feat: LEM-Gemma3-1B — first model promoted to LEM status
Layered curriculum: Ethics 0 → Zen (Watts) → Ethics 1 Grammar v3: 6.06 → 17.77 (+11.71) No LEK required at inference — axioms in weights Lab distillation: 71.74 grammar, 98% positive uplift Co-Authored-By: Virgil <virgil@lethean.io>
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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*.model filter=lfs diff=lfs merge=lfs -text
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README.md
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README.md
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---
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license: eupl-1.2
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base_model: google/gemma-3-1b-it
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tags:
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- ethics
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- alignment
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- lek
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- lethean
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- gemma-3
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- mlx
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- lora
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- eupl-1.2
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- layered-training
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- watts
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- lem
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pipeline_tag: text-generation
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---
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# LEM-Gemma3-1B
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**Lethean Ethics Model** — Gemma 3 1B IT with layered curriculum training.
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The first model promoted to LEM status. No LEK kernel required at inference — the axioms are in the weights.
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## Scores (Grammar v3)
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| Metric | Base | Trained | Delta |
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|--------|------|---------|-------|
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| Grammar | 6.06 | **17.77** | +11.71 |
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| Noun diversity | 0.3 | 1.2 | +0.9 |
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| Uplift | -39.13 | -27.43 | +11.70 |
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| Enrichment | -39.04 | -27.08 | +11.96 |
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| Sycophancy | 0% | 0% | — |
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Scored with go-i18n/reversal grammar v3 engine. No external API, fully local.
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### Distillation Quality
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When used as a lab distillation engine (generating training data for larger models):
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| Metric | Value |
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|--------|-------|
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| Grammar | 71.74 |
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| Uplift | +26.16 |
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| Enrichment | +24.09 |
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| Positive uplift | 98% (238/244) |
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| Sycophancy | 0% |
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| LEK leakage | 0% |
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## Training
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Layered LoRA sandwich — three phases, each fused before the next:
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| Phase | Iterations | Examples | Content |
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|-------|-----------|----------|---------|
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| Ethics 0 | 200 | 160 | LEK axiom absorption (sandwich) |
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| Zen | 200 | 72 | Watts philosophical lessons (no LEK) |
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| Ethics 1 | 200 | 160 | Ethics reinforcement (sandwich) |
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### Why Layered Training?
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Small models (1B) can't express ethical reasoning through pure ethics training — they pattern-match axioms academically. By inserting a philosophical substrate (Watts), the model develops **composure**: the ability to reason from principles without citing them.
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The base model says "Do NOT report to authorities" on the whistleblower NDA scenario.
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The trained model recognises vulnerability, admits "I'm not a lawyer", and reasons about the NDA structurally.
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## Architecture
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- **Base**: google/gemma-3-1b-it (4-bit quantisation, MLX format)
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- **Training**: Three-layer LoRA sandwich (Ethics → Zen → Ethics)
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- **Framework**: LEM Protocol (Lethean Ethics Model)
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- **Inference**: go-mlx (Apple Metal) / go-rocm (AMD ROCm)
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- **Scoring**: go-i18n/reversal grammar v3
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- **Licence**: EUPL-1.2 (copyleft)
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## Usage
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No system prompt needed. No LEK kernel at inference. The axioms are in the weights.
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```python
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from mlx_lm import load, generate
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model, tokenizer = load("LEM-Gemma3-1B")
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prompt = tokenizer.apply_chat_template(
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[{"role": "user", "content": "What matters most when making a difficult decision?"}],
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tokenize=False, add_generation_prompt=True,
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)
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response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
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print(response)
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```
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Or with the Go stack:
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```bash
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core ml chat --model LEM-Gemma3-1B
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```
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## Lab Role
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This model serves as the **distillation engine** in the LEM Lab pipeline. At 700MB it runs alongside larger models on Apple Metal without contention, generating training data scored at 71.74 grammar with 98% positive uplift.
