Instructions to use lillycham/tiny-lm-web-98m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lillycham/tiny-lm-web-98m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lillycham/tiny-lm-web-98m")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lillycham/tiny-lm-web-98m") model = AutoModelForCausalLM.from_pretrained("lillycham/tiny-lm-web-98m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use lillycham/tiny-lm-web-98m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lillycham/tiny-lm-web-98m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lillycham/tiny-lm-web-98m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lillycham/tiny-lm-web-98m
- SGLang
How to use lillycham/tiny-lm-web-98m with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "lillycham/tiny-lm-web-98m" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lillycham/tiny-lm-web-98m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "lillycham/tiny-lm-web-98m" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lillycham/tiny-lm-web-98m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lillycham/tiny-lm-web-98m with Docker Model Runner:
docker model run hf.co/lillycham/tiny-lm-web-98m
Upload folder using huggingface_hub
Browse files- README.md +76 -0
- chat/config.json +22 -0
- chat/generation_config.json +4 -0
- chat/model.safetensors +3 -0
- chat/tokenizer.json +0 -0
- chat/tokenizer_config.json +7 -0
- config.json +22 -0
- generation_config.json +4 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +7 -0
README.md
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---
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license: mit
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language: en
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- gpt2
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- tiny-lm
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- educational
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datasets:
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- HuggingFaceFW/fineweb-edu
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- SimpleStories/SimpleStories
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- databricks/databricks-dolly-15k
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---
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# tiny-lm-web-98m
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A 98M-parameter GPT-2 small shaped model, trained from scratch on web text. It is from Part 3 of
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[tiny-lm](https://github.com/lillycham/tiny-lm), a project that builds a language model step by
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step and then looks inside it.
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| | |
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|---|---|
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| Size | 12 layers, width 768, 12 heads, 98.4M parameters |
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| Context | 1,024 tokens |
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| Tokeniser | byte-level BPE, 16,384 tokens, trained on FineWeb-Edu |
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| Training | 2.70B tokens of FineWeb-Edu, 203 minutes on one RTX 5090 |
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| Validation loss | 2.983 nats per token, 1.03 bits per character |
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For comparison, OpenAI's GPT-2 small is about 1.04 bits per character on the same text.
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There are two models in this repository:
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- **The root** is the base model. It continues text.
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- **`chat/`** is the base model after a 150M-token anneal (25% SimpleStories), then supervised
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fine-tuning on Dolly 15k, then fine-tuning on `System:` lines (rules such as "Answer in one
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sentence", and names for the model and the user).
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The weights are a standard `GPT2LMHeadModel`. The q, k and v biases that GPT-2 has are zero,
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because the model was trained without them.
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "lillycham/tiny-lm-web-98m"
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tok = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(repo)
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ids = tok("The water cycle is", return_tensors="pt").input_ids
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ids = torch.cat([torch.tensor([[tok.eos_token_id]]), ids], dim=1) # text starts after <|endoftext|>
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out = model.generate(ids, max_new_tokens=100, do_sample=True, temperature=0.8)
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print(tok.decode(out[0, 1:]))
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```
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The chat model uses this format, and ends its answer with `<|endoftext|>`. Load it with
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`subfolder="chat"`. The `System:` line is optional.
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```
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System: Answer in one sentence.
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User: Why is the sky blue?
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Assistant:
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```
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## Limits
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The text is fluent, but most facts are invented. The chat model answers in the right form (a list,
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verse or one line) but does not know much. It does not yet take answers from a paragraph in its
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context. Do not use it for anything that needs true answers.
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## Data and licence
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Trained on [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) (ODC-By),
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[SimpleStories](https://huggingface.co/datasets/SimpleStories/SimpleStories) (MIT) and, for the chat
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model, [Dolly 15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) (CC BY-SA 3.0).
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The code and weights are MIT.
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chat/config.json
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{
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"architectures": [
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"GPT2LMHeadModel"
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],
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"model_type": "gpt2",
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"vocab_size": 16384,
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"n_positions": 1024,
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"n_ctx": 1024,
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"n_embd": 768,
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"n_layer": 12,
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"n_head": 12,
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"n_inner": 3072,
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"activation_function": "gelu_new",
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"layer_norm_epsilon": 1e-05,
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"resid_pdrop": 0.0,
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"embd_pdrop": 0.0,
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"attn_pdrop": 0.0,
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"initializer_range": 0.02,
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"tie_word_embeddings": true,
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"bos_token_id": 16383,
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"eos_token_id": 16383
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}
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chat/generation_config.json
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{
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"bos_token_id": 16383,
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"eos_token_id": 16383
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}
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chat/model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:008a70080d37bf9b6b82d3cd30f55c2cca29c949c544262aabdb53770506f7de
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size 393716352
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chat/tokenizer.json
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chat/tokenizer_config.json
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{
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"tokenizer_class": "PreTrainedTokenizerFast",
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"model_max_length": 1024,
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces": false
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}
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config.json
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{
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"architectures": [
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"GPT2LMHeadModel"
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],
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"model_type": "gpt2",
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"vocab_size": 16384,
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"n_positions": 1024,
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"n_ctx": 1024,
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"n_embd": 768,
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"n_layer": 12,
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"n_head": 12,
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"n_inner": 3072,
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"activation_function": "gelu_new",
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"layer_norm_epsilon": 1e-05,
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"resid_pdrop": 0.0,
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"embd_pdrop": 0.0,
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"attn_pdrop": 0.0,
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"initializer_range": 0.02,
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"tie_word_embeddings": true,
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"bos_token_id": 16383,
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"eos_token_id": 16383
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}
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generation_config.json
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{
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"bos_token_id": 16383,
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"eos_token_id": 16383
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:072ed8f16adff3733bb9fc55eb57a3c0cbb7c79f69cf02b31f578d774d8fb781
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size 393716352
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tokenizer.json
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The diff for this file is too large to render.
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tokenizer_config.json
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{
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"tokenizer_class": "PreTrainedTokenizerFast",
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"model_max_length": 1024,
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"bos_token": "<|endoftext|>",
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"eos_token": "<|endoftext|>",
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"clean_up_tokenization_spaces": false
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}
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