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Model: farbodtavakkoli/OTel-LLM-1.2B-IT
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- LiquidAI/LFM2.5-1.2B-Instruct
+tags:
+- telecom
+- telecommunications
+- gsma
+- fine-tuned
+pipeline_tag: text-generation
+---
+
+# OTel-LLM-1.2B-IT
+
+**OTel-LLM-1.2B-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** | [LiquidAI/LFM2.5-1.2B-Instruct](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct) |
+| **Parameters** | 1.2B |
+| **Training Method** | Full parameter fine-tuning |
+| **Language** | English |
+| **License** | Apache 2.0 |
+
+## Training Data
+
+The model was trained on high-quality telecom-focused data curated by 100+ domain experts from organizations including AT&T, Microsoft, AMD, GSMA, RelationalAI, Essential AI, Purdue University, Khalifa University, University of Leeds, Yale University, The University of Texas at Dallas, NetoAI, and MantisNLP.
+
+**Data Sources:**
+- GSMA Permanent Reference Documents
+- 3GPP Specifications
+- O-RAN Documentation
+- RFC Series
+- eSIM, terminals, security, networks, roaming, APIs
+- Industry whitepapers and telecom academic papers
+
+## 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.
+
+## Citation
+
+```bibtex
+@misc{otel2026,
+  title={OTel: Open Telco AI Models},
+  author={Tavakkoli, Farbod and Diamos, Gregory and Paulk, Roderic and Terrazas, Jorden},
+  year={2026},
+  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

chat_template.jinja

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+{{- bos_token -}}
+{%- set keep_past_thinking = keep_past_thinking | default(false) -%}
+{%- set ns = namespace(system_prompt="") -%}
+{%- if messages[0]["role"] == "system" -%}
+    {%- set ns.system_prompt = messages[0]["content"] -%}
+    {%- set messages = messages[1:] -%}
+{%- endif -%}
+{%- if tools -%}
+    {%- set ns.system_prompt = ns.system_prompt + ("\n" if ns.system_prompt else "") + "List of tools: [" -%}
+    {%- for tool in tools -%}
+        {%- if tool is not string -%}
+            {%- set tool = tool | tojson -%}
+        {%- endif -%}
+        {%- set ns.system_prompt = ns.system_prompt + tool -%}
+        {%- if not loop.last -%}
+            {%- set ns.system_prompt = ns.system_prompt + ", " -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns.system_prompt = ns.system_prompt + "]" -%}
+{%- endif -%}
+{%- if ns.system_prompt -%}
+    {{- "<|im_start|>system\n" + ns.system_prompt + "<|im_end|>\n" -}}
+{%- endif -%}
+{%- set ns.last_assistant_index = -1 -%}
+{%- for message in messages -%}
+    {%- if message["role"] == "assistant" -%}
+        {%- set ns.last_assistant_index = loop.index0 -%}
+    {%- endif -%}
+{%- endfor -%}
+{%- for message in messages -%}
+    {{- "<|im_start|>" + message["role"] + "\n" -}}
+    {%- set content = message["content"] -%}
+    {%- if content is not string -%}
+        {%- set content = content | tojson -%}
+    {%- endif -%}
+    {%- if message["role"] == "assistant" and not keep_past_thinking and loop.index0 != ns.last_assistant_index -%}
+        {%- if "</think>" in content -%}
+            {%- set content = content.split("</think>")[-1] | trim -%}
+        {%- endif -%}
+    {%- endif -%}
+    {{- content + "<|im_end|>\n" -}}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{- "<|im_start|>assistant\n" -}}
+{%- endif -%}

config.json

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+{
+  "architectures": [
+    "Lfm2ForCausalLM"
+  ],
+  "block_auto_adjust_ff_dim": true,
+  "block_dim": 2048,
+  "block_ff_dim": 12288,
+  "block_ffn_dim_multiplier": 1.0,
+  "block_mlp_init_scale": 1.0,
+  "block_multiple_of": 256,
+  "block_norm_eps": 1e-05,
+  "block_out_init_scale": 1.0,
+  "block_use_swiglu": true,
+  "block_use_xavier_init": true,
+  "bos_token_id": 1,
+  "conv_L_cache": 3,
+  "conv_bias": false,
+  "conv_dim": 2048,
+  "conv_use_xavier_init": true,
+  "dtype": "bfloat16",
+  "eos_token_id": 7,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 12288,
+  "layer_types": [
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv",
+    "full_attention",
+    "conv"
+  ],
+  "max_position_embeddings": 128000,
+  "model_type": "lfm2",
+  "norm_eps": 1e-05,
+  "num_attention_heads": 32,
+  "num_heads": 32,
+  "num_hidden_layers": 16,
+  "num_key_value_heads": 8,
+  "pad_token_id": 0,
+  "rope_theta": 1000000.0,
+  "tie_embedding": true,
+  "transformers_version": "4.57.6",
+  "use_cache": false,
+  "use_pos_enc": true,
+  "vocab_size": 65536
+}

generation_config.json

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+{
+  "_from_model_config": true,
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": [
+    7
+  ],
+  "pad_token_id": 0,
+  "transformers_version": "4.57.6"
+}

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:bf3e6c77415507e49788f276fecc26b32c53b954c4f253c0b14e918fea4609af
+size 4681411139

special_tokens_map.json

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+{
+  "bos_token": {
+    "content": "<|startoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|pad|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}