TSLAM