KLM 1.1 base model

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README.md

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+---
+license: apache-2.0
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+base_model: MinimaLabs/KeyLM-75M
+tags:
+  - klm
+  - keylm
+  - small-language-model
+  - base
+  - pretrained
+  - gqa
+  - rope
+  - swiglu
+  - qk-norm
+datasets:
+  - mlfoundations/dclm-baseline-1.0
+  - HuggingFaceTB/smollm-corpus
+  - openbmb/UltraData-Math
+  - nvidia/OpenMathInstruct-2
+  - IFM/Pretrain-Behaviors
+  - IFM/Code-Reasoning
+  - openbmb/UltraX-Preview
+---
+
+![KLM 1.1](charts/banner.svg)
+
+# KLM 1.1
+
+**KLM 1.1 is an 88M-parameter base language model, the continued training of KeyLM-75M.** It scores 17.40 on the Open SLM Intelligence Index, up from KeyLM-75M's 10.44. That moves it from 38th to 9th among models under 100M parameters on the Open SLM leaderboard.
+
+KeyLM is now KLM. This release keeps the KeyLM architecture and continues from its weights.
+
+## Why KLM 1.1 exists
+
+KeyLM-75M was the first model I trained, and it was flawed in many ways. Most of its data was casual chat (Reddit, WildChat, LMSYS and similar), its tokenizer was small, and it stopped at about 18B tokens while still at a high learning rate. It is currently 38th on the leaderboard among models under 100M parameters.
+
+The goal of KLM 1.1 was to find out how far that same base model could be pushed by more training alone, without changing its architecture. Over about 120B more tokens, I gave it a larger tokenizer, retrained it on a cleaner, education-, math- and reasoning-heavy mix, and ended with a proper learning-rate decay.
+
+![KLM 1.1 on the Open SLM leaderboard, models under 100M parameters](charts/leaderboard.svg)
+
+The gain shows up on every benchmark, most on ARC-Easy (+8.6 points) and ArithMark-3 (+5.7).
+
+![KeyLM-75M and KLM 1.1 on each benchmark](charts/jump.svg)
+
+| | KeyLM-75M | KLM 1.1 |
+|---|---|---|
+| Int Index | 10.44 | **17.40** |
+| Rank, under 100M | #38 | **#9** |
+| HellaSwag | 29.66% | **33.65%** |
+| ARC-Easy | 35.73% | **44.36%** |
+| ARC-Challenge | 23.98% | **25.17%** |
+| PIQA | 60.50% | **64.80%** |
+| ArithMark-3 | 30.10% | **35.80%** |
+| Parameters | 75.3M | 88.1M |
+| Vocabulary | 12,020 | 24,576 |
+| Training tokens | ~18.7B | ~138.6B in total |
+
+## How it was trained
+
+KLM 1.1 went through three stages after KeyLM-75M, each continuing from the last one's weights:
+
+| Stage | Tokens | What changed | Int Index after |
+|---|---|---|---|
+| KeyLM-75M (start) | 18.7B | | 10.47 |
+| Tokenizer expansion | 20.0B | Vocabulary grown from 12,020 to 24,576 tokens; trained on web, math and code | 15.15 |
+| Short continuation | 5.0B | Same mix, learning rate re-warmed | 15.52 |
+| Long continuation | 94.9B | New mix, 4-GPU DiLoCo, warmup-stable-decay schedule | **17.40** |
+
+Int Index values in this table are from our own evaluation runs. Our harness scores KeyLM-75M at 10.47; the leaderboard lists 10.44.
+
+![Int Index across training, and held-out loss during the long continuation](charts/progress.svg)
+
+Held-out loss fell at every evaluation of the long continuation, from 2.131 to 1.814 nats per token, averaged over held-out text from each of its 11 data sources. Most of the benchmark gain arrived in the final learning-rate decay.
+
+### Tokenizer expansion
+
+The 24,576-token vocabulary keeps all of KeyLM-75M's tokens and adds 12,299 new byte-level BPE merges learned on the training mix. Each new token starts from the average of the old tokens it replaces. The larger vocabulary encodes the same text in 1.31–1.41× fewer tokens, with the biggest savings on code and math. This stage trained on [UltraX Ultra-FineWeb](https://huggingface.co/datasets/openbmb/UltraX-Preview) web text (80%), [UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math) (15%) and [Code-Reasoning](https://huggingface.co/datasets/IFM/Code-Reasoning) (5%).
