endif -%} +{%- endif -%} \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..89ad848 --- /dev/null +++ b/config.json @@ -0,0 +1,109 @@ +{ + "architectures": [ + "SmolLM3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "bos_token_id": 128000, + "eos_token_id": 128012, + "hidden_act": "silu", + "hidden_size": 2048, + "initializer_range": 0.02, + "intermediate_size": 11008, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 65536, + "max_window_layers": 28, + "mlp_bias": false, + "model_type": "smollm3", + "no_rope_layer_interval": 4, + "no_rope_layers": [ + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 0, + 1, + 1, + 1, + 0 + ], + "num_attention_heads": 16, + "num_hidden_layers": 36, + "num_key_value_heads": 4, + "pad_token_id": 128004, + "pretraining_tp": 2, + "rms_norm_eps": 1e-06, + "rope_scaling": null, + "rope_theta": 5000000.0, + "sliding_window": null, + "tie_word_embeddings": true, + "torch_dtype": "bfloat16", + "transformers_version": "4.54.0.dev0", + "use_cache": false, + "use_sliding_window": false, + "vocab_size": 128256 +} diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..bbeeda1 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "text-generation", "allow_remote": true} \ No newline at end of file diff --git a/generation_config.json b/generation_config.json new file mode 100644 index 0000000..5339609 --- /dev/null +++ b/generation_config.json @@ -0,0 +1,9 @@ +{ + "bos_token_id": 128000, + "eos_token_id": 128012, + "pad_token_id": 128004, + "transformers_version": "4.54.0.dev0", + "temperature": 0.6, + "top_p": 0.95, + "do_sample": true +} diff --git a/model-00001-of-00002.safetensors b/model-00001-of-00002.safetensors new file mode 100644 index 0000000..1fff558 --- /dev/null +++ b/model-00001-of-00002.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e5eed4113a925264c33e3cdf76cd9f547c06a207ef2270914a7274a2bf685ffd +size 4966315264 diff --git a/model-00002-of-00002.safetensors b/model-00002-of-00002.safetensors new file mode 100644 index 0000000..2483faf --- /dev/null +++ b/model-00002-of-00002.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid 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"base_uri": "https://localhost:8080/" + } + }, + "execution_count": 1, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.9/40.9 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.8/10.8 MB\u001b[0m \u001b[31m95.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Local Inference on GPU\n", + "Model page: https://huggingface.co/HuggingFaceTB/SmolLM3-3B\n", + "\n" + ], + "metadata": { + "id": "oVFMyJQO3AHw" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Using pipeline" + ], + "metadata": { + "id": "oK42H4Gb-T7B" + } + }, + { + "cell_type": "code", + "source": [ + "# Use a pipeline as a high-level helper\n", + "from transformers import pipeline\n", + "from transformers import AutoTokenizer\n", + "\n", + "model_id = \"HuggingFaceTB/SmolLM3-3B\"\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(model_id)\n", + "\n", + "pipe = pipeline(\"text-generation\", model=model_id, tokenizer=tokenizer)" + ], + "metadata": { + "id": "onUJj3B_3AHx", + "outputId": "5c089143-41f6-46d2-c41e-79e2cc590cc3", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 387, + "referenced_widgets": [ + "916e5b2a5315478b93a3eeb58e6e39fb", + "d82a890a7a7942ccb3386a420f15a226", + "0b43ee3e35324cd3b158dc76d52992eb", + "bc9ac498ed284898bf0f6cf7350491b5", + "7b97096ac5a84719a9037e198a2de115", + "8f2eb544f0b94031ae7954e72b2c4664", + "7e78e738d0cd4f10aa499df5f209b7bb", + "1a0daa0300b84460b43ff4ef7b13f580", + "6c99cf07e3724347b0c96dd251eb2d81", + "fdc780b0d43844c4a6301298c8d643e3", + "a56eb33bbf524c59b684de11182ff800", + "bc832b58f4b847bba247714934a4aee3", + "d89e14ff9b934a8db4991f2283ab1b89", + "0d117d61df204342964cfe0549bae9d8", + "ff5734104386496db9b1eee49bf08bc8", + 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"a2bd0c1e1e134e969eae073b76d6d2cc", + "0aaf44d292ba4821a7b0943c33d39ff1" + ] + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0.00B [00:00, ?B/s]" + ], + "application/vnd.jupyter.widget-view+json": { + "version_major": 2, + "version_minor": 0, + "model_id": "916e5b2a5315478b93a3eeb58e6e39fb" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer.json: 0%| | 0.00/17.2M [00:00\\nOkay, I need to explain gravity in simple terms. Let me start by recalling what I know. Gravity is the force that pulls objects towards each other. Like how the Earth pulls you down to the ground. But why does that happen? I remember Newton's law of universal gravitation. It says that every mass attracts every other mass with a force proportional to their masses and inversely proportional to the square of the distance between them. So, the bigger the objects, the stronger the gravitational pull. But how do I explain that without using complex math?