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Model: josh-a/rivet-1b-pt
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- text-generation
+- pretrained
+- base-model
+- from-scratch
+- qwen3
+library_name: transformers
+---
+
+# Rivet-1B-PT (base model)
+
+The **pretrained base** of Rivet-1B: a 1.09B parameter language model trained **entirely from scratch** (custom tokenizer, custom corpus, no inherited weights). This repo holds the raw pretrained checkpoint (v9 lineage), before any supervised fine-tuning.
+
+> Uses the Qwen3 *architecture* for serving compatibility. Shares **no weights** with Qwen: the tokenizer (custom 32k SentencePiece) and every parameter were trained from zero.
+
+**Note:** this is the raw base model. It completes text; it does not follow instructions. The post-trained sibling (rivet-1b-it), which chats and reasons in `<think>` blocks, is not currently released.
+
+## What this is for
+
+- Fine-tuning your own model on top of a from-scratch 1B with a 16K context
+- Research into small-model pretraining dynamics (full pipeline and data disclosure in the [technical report](https://huggingface.co/josh-a/rivet-1b-pt/blob/main/TECHNICAL_REPORT.md))
+- A clean, fully-documented provenance chain: every pretraining token is public data or documented
+
+## Training lineage
+
+1. **v6 pilot**: 6.3B tokens, deliberately undertrained (2x RTX 5090)
+2. **v7 main pretrain**: ~100B tokens over 7 weeks (from the 283.4B-token corpus), eval loss 1.598 (2x RTX 5090)
+3. **v8 context extension**: RoPE ABF (theta 100K → 1e6) to 16K context, +3B long-doc tokens (2x H100)
+4. **v9 "intelligence run"**: +20B tokens reweighted toward reasoning/math/code, eval loss 1.477. **This checkpoint.**
+
+Total: ~129B tokens trained from the 387.5B-token pool (full composition in the [technical report](https://huggingface.co/josh-a/rivet-1b-pt/blob/main/TECHNICAL_REPORT.md)).
+
+## Training logs
+
+Raw TensorBoard event files for the v7 main pretrain and v9 intelligence run are in `tensorboard/` (the report's loss-curve figure is generated from these).
+
+## Model details
+
+| | |
+|---|---|
+| Params | 1,086,422,528 |
+| Architecture | Qwen3ForCausalLM (from-scratch weights) |
+| Hidden / layers | 2048 / 22 |
+| Heads | 16 query / 8 KV (GQA), head_dim 128 |
+| FFN | 5504 (SwiGLU) |
+| Vocab | 32000 (custom SentencePiece) |
+| Context | 16384 (RoPE θ=1e6; YaRN-extrapolable to ~64K) |
+| Tokenizer | custom 32k; `<think>`=4, `</think>`=5, `<|im_start|>`=6, `<|im_end|>`=7 |
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+m = "josh-a/rivet-1b-pt"
+tok = AutoTokenizer.from_pretrained(m)
+model = AutoModelForCausalLM.from_pretrained(m, dtype=torch.bfloat16).to("cuda").eval()
+
+ids = tok("The capital of New Zealand is", return_tensors="pt").to("cuda")
+out = model.generate(**ids, max_new_tokens=20, do_sample=False)
+print(tok.decode(out[0][ids["input_ids"].shape[1]:]))
+# -> " Wellington" (as of the v9 intelligence run)
+```
+
+## Evals
+
+Per-question GSM8K results are in `evals/gsm8k_v10_results.jsonl` (1,319 rows: question, gold, prediction, raw output tail). Note: GSM8K was run on the post-trained sibling checkpoint (rivet-1b-it, not released), scoring 12.7% (168/1319). Methodology in the technical report.
+
+## Limitations
+
+- This is a **base model**: no instruction following, no chat template behaviour, no refusals, no alignment of any kind.
+- Factually modest (1B parameters); see the technical report for the honest eval picture.
+
+## License
+
+Apache-2.0. From-scratch weights; no upstream model license applies.