+
+### Long continuation
+
+The long continuation ran 1,810 rounds of DiLoCo across four GPUs. Each GPU trained 100 steps on its own slice of the data, and the four sets of changes were then merged. The learning rate warmed up to 3e-5, held there for the first 80% of the run (75.9B tokens), then decayed linearly to 10% of peak over the final 20% (19.0B tokens). The decay used a mix heavier in math and textbooks.
+
+| Source | Stable phase | Decay phase |
+|---|---:|---:|
+| [DCLM-baseline](https://huggingface.co/datasets/mlfoundations/dclm-baseline-1.0) web text | 35% | 20% |
+| [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) (dedup) | 30% | 25% |
+| [Cosmopedia v2](https://huggingface.co/datasets/HuggingFaceTB/smollm-corpus) synthetic textbooks | 15% | 20% |
+| [UltraData-Math](https://huggingface.co/datasets/openbmb/UltraData-Math) L2 | 12.5% | 11% |
+| UltraData-Math L3 textbook exercises and QA | 2.5% | 6% |
+| [OpenMathInstruct-2](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2) problems with solutions | | 8% |
+| [Pretrain-Behaviors](https://huggingface.co/datasets/IFM/Pretrain-Behaviors) reasoning and format rewrites | 3% | 8% |
+| [Code-Reasoning](https://huggingface.co/datasets/IFM/Code-Reasoning) | 2% | 2% |
+
+All sources were filtered before training. The filters dropped documents the model had already trained on, exact repeats, empty documents, and any document sharing a 13-word sequence with an item from the five evaluation sets.
+
+| Field | Value |
+|---|---|
+| Optimizer | AdamW (betas 0.9 / 0.95, weight decay 0.1), state carried over from the previous stage |
+| DiLoCo | 4 workers, 100 inner steps per round, outer learning rate 0.7 with Nesterov momentum 0.9 |
+| Batch | 64 × 2,048 tokens per worker step (524,288 tokens per step across the four workers) |
+| Precision | bf16 autocast over fp32 weights |
+| Gradient clipping | 1.0 |
+
+## Evaluation
+
+All scores are zero-shot. ARC, HellaSwag and PIQA use lm-eval 0.4.12 with length-normalized accuracy. ArithMark-3 uses the official [ArithMark-3.0](https://huggingface.co/datasets/AxiomicLabs/ArithMark-3.0) script. Our harness reproduces the leaderboard's published KeyLM numbers to within ±0.17 points per task.
+
+The Intelligence Index is the Open SLM leaderboard's summary score. Each benchmark is rescaled so that random guessing scores 0 and a perfect score is 100, then averaged, with ARC as the mean of Easy and Challenge:
+
+```
+Int Index = (HellaSwag + ARC + PIQA + 0.65 × ArithMark-3) / 3.65
+```
+
+The table lists the models ranked just above and below KLM 1.1 among models under 100M parameters, with scores from the leaderboard (snapshot 2026-10-11).
+
+| Rank | Model | Params | ARC-Easy | ARC-Challenge | HellaSwag | PIQA | ArithMark-3 | Int Index |
+|---|---|---|---|---|---|---|---|---|
+| 1 | [void.1](https://huggingface.co/dotlabs/void.1) | 90.2M | 47.31% | 28.16% | 38.68% | 67.46% | 44.80% | 23.92 |
+| 2 | [100M-exp](https://huggingface.co/User01110/100M-exp) | 98.2M | 49.83% | 27.22% | 37.78% | 66.97% | 40.00% | 22.47 |
+| 3 | [Rose-1.5-Medium](https://huggingface.co/GODELEV/Rose-1.5-Medium) | 98.2M | 47.22% | 27.13% | 38.09% | 64.80% | 40.70% | 21.07 |
+| 4 | [tinctura-v1](https://huggingface.co/bench-labs/tinctura-v1) | 96.2M | 47.98% | 25.77% | 37.96% | 65.61% | 38.40% | 20.81 |
+| 5 | [Surjo-100m](https://huggingface.co/SurjoLabs/Surjo-100m) | 97.7M | 47.64% | 25.85% | 35.05% | 63.87% | 38.90% | 18.86 |
+| 6 | [cRia-LM-75M](https://huggingface.co/sz14/cRia-LM-75M) | 75.7M | 46.97% | 25.77% | 35.49% | 63.93% | 35.30% | 18.06 |
+| 7 | [50M-pro](https://huggingface.co/fromziro/50M-pro) | 49.6M | 45.62% | 25.51% | 33.25% | 64.80% | 37.40% | 17.93 |
+| 8 | [Rose-Medium](https://huggingface.co/GODELEV/Rose-Medium) | 97.8M | 44.19% | 26.19% | 35.29% | 62.94% | 38.30% | 17.73 |