\\n\\nMaybe start with the basic idea: gravity is a force that keeps things from floating away. For example, planets orbit the sun because of gravity. Without it, they'd just float off. But why do we feel it on Earth? Because the Earth's gravity is pulling us down. If there were no Earth, we'd float in space. But why doesn't the Earth just float away? Because it's massive, so its gravity is strong enough to hold us.\\n\\nWait, maybe mention that gravity is always attractive. Unlike electric charges, which can be attractive or repulsive. Gravity is only attractive. That's a key point. Also, the strength of gravity depends on mass and distance. The more massive the\"}]}]" + ] + }, + "metadata": {}, + "execution_count": 3 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### With Extended Thinking Mode Disabled\n", + "\n", + "We disabled it adding `/no_think` to the system prompt" + ], + "metadata": { + "id": "yzZM_YO--uCZ" + } + }, + { + "cell_type": "code", + "source": [ + "messages = [\n", + " {\"role\": \"system\", \"content\": \"/no_think\"},\n", + " {\"role\": \"user\", \"content\": \"Give me a brief explanation of gravity in simple terms.\"},\n", + "]\n", + "pipe(messages)" + ], + "metadata": { + "id": "L0NJWEeW9Wel", + "outputId": "9937daaf-b56e-4944-d211-dad32d131d1d", + "colab": { + "base_uri": "https://localhost:8080/" + } + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "[{'generated_text': [{'role': 'system', 'content': '/no_think'},\n", + " {'role': 'user',\n", + " 'content': 'Give me a brief explanation of gravity in simple terms.'},\n", + " {'role': 'assistant',\n", + " 'content': \"Gravity is a force that pulls objects towards each other. Imagine you're holding a rubber ball. If you let go, the ball will fall towards the ground because of gravity. This force is what makes things stay on the ground, like you standing on the floor. It also keeps the moon orbiting around Earth, and the Earth orbiting around the sun. Gravity is always pulling things towards each other, but sometimes, like when the moon and Earth orbit each other, their speed keeps them moving in circles instead of falling towards each other.\"}]}]" + ] + }, + "metadata": {}, + "execution_count": 4 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Loading the model directly" + ], + "metadata": { + "id": "fxu1LpyF-vTP" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoModelForCausalLM, AutoTokenizer\n", + "\n", + "model_name = \"HuggingFaceTB/SmolLM3-3B\"\n", + "device = \"cuda\" # for GPU usage or \"cpu\" for CPU usage\n", + "\n", + "# load the tokenizer and the model\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModelForCausalLM.from_pretrained(\n", + " model_name,\n", + ").to(device)" + ], + "metadata": { + "id": "mllOfUzABhkE", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 369, + "referenced_widgets": [ + 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"9aaecfa0ff3f41eea181f905deaf2d79" + } + }, + "metadata": {} + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer.json: 0%| | 0.00/17.2M [00:00\n", + "Okay, so I need to explain gravity in simple terms. Let me start by recalling what I know about gravity. Gravity is the force that pulls things towards each other. I remember that it's what keeps us on the ground and makes things fall when we drop them. But how exactly does it work?\n", + "\n", + "Hmm, Newton's law of universal gravitation comes to mind. He said that every mass attracts every other mass. The force depends on the masses of the objects and the distance between them. The formula is F = G*(m1*m2)/r². But I need to simplify this for someone who might not know calculus or physics terms.\n", + "\n", + "Wait, maybe I should start with a basic analogy. Like, imagine two balls. If you have a heavy ball and a light ball, they attract each other. The heavier the balls, the stronger the pull. The closer they are, the stronger the pull. The farther apart, the weaker. That makes sense.\n", + "\n", + "But how do I explain it without using math? Maybe talk about everyday examples. Like, why do we stay on the ground? Because the Earth's gravity pulls us towards it. If there was no gravity, we'd float off into space. But why don't we get pulled into the Earth's core? Because the Earth is so big, the force is balanced by the ground pushing up on us.\n", + "\n", + "Oh, and astronauts in space experience microgravity because there's no air resistance, but they're still under Earth's gravity. So gravity is always there, but if you're in free fall, you don't feel it. Like how when you're in an elevator that's in free fall, you feel weightless.\n", + "\n", + "Also, gravity affects the Moon's orbit around Earth. The Moon is constantly falling towards Earth, but it has enough sideways speed to keep missing it. So gravity is what keeps the Moon in orbit. Similarly, the Earth orbits the Sun because of the Sun's gravity.\n", + "\n", + "I should mention that gravity is a force, but it's also a result of spacetime curvature according to Einstein's theory of general relativity. But maybe that's too advanced for a simple explanation. The user might not know about Einstein's theory, so I should focus on Newton's explanation first.