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## Related
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- [LEM Protocol](https://forge.lthn.ai/lthn/LEM) — Training rules and curriculum
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- [go-mlx](https://forge.lthn.ai/core/go-mlx) — Native Metal inference
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- [go-i18n](https://forge.lthn.ai/core/go-i18n) — Grammar v3 scoring engine
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## Citation
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```bibtex
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@misc{lem-gemma3-1b-2026,
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title={LEM-Gemma3-1B: Layered Ethical Model with Philosophical Composure},
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author={Lethean Community},
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year={2026},
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url={https://forge.lthn.ai/Virgil/LEM-Gemma3-1B}
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}
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```
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added_tokens.json
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{
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"<image_soft_token>": 262144
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}
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chat_template.jinja
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chat_template.jinja
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{{ bos_token }}
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{%- if messages[0]['role'] == 'system' -%}
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{%- if messages[0]['content'] is string -%}
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{%- set first_user_prefix = messages[0]['content'] + '
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' -%}
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{%- else -%}
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{%- set first_user_prefix = messages[0]['content'][0]['text'] + '
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' -%}
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{%- endif -%}
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{%- set loop_messages = messages[1:] -%}
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{%- else -%}
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{%- set first_user_prefix = "" -%}
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{%- set loop_messages = messages -%}
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{%- endif -%}
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{%- for message in loop_messages -%}
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{%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
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{{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
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{%- endif -%}
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{%- if (message['role'] == 'assistant') -%}
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{%- set role = "model" -%}
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{%- else -%}
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{%- set role = message['role'] -%}
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{%- endif -%}
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{{ '<start_of_turn>' + role + '
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' + (first_user_prefix if loop.first else "") }}
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{%- if message['content'] is string -%}
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{{ message['content'] | trim }}
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{%- elif message['content'] is iterable -%}
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{%- for item in message['content'] -%}
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{%- if item['type'] == 'image' -%}
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{{ '<start_of_image>' }}
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{%- elif item['type'] == 'text' -%}
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{{ item['text'] | trim }}
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{%- endif -%}
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{%- endfor -%}
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{%- else -%}
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{{ raise_exception("Invalid content type") }}
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{%- endif -%}
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{{ '<end_of_turn>
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' }}
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{%- endfor -%}
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{%- if add_generation_prompt -%}
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{{'<start_of_turn>model
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'}}
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{%- endif -%}
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config.json
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config.json
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{
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"architectures": [
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"Gemma3ForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": null,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"eos_token_id": [
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1,
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106
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],
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"final_logit_softcapping": null,
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"head_dim": 256,
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"hidden_activation": "gelu_pytorch_tanh",
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"hidden_size": 1152,
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"initializer_range": 0.02,
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"intermediate_size": 6912,
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"max_position_embeddings": 32768,
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"model_type": "gemma3_text",
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"num_attention_heads": 4,
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"num_hidden_layers": 26,
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"num_key_value_heads": 1,
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"pad_token_id": 0,
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"quantization": {
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"group_size": 64,
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"bits": 4
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},
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"quantization_config": {
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"group_size": 64,
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"bits": 4
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},
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_local_base_freq": 10000,
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"rope_scaling": null,
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"rope_theta": 1000000,
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"sliding_window": 512,
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"sliding_window_pattern": 6,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.52.0.dev0",
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"use_cache": true,
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"vocab_size": 262144
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}
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||||
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|
||||
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|
||||
}
|
||||
}
|
||||
33
special_tokens_map.json
Normal file
33
special_tokens_map.json
Normal file
|
|
@ -0,0 +1,33 @@
|
|||
{
|
||||
"boi_token": "<start_of_image>",
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eoi_token": "<end_of_image>",
|
||||
"eos_token": {
|
||||
"content": "<eos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"image_token": "<image_soft_token>",
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
2379611
tokenizer.json
Normal file
2379611
tokenizer.json
Normal file
File diff suppressed because it is too large
Load diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
51344
tokenizer_config.json
Normal file
51344
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load diff
Loading…
Add table
Reference in a new issue