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Model: farbodtavakkoli/OTel-LLM-1B-IT
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- google/gemma-3-1b-it
+tags:
+- telecom
+- telecommunications
+- gsma
+- fine-tuned
+pipeline_tag: text-generation
+---
+
+# OTel-LLM-1B-IT
+
+**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.
+
+## Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Base Model** | [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) |
+| **Parameters** | 1B |
+| **Training Method** | Full parameter fine-tuning |
+| **Language** | English |
+| **License** | Apache 2.0 |
+
+## Training Data
+
+The model was trained on telecom-focused data curated by 100+ domain experts. Each source class was contributed by a specific institutional partner:
+
+| Source | Contributor |
+|---|---|
+| arXiv telecom papers, 3GPP standards, telecom Wikipedia, telecom Common Crawl | Yale University |
+| GSMA Permanent Reference Documents, Discover portal | GSMA |
+| IETF RFC series | NetoAI |
+| Industry whitepapers | Khalifa University |
+| O-RAN specifications (working groups 1, 2, 4, 5, 6, 7, 8, 9, 10) | University of Leeds |
+| O-RAN documents across working groups | The University of Texas at Dallas |
+
+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).
+
+## Intended Use
+ 
+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:
+ 
+1. **Embedding** — Retrieve relevant chunks from telecom specifications, standards, and documentation.
+2. **Reranker** — Re-score and prioritize the retrieved chunks for relevance.
+3. **LLM** — Generate accurate responses grounded in the retrieved context.
+ 
+Users can deploy the full pipeline or use individual models independently based on their needs.
+ 
+**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.
+
+## Related Models
+
+### Language Models
+- [OTel LLM Collection](https://huggingface.co/collections/farbodtavakkoli/otel-llm)
+
+### Embedding Models
+- [OTel Embedding Collection](https://huggingface.co/collections/farbodtavakkoli/otel-embedding)
+
+### Reranker Models
+- [OTel Reranker Collection](https://huggingface.co/collections/farbodtavakkoli/otel-reranker)
+
+## Related Datasets
+
+- [OTel-Embedding](https://huggingface.co/datasets/farbodtavakkoli/OTel-Embedding)
+- [OTel-Safety](https://huggingface.co/datasets/farbodtavakkoli/OTel-Safety)
+- [OTel-LLM](https://huggingface.co/datasets/farbodtavakkoli/OTel-LLM)
+- [OTel-Reranker](https://huggingface.co/datasets/farbodtavakkoli/OTel-Reranker)
+
+## Training Infrastructure
+
+- **Framework**: ScalarLM (GPU-agnostic)
+- **Compute**: AMD and NVIDIA GPUs.
+
+## Project Resources
+
+- **Project page:** https://huggingface.co/farbodtavakkoli
+- **Code:** https://github.com/farbodtavakkoli/OTel
+- **Media coverage list:** https://github.com/farbodtavakkoli/OTel/blob/main/docs/media_coverage.md
+
+## Citation
+
+
+```bibtex
+@misc{otel_models_2026,
+  title  = {OTel: Open Telco AI Datasets, Benchmarks, and Models},
+  author = {Tavakkoli, Farbod and others},
+  year   = {2026},
+  note   = {Open Telco (OTel) model release},
+  url    = {https://huggingface.co/farbodtavakkoli}
+}
+```
+
+## Contact
+
+If you have any technical questions, please feel free to reach out to farbod.tavakkoli@att.com or farbodtavakoli@gmail.com

added_tokens.json

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+{
+  "<image_soft_token>": 262144
+}

chat_template.jinja

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+{{ bos_token }}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- if messages[0]['content'] is string -%}
+        {%- set first_user_prefix = messages[0]['content'] + '
+
+' -%}
+    {%- else -%}
+        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
+
+' -%}
+    {%- endif -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = "" -%}
+    {%- set loop_messages = messages -%}
+{%- endif -%}
+{%- for message in loop_messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
+    {%- endif -%}
+    {%- if (message['role'] == 'assistant') -%}
+        {%- set role = "model" -%}
+    {%- else -%}
+        {%- set role = message['role'] -%}
+    {%- endif -%}
+    {{ '<start_of_turn>' + role + '
+' + (first_user_prefix if loop.first else "") }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ raise_exception("Invalid content type") }}
+    {%- endif -%}
+    {{ '<end_of_turn>
+' }}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{'<start_of_turn>model
+'}}
+{%- endif -%}

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "dtype": "bfloat16",
+  "eos_token_id": 1,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 1152,
+  "initializer_range": 0.02,
+  "intermediate_size": 6912,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 26,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000,
+  "rope_scaling": null,
+  "rope_theta": 1000000,
+  "sliding_window": 512,
+  "sliding_window_pattern": 6,
+  "transformers_version": "4.57.6",
+  "use_bidirectional_attention": false,
+  "use_cache": false,
+  "vocab_size": 262144
+}

generation_config.json

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+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    1,
+    106
+  ],
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.57.6"
+}

model.safetensors

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special_tokens_map.json

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+{
+  "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
+  }
+}

tokenizer.json

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tokenizer.model

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