\n", + "\n", + "Let me structure this: start with the basic idea of gravity as a force between masses, use everyday examples, explain why we stay on the ground, how it affects orbits, and maybe touch on why we don't fall into the Earth. Keep it conversational and avoid jargon. Make sure to emphasize that it's not just a force but a fundamental aspect of the universe that shapes everything around us.\n", + "\n", + "\n", + "Gravity is the force that pulls objects toward each other. Here's a simple breakdown:\n", + "\n", + "1. **Every Mass Attracts Every Other Mass**: Gravity is like a \"pull\" between two objects, no matter how big or small they are. The Earth pulls you down, and you pull the Earth up, but the Earth is so massive that its pull on you is much stronger.\n", + "\n", + "2. **Stronger with More Mass**: The heavier the object, the stronger the pull. A mountain is pulled down by gravity just as much as a pebble.\n", + "\n", + "3. **Closer Matters**: If two objects are closer, gravity acts faster. That's why you feel pulled down harder when you're standing on the ground than when you're standing on a mountain (even though the mountain is higher, you're farther from Earth's center).\n", + "\n", + "4. **Why We Stay on the Ground**: Gravity holds you to Earth. If there were no gravity, you'd float off into space. But Earth's surface pushes up on you, balancing the pull.\n", + "\n", + "5. **Orbits and Free Fall**: Gravity keeps the Moon orbiting Earth and Earth orbiting the Sun. When you jump, you're in free fall—falling toward Earth, but you don't hit the ground because Earth is also moving toward you. Astronauts in space feel weightless because they're in free fall around Earth.\n", + "\n", + "6. **It's Everywhere**: Gravity is always there, even in space. But in space, you don't feel it because you're in free fall. Einstein's theory (general relativity) says gravity isn't a force but the bending of spacetime, but for everyday purposes, think of it as a pull.\n", + "\n", + "In short, gravity is what keeps everything connected in the universe—our feet on the ground, the Moon in orbit, and stars in galaxies.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Enabling and Disabling Extended Thinking Mode\n", + "\n", + "We disabled it adding `/no_think` to the system prompt. By default, it's active." + ], + "metadata": { + "id": "Q2WIgjZz---3" + } + }, + { + "cell_type": "code", + "source": [ + "prompt = \"Give me a brief explanation of gravity in simple terms.\"\n", + "messages = [\n", + " {\"role\": \"system\", \"content\": \"/no_think\"},\n", + " {\"role\": \"user\", \"content\": prompt}\n", + "]\n", + "\n", + "text = tokenizer.apply_chat_template(\n", + " messages,\n", + " tokenize=False,\n", + " add_generation_prompt=True,\n", + ")\n", + "\n", + "model_inputs = tokenizer([text], return_tensors=\"pt\").to(model.device)\n", + "\n", + "# Generate the output\n", + "generated_ids = model.generate(**model_inputs, max_new_tokens=32768)\n", + "\n", + "# Get and decode the output\n", + "output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]\n", + "print(tokenizer.decode(output_ids, skip_special_tokens=True))" + ], + "metadata": { + "id": "as_ze0BO_HuP", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "35144016-9237-41fe-eba7-915df5d220fb" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Gravity is a force that pulls objects towards each other. Imagine you're holding a ball, and you let go of it. The ball starts to fall towards the ground because of gravity. This force is always pulling objects towards each other, no matter how far apart they are. It's what keeps the moon orbiting around the Earth, the Earth orbiting around the Sun, and keeps us grounded on the ground. Gravity is what makes things \"stick\" to the ground and keeps everything in place. It's a very weak force, but it's incredibly strong when you consider the vast distances between objects in space.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### Agentic Usage\n", + "\n", + "SmolLM3 supports tool calling. Pass a list of tools under the argument `xml_tools` (for standard tool-calling), or `python_tools` (for calling tools like python functions in a `` snippet)." + ], + "metadata": { + "id": "omKKT7G3_PZA" + } + }, + { + "cell_type": "code", + "source": [ + "tools = [\n", + " {\n", + " \"name\": \"get_weather\",\n", + " \"description\": \"Get the weather in a city\",\n", + " \"parameters\": {\"type\": \"object\", \"properties\": {\"city\": {\"type\": \"string\", \"description\": \"The city to get the weather for\"}}}}\n", + "]\n", + "\n", + "messages = [\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"Hello! How is the weather today in Copenhagen?\"\n", + " }\n", + "]\n", + "\n", + "inputs = tokenizer.apply_chat_template(\n", + " messages,\n", + " enable_thinking=False, # True works as well, your choice!