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TSLAM-8B-L31

Telecom-Specific Large Action Model for Real-Time Intelligent Agents

Overview

TSLAM-8B-L31 is a production-ready, domain-specialized language model engineered for telecommunications operations. Built on an optimized version of Llama-3.1-8B-Instruct and fine-tuned on telecom-specific data, this model delivers SME-level expertise for real-time agent deployments, network operations, and customer support systems.

Key Capabilities:

🎯 Real-time customer support with technical accuracy
🔧 Network troubleshooting and diagnostics
📊 Service provisioning and activation workflows
🤖 Autonomous agent operations
📱 Multi-turn conversational intelligence

Model Details

Property	Value
Base Model	Llama-3.1-8B-Instruct (optimized)
Parameters	8 Billion
Context Window	128K tokens
Optimization	Flash Attention 2, BF16 precision
License	Llama 3.1 Community License

Use Cases

1. Autonomous Customer Support Agent

Deploy AI agents that handle complex customer inquiries with technical precision:

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_id = "NetoAISolutions/TSLAM-8B-L31"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

messages = [
    {
        "role": "system",
        "content": "You are an expert telecommunications support agent helping customers resolve technical issues."
    },
    {
        "role": "user",
        "content": "My 5G connection keeps dropping every few minutes. I'm using a Samsung Galaxy S23 in downtown Chicago."
    }
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt"
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    pad_token_id=tokenizer.eos_token_id
)

response = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True)
print(response)

2. Network Operations Assistant

Enable NOC teams to diagnose and resolve issues faster:

messages = [
    {
        "role": "system",
        "content": "You are a network operations expert assisting NOC engineers with diagnostics and troubleshooting."
    },
    {
        "role": "user",
        "content": "Cell tower ID 2847 is showing high RACH failures. Current RACH success rate: 73%. What should I check?"
    }
]

3. Field Technician Copilot

Provide real-time guidance for on-site installations and repairs:

messages = [
    {
        "role": "system",
        "content": "You are an expert field technician assistant providing step-by-step guidance for installations and repairs."
    },
    {
        "role": "user",
        "content": "I'm installing a small cell unit. The fiber connection is established but I'm not seeing any signal propagation. What are the likely causes?"
    }
]

4. Service Provisioning Automation

Streamline activation and configuration workflows:

messages = [
    {
        "role": "system",
        "content": "You are a service provisioning specialist helping with device activation and configuration."
    },
    {
        "role": "user",
        "content": "I need to activate VoLTE for a customer on an iPhone 14 Pro. Walk me through the provisioning checklist."
    }
]

Inference Optimization

Using Transformers Pipeline

import transformers
import torch

pipeline = transformers.pipeline(
    "text-generation",
    model="NetoAISolutions/TSLAM-8B-L31",
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto"
)

messages = [
    {"role": "system", "content": "You are a helpful telecom expert."},
    {"role": "user", "content": "Explain the difference between NSA and SA 5G deployments."}
]

outputs = pipeline(messages, max_new_tokens=256)
print(outputs[0]["generated_text"][-1]["content"])

Deployment with vLLM

For production deployments requiring high throughput:

pip install vllm

python -m vllm.entrypoints.openai.api_server \
    --model NetoAISolutions/TSLAM-8B-L31 \
    --dtype bfloat16 \
    --max-model-len 8192

Quantization with BitsAndBytes

For memory-constrained environments:

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4"
)

model = AutoModelForCausalLM.from_pretrained(
    "NetoAISolutions/TSLAM-8B-L31",
    quantization_config=quantization_config,
    device_map="auto"
)

Prompt Template

TSLAM-8B-L31 uses the standard Llama 3.1 chat template:

<|begin_of_text|><|start_header_id|>system<|end_header_id|>

You are a helpful telecom expert assistant.<|eot_id|><|start_header_id|>user<|end_header_id|>

How do I configure APN settings for LTE?<|eot_id|><|start_header_id|>assistant<|end_header_id|>

The template is automatically applied when using tokenizer.apply_chat_template().

Hardware Requirements

Deployment Type	GPU	VRAM	Precision
Full Precision	A100 40GB	40GB	BF16
Recommended	A10 / RTX 4090	24GB	BF16
Quantized (4-bit)	RTX 3090 / 4080	16GB	INT4
CPU Inference	64GB RAM	-	FP32

Limitations & Considerations

Domain Specificity: Optimized for telecommunications use cases. Performance on general tasks may vary.
Language: Primarily trained on English telecom data. Multilingual support inherits from base Llama 3.1 model.
Safety: Deploy with appropriate content filtering for production customer-facing applications.
Context: While supporting 128K context, optimal performance is observed with contexts under 8K tokens.

Responsible AI

This model should be deployed as part of a complete AI system with appropriate safeguards:

Implement content moderation for customer-facing applications
Monitor outputs for accuracy in critical operations
Maintain human oversight for network configuration changes
Follow industry compliance standards (GDPR, CCPA, etc.)

Citation

If you use TSLAM-8B-L31 in your research or applications, please cite:

@misc{tslam-8b-l31,
  title={TSLAM-8B-L31: Telecom-Specific Large Action Model},
  author={NetoAI Solutions},
  year={2025},
  publisher={HuggingFace},
  howpublished={\url{https://huggingface.co/NetoAISolutions/TSLAM-8B-L31}}
}

Community & Support

🤗 Hugging Face: NetoAISolutions
🌐 Website: netoai.ai
📧 Enterprise: [email protected]
💼 LinkedIn: NetoAI Solutions

License

Licensed under the Llama 3.1 Community License.

For commercial deployments exceeding 700M MAU, additional licensing may be required per Meta's terms.

Acknowledgments

Built on top of Meta's Llama-3.1-8B-Instruct and developed by the NetoAI Solutions team

_{Built with ❤️ by NetoAI Solutions | Empowering the Future of Telecom with AI}

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Safetensors

Model size

8B params

Tensor type

BF16