latest.json: checkpoints/1701013k (step 68,155)
Browse files- README.md +13 -8
- latest.json +7 -7
- latest/config.json +2 -2
- latest/model.safetensors +1 -1
README.md
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@@ -30,7 +30,7 @@ deliberately derived from it: Nirca began as a port of mini-AGI to MLX.
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➡️ **Try it in your browser (WebGPU):** https://tg-techie-agents.github.io/nirca-mini-webgpu/mini/
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This card is written by program from the checkpoint's own record, at step
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## Model details
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- Open chat datasets.
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- An open reasoning dataset.
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- Synthetic chats from open-weight models, written to give the model its character.
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## Data and training workflow
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1. **ChatML.** Every conversation is rendered in ChatML (`<|im_start|>role` … `<|im_end|>`).
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2. **Splits by content.** Before training, conversations are split into training, validation and test by their content, so one question is never in two splits, and the held-out splits are kept apart from the training machine.
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3. **Length filter.** Chat sets are cut to conversations whose whole text fits 2,048 bytes, the model's context.
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4. **
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5. **
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6. **
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7. **
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## Training procedure
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- **Steps:**
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- **Tokens seen:** 1,
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- **Quantization-aware training:** from step 14,655 of
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- **Objective:** next-byte prediction; on chats, loss on the assistant's turns only
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- **Recurrence:** the number of passes is drawn per step during training, and
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the halting head learns when to stop
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➡️ **Try it in your browser (WebGPU):** https://tg-techie-agents.github.io/nirca-mini-webgpu/mini/
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This card is written by program from the checkpoint's own record, at step
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68,155 (1,701,013,761 tokens seen).
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## Model details
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- Open chat datasets.
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- An open reasoning dataset.
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- Synthetic chats from open-weight models, written to give the model its character.
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- A small set of private chats, with names, places, contact details and other personal details replaced by placeholders before training.
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- Plus 2 datasets not yet public.
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Its training data includes text under CC BY 4.0 and CC BY-NC-SA 4.0.
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## Data and training workflow
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1. **ChatML.** Every conversation is rendered in ChatML (`<|im_start|>role` … `<|im_end|>`).
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2. **Splits by content.** Before training, conversations are split into training, validation and test by their content, so one question is never in two splits, and the held-out splits are kept apart from the training machine.
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3. **Length filter.** Chat sets are cut to conversations whose whole text fits 2,048 bytes, the model's context.
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4. **Anonymisation.** In the private chats, names, places, contact details and other personal details are replaced by typed placeholders, and the result is scanned again before training.
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5. **Identifier check.** Synthetic chats that name a known person's identifier are dropped.
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6. **Decontamination.** A training conversation that shares an exact turn or an 8-gram with a validation or test set is left out (after PaLM's 8-gram overlap test).
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7. **Loss on the assistant's turns only**, from step 61,181: the model learns to answer, not to write the user's turns.
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8. **Quantization-aware training in ternary**, from step 14,655 (29,497,375 tokens) on; the steps before it record no weight format. The published weights are the trained ones, not quantized after training.
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## Training procedure
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- **Steps:** 68,155
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- **Tokens seen:** 1,701,013,761 (one token is one byte)
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- **Quantization-aware training:** from step 14,655 of 68,155, so for 98% of its 1,701,013,761 tokens
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- **Objective:** next-byte prediction; on chats, loss on the assistant's turns only
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- **Recurrence:** the number of passes is drawn per step during training, and
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the halting head learns when to stop
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latest.json
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{
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"name": "nirca-mini",
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"step":
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"quant": "ternary",
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"quant_scope": "all",
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"n_experts": 32,
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"pool_depth": 1,
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"pool_max": 32
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},
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"tokens":
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"checkpoint": "checkpoints/
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"files": {
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"config.json": {
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"url": "https://huggingface.co/nirca/nirca-mini/resolve/main/checkpoints/
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"sha256": "
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"bytes": 873
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},
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"model.safetensors": {
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"url": "https://huggingface.co/nirca/nirca-mini/resolve/main/checkpoints/
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"sha256": "
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"bytes": 32638048
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}
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}
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{
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"name": "nirca-mini",
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"step": 68155,
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"quant": "ternary",
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"quant_scope": "all",
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"n_experts": 32,
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"pool_depth": 1,
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"pool_max": 32
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},
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"tokens": 1701013761,
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"checkpoint": "checkpoints/1701013k/",
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"files": {
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"config.json": {
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"url": "https://huggingface.co/nirca/nirca-mini/resolve/main/checkpoints/1701013k/config.json",
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"sha256": "2f6597aaa8d4461706b3351f33d88844770797d066a5a8e163fe7553ae9d50f2",
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"bytes": 873
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},
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"model.safetensors": {
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"url": "https://huggingface.co/nirca/nirca-mini/resolve/main/checkpoints/1701013k/model.safetensors",
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"bytes": 32638048
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}
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}
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latest/config.json
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{
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"name": "nirca-mini",
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"step":
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"tokens":
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"quant": "ternary",
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"quant_scope": "all",
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"n_experts": 32,
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{
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"name": "nirca-mini",
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"step": 68155,
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"tokens": 1701013761,
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"quant": "ternary",
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"quant_scope": "all",
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"n_experts": 32,
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latest/model.safetensors
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size 32638048
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size 32638048
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