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latest.json: checkpoints/1701013k (step 68,155)

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  1. README.md +13 -8
  2. latest.json +7 -7
  3. latest/config.json +2 -2
  4. latest/model.safetensors +1 -1
README.md CHANGED
@@ -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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- 62,895 (1,217,308,397 tokens seen).
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  ## Model details
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@@ -95,6 +95,10 @@ Across its training, this checkpoint and the runs it was continued from saw:
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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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@@ -103,16 +107,17 @@ Each step is listed because the run's records show it was done:
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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. **Identifier check.** Synthetic chats that name a known person's identifier are dropped.
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- 5. **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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- 6. **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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- 7. **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:** 62,895
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- - **Tokens seen:** 1,217,308,397 (one token is one byte)
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- - **Quantization-aware training:** from step 14,655 of 62,895, so for 98% of its 1,217,308,397 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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  ➡️ **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
33
+ 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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+
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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
latest.json CHANGED
@@ -1,6 +1,6 @@
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  {
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  "name": "nirca-mini",
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- "step": 62895,
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  "quant": "ternary",
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  "quant_scope": "all",
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  "n_experts": 32,
@@ -40,17 +40,17 @@
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  "pool_depth": 1,
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  "pool_max": 32
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  },
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- "tokens": 1217308397,
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- "checkpoint": "checkpoints/1217308k/",
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  "files": {
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  "config.json": {
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  "model.safetensors": {
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  "name": "nirca-mini",
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  "pool_depth": 1,
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+ "checkpoint": "checkpoints/1701013k/",
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latest/config.json CHANGED
@@ -1,7 +1,7 @@
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  "n_experts": 32,
 
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  "quant": "ternary",
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latest/model.safetensors CHANGED
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