\n", + " xml_tools=tools,\n", + " add_generation_prompt=True,\n", + " tokenize=True,\n", + " return_tensors=\"pt\"\n", + ").to(model.device)\n", + "\n", + "outputs = model.generate(inputs)\n", + "print(tokenizer.decode(outputs[0]))" + ], + "metadata": { + "id": "DP0gagny_RMh", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "da67f501-bce0-4f57-ac53-dce1e497f36f" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "<|im_start|>system\n", + "## Metadata\n", + "\n", + "Knowledge Cutoff Date: June 2025\n", + "Today Date: 09 July 2025\n", + "Reasoning Mode: /no_think\n", + "\n", + "## Custom Instructions\n", + "\n", + "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n", + "\n", + "### Tools\n", + "\n", + "You may call one or more functions to assist with the user query.\n", + "You are provided with function signatures within XML tags:\n", + "\n", + "\n", + "{'name': 'get_weather', 'description': 'Get the weather in a city', 'parameters': {'type': 'object', 'properties': {'city': {'type':'string', 'description': 'The city to get the weather for'}}}}\n", + "\n", + "\n", + "For each function call, return a json object with function name and arguments within XML tags:\n", + "\n", + "{\"name\": , \"arguments\": }\n", + "\n", + "\n", + "<|im_end|>\n", + "<|im_start|>user\n", + "Hello! How is the weather today in Copenhagen?<|im_end|>\n", + "<|im_start|>assistant\n", + "\n", + "\n", + "\n", + "\n", + "{\"name\": \"get_weather\", \"arguments\": {\"city\": \"Copenhagen\"}}\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Remote Inference via Inference Providers\n", + "Ensure you have a valid **HF_TOKEN** set in your environment, running this may bill your account above the free tier.\n", + "The following Python example shows how to run the model remotely on HF Inference Providers, using the **auto** provider setting (automatically selects an available inference provider)." + ], + "metadata": { + "id": "J1JaXMSD3AHx" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "from huggingface_hub import InferenceClient\n", + "\n", + "client = InferenceClient(\n", + " provider=\"auto\",\n", + " api_key=os.environ[\"HF_TOKEN\"],\n", + ")\n", + "\n", + "completion = client.chat.completions.create(\n", + " model=\"HuggingFaceTB/SmolLM3-3B\",\n", + " messages=[\n", + " {\n", + " \"role\": \"user\",\n", + " \"content\": \"What is the capital of France?\"\n", + " }\n", + " ],\n", + ")\n", + "\n", + "print(completion.choices[0].message)" + ], + "metadata": { + "id": "9z13KW6U3AHx", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "b847028e-76dc-40c9-81ee-72493d89aced" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "ChatCompletionOutputMessage(role='assistant', content=\"\\nOkay, so the user is asking for the capital of France. Let me start by recalling what I know. I think the capital is Paris. But wait, maybe I should double-check to be sure. Sometimes people might confuse it with other cities like Lyon or Marseille, but those are major cities in France, not the capital. \\n\\nI remember that Paris is known for the Eiffel Tower and the Louvre Museum. Also, it's the heart of French culture and fashion. The government is based there, right? The President of France lives in the Élysée Palace, which is in Paris. \\n\\nWait, could there be any other city that's considered the capital? Maybe in the past? I don't think so. The capital has been Paris since the city became the capital of the Kingdom of France in the 12th century, and it's been the capital ever since. \\n\\nI should also consider if there's any recent change. For example, sometimes cities like Lyon or Marseille might have been proposed as capitals, but I don't recall any official change. The current capital is definitely Paris. \\n\\nJust to be thorough, I can think of some historical facts. The French Revolution started in Paris, and that's where the revolutionaries declared the end of the monarchy. The city has been a cultural and political center for centuries. \\n\\nSo, putting it all together, the capital of France is Paris. I'm pretty confident about this, but I'll make sure there's no ambiguity. No, there's no doubt. The answer is Paris.\\n\\n\\nThe capital of France is **Paris**. It is a city renowned for its cultural, historical, and architectural significance, as well as its status as a global hub for fashion, art, and finance. Paris is home to iconic landmarks like the Eiffel Tower, the Louvre Museum, and the Seine River, and it serves as the political and administrative center of the country.\", tool_call_id=None, tool_calls=[], reasoning_content=None)\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..190d562 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,16 @@ +{ + "eos_token": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "<|im_end|>", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..074fa66 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7b6a500b662a34eb3f0374db856ba4ad7de4c81040571d78dc0d357238930005 +size 17208819 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