+| **9** | **KLM 1.1** | **88.1M** | **44.36%** | **25.17%** | **33.65%** | **64.80%** | **35.80%** | **17.40** |
+| 10 | [Surjo-50M](https://huggingface.co/SurjoLabs/Surjo-50m) | 53.8M | 44.99% | 25.94% | 31.37% | 63.11% | 37.90% | 16.40 |
+| 11 | [gibc-v2-lm](https://huggingface.co/Unluckyexe/gibc-v2-lm) | 49.8M | 47.85% | 26.11% | 31.87% | 60.28% | 40.20% | 16.13 |
+| 38 | [KeyLM-75M](https://huggingface.co/MinimaLabs/KeyLM-75M) | 75.3M | 35.73% | 23.98% | 29.66% | 60.50% | 30.10% | 10.44 |
+
+## Architecture
+
+KLM 1.1 keeps the KeyLM-75M architecture unchanged. Only the embedding and output layers grew with the vocabulary, from 75.3M to 88.1M parameters.
+
+| Field | Value |
+|---|---|
+| Parameters | 88,108,544 |
+| Architecture | Decoder-only transformer (Llama/Qwen3-style) |
+| Layers | 24 |
+| Hidden size | 512 |
+| Attention | 8 query heads, 2 KV heads (GQA), per-head QK-RMSNorm |
+| Feed-forward | SwiGLU, hidden size 1,280 |
+| Position encoding | RoPE (theta 10,000) |
+| Context length | 2,048 tokens |
+| Vocabulary | 24,576 (byte-level BPE) |
+| Embeddings | Untied input and output |
+| Weights | fp32 safetensors, as trained |
+
+The weights load as a standard Transformers `Qwen3ForCausalLM`, since the network computes exactly what that class computes. No custom code is needed, and tools that support Qwen3 can run it.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "MinimaLabs/KLM-1.1"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16)
+
+inputs = tokenizer("The three primary colors are", return_tensors="pt")
+outputs = model.generate(
+    **inputs, max_new_tokens=40, do_sample=True,
+    temperature=0.7, top_p=0.9, repetition_penalty=1.1,
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Limitations
+
+- **Base model only.** KLM 1.1 completes text. It is not instruction-tuned and does not hold a conversation. The tokenizer has ChatML tokens (`<|im_start|>`, `<|im_end|>`), but the model never saw them in training.
+- **Small model.** Its world knowledge is thin and its facts are often wrong. Do not rely on it for factual answers, math or code.
+- English only, with a 2,048-token context window.
+- No safety alignment. Add your own filtering before any user-facing use.
+
+## License
+
+Apache-2.0. See [LICENSE](LICENSE).
+
+## Citation
+
+```bibtex
+@misc{klm11_2026,
+  title  = {KLM 1.1: continued training of KeyLM-75M},
+  author = {Eclipse-Senpai},
+  year   = {2026},
+  howpublished = {\url{https://huggingface.co/MinimaLabs/KLM-1.1}}
+}
+```

config.json

@@ -0,0 +1,59 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "float32",
+  "eos_token_id": 2,
+  "head_dim": 64,
+  "hidden_act": "silu",
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 1280,
+  "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"
+  ],
+  "max_position_embeddings": 2048,
+  "max_window_layers": 28,
+  "model_type": "qwen3",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 24,
+  "num_key_value_heads": 2,
+  "pad_token_id": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 10000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "5.19.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 24576
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "pad_token_id": 2,
+  "transformers_version": "5.19.0",
+  "use_cache": true
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:71a06958227c22560ac43a07a1ded5b149e0ce64a2182b448f39336f5bb65d8c
+size 352463752

special_tokens_map.json

@@ -0,0 +1,9 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "unk_token": "[UNK]",
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>"
+  ]
+}

tokenizer_config.json

@@ -0,0 +1,11 @@
+{
+  "bos_token": "<s>",
+  "eos_token": "</s>",
+  "lowercase": false,
+  "model_max_length": 2048,
+  "tokenizer_class": "PreTrainedTokenizerFast",
+  "unk_token": "[UNK]",
+  "add_bos_token": false,
+  "add_eos_token": false,
+  "clean_up_tokenization_spaces": false
+}