TSLAM-15B: Telecom-Specific Large Action Model

TSLAM-15B is a 15-billion parameter, cutting-edge language model developed by NetoAI Solutions Pvt. Ltd., tailored explicitly for the telecommunications industry. This model is a fine-tuned variant of the powerful Mixture-of-Experts (MoE) Qwen3-30B-A3B-Instruct-2507 model, optimized for telecom domain expertise, advanced reasoning, and action-oriented workflows.

License

This model is fully owned by NetoAI, contact us at [email protected] for access and commercial usage license.

Model Architecture and Benefits

TSLAM-15B builds on the Qwen3-30B-A3B-Instruct-2507 Mixture-of-Experts (MoE) model, which features:

Efficiency and Speed: TSLAM-15B, being a 4-bit quantized model provides enterprise level performance while maintaining a smaller size that can be run on a single Nvidia A100 GPU
Long Context Window: Supports long sequences of upto 256,000 tokens, enabling comprehensive multi-turn dialogs and large document analysis.
Robust Telecom Language Understanding: Fine-tuned on proprietary telecom datasets, including protocols (3GPP, IETF), technical manuals, operational logs, and customer interactions.

Key Features

Telecom-Domain Expertise: Specialized knowledge from telecom datasets for accurate domain-specific responses.
Action-Oriented Outputs: Can suggest configurations, troubleshoot faults, automate network operations, and generate technical documentation.
Large Context Window (256K tokens): Enables analysis of long conversations, extended reports, and multi-document reasoning.
Enterprise-Grade Deployment: Designed to operate efficiently in demanding environments with real-time constraints.

Use Cases

TSLAM-15B is ideal for a range of telecom industry applications:

Network Troubleshooting & Diagnostics
Automated Configuration Generation and Validation (BGP, OSPF, QoS, etc.)
Technical Customer Support Chatbots
RF Network Planning and Capacity Management
Regulatory Compliance Support
Technical Documentation Generation and Summarization

Model Evaluation & Performance

Demonstrates improved telecom-specific reasoning and generation quality compared to baseline Qwen models.
Maintains low latency inference with 4-bit quantization and MoE efficiency.
Effectively handles extremely long contexts critical for telecom workflows.

Prerequisites

To use TSLAM-15B you need to have the following: -Python >=3.10 -Pytorch -Transformers library >=4.51.0

Example Code Snippet to use TSLAM-15B

For inference you can import and use the TSLAM-15B model directly with the Transformers library:

import torch
from huggingface_hub import login
from transformers import AutoModelForCausalLM,AutoTokenizer
import time

# Login to Hugging Face
hf_token = "YOUR HF TOKEN"
login(token=hf_token)

# Model and tokenizer setup
model_name = "NetoAISolutions/TSLAM-15B"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    trust_remote_code=True,
).to("cuda")

# Prepare the prompt
prompt = "How is QOS applied to routers"
messages = [
    {"role": "system", "content": "You are a helpful assistant that is an expert in the telecom domain."},
    {"role": "user", "content": prompt}
]
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

# Tokenize input
inputs = tokenizer(input_text, return_tensors="pt").to("cuda")

# Generate response
start_time = time.time() #track time to check inference times
outputs = model.generate(
    **inputs,
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    eos_token_id=tokenizer.eos_token_id
)
inf_time = time.time()-start_time

output_ids = outputs[0][len(inputs.input_ids[0]):].tolist()

# Decode and print response
response = tokenizer.decode(output_ids, skip_special_tokens=True)
print(f"Time taken for inference: {inf_time}\n")
print("--------------------------------------------------------------------------------")
print("MODEL RESPONSE:\n")
print(response)

NetoAISolutions
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TSLAM-15B