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Model: farbodtavakkoli/OTel-LLM-1B-IT Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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base_model:
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- google/gemma-3-1b-it
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tags:
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- telecom
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- telecommunications
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- gsma
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- fine-tuned
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pipeline_tag: text-generation
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---
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# OTel-LLM-1B-IT
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**OTel-LLM-1B-IT** is a telecom-specialized language model fine-tuned on telecommunications domain data. It is part of the [OTel Family of Models](https://huggingface.co/collections/farbodtavakkoli/otel-llm), an open-source initiative to build industry-standard AI models for the global telecommunications sector.
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## Model Details
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| Attribute | Value |
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|-----------|-------|
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| **Base Model** | [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) |
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| **Parameters** | 1B |
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| **Training Method** | Full parameter fine-tuning |
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| **Language** | English |
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| **License** | Apache 2.0 |
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## Training Data
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The model was trained on telecom-focused data curated by 100+ domain experts. Each source class was contributed by a specific institutional partner:
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| Source | Contributor |
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|---|---|
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| arXiv telecom papers, 3GPP standards, telecom Wikipedia, telecom Common Crawl | Yale University |
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| GSMA Permanent Reference Documents, Discover portal | GSMA |
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| IETF RFC series | NetoAI |
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| Industry whitepapers | Khalifa University |
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| O-RAN specifications (working groups 1, 2, 4, 5, 6, 7, 8, 9, 10) | University of Leeds |
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| O-RAN documents across working groups | The University of Texas at Dallas |
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Released datasets: [OTel-LLM](https://huggingface.co/datasets/farbodtavakkoli/OTel-LLM), [OTel-Embedding](https://huggingface.co/datasets/farbodtavakkoli/OTel-Embedding), [OTel-Reranker](https://huggingface.co/datasets/farbodtavakkoli/OTel-Reranker), [OTel-Safety](https://huggingface.co/datasets/farbodtavakkoli/OTel-Safety).
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## Intended Use
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The OTel model family is designed to power end-to-end Retrieval-Augmented Generation (RAG) pipelines for telecommunications. The three model types serve complementary roles:
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1. **Embedding** — Retrieve relevant chunks from telecom specifications, standards, and documentation.
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2. **Reranker** — Re-score and prioritize the retrieved chunks for relevance.
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3. **LLM** — Generate accurate responses grounded in the retrieved context.
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Users can deploy the full pipeline or use individual models independently based on their needs.
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**Note:** The LLMs include abstention training — if the model does not receive sufficient context, it will decline to answer rather than hallucinate. This means the models are optimized for context-grounded generation, not open-ended question answering.
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## Related Models
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### Language Models
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- [OTel LLM Collection](https://huggingface.co/collections/farbodtavakkoli/otel-llm)
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### Embedding Models
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- [OTel Embedding Collection](https://huggingface.co/collections/farbodtavakkoli/otel-embedding)
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### Reranker Models
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- [OTel Reranker Collection](https://huggingface.co/collections/farbodtavakkoli/otel-reranker)
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## Related Datasets
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- [OTel-Embedding](https://huggingface.co/datasets/farbodtavakkoli/OTel-Embedding)
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- [OTel-Safety](https://huggingface.co/datasets/farbodtavakkoli/OTel-Safety)
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- [OTel-LLM](https://huggingface.co/datasets/farbodtavakkoli/OTel-LLM)
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- [OTel-Reranker](https://huggingface.co/datasets/farbodtavakkoli/OTel-Reranker)
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## Training Infrastructure
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- **Framework**: ScalarLM (GPU-agnostic)
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- **Compute**: AMD and NVIDIA GPUs.
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## Project Resources
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- **Project page:** https://huggingface.co/farbodtavakkoli
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- **Code:** https://github.com/farbodtavakkoli/OTel
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- **Media coverage list:** https://github.com/farbodtavakkoli/OTel/blob/main/docs/media_coverage.md
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## Citation
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```bibtex
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@misc{otel_models_2026,
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title = {OTel: Open Telco AI Datasets, Benchmarks, and Models},
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author = {Tavakkoli, Farbod and others},
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year = {2026},
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note = {Open Telco (OTel) model release},
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url = {https://huggingface.co/farbodtavakkoli}
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}
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```
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## Contact
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If you have any technical questions, please feel free to reach out to farbod.tavakkoli@att.com or farbodtavakoli@gmail.com
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added_tokens.json
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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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"_sliding_window_pattern": 6,
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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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"dtype": "bfloat16",
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"eos_token_id": 1,
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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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"layer_types": [
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"sliding_attention",
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"full_attention",
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"sliding_attention",
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"sliding_attention"
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],
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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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"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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"transformers_version": "4.57.6",
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"use_bidirectional_attention": false,
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"use_cache": false,
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"vocab_size": 262144
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}
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generation_config.json
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{
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"do_sample": true,
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"eos_token_id": [
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"pad_token_id": 0,
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"top_k": 64,
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"top_p": 0.95,
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"transformers_version": "4.57.6"
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}
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model.safetensors
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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size 1999811208
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special_tokens_map.json
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special_tokens_map.json
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{
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"boi_token": "<start_of_image>",
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"bos_token": {
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"content": "<bos>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eoi_token": "<end_of_image>",
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"eos_token": {
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"content": "<eos>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"image_token": "<image_soft_token>",
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"pad_token": {
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"content": "<pad>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"unk_token": {
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"content": "<unk>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.json
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version https://git-lfs.github.com/spec/v1
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oid sha256:4667f2089529e8e7657cfb6d1c19910ae71ff5f28aa7ab2ff2763330affad795
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size 33384568
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tokenizer.model
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tokenizer.model
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version https://git-lfs.github.com/spec/v1
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oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
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size 4689074
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tokenizer_config.json
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tokenizer_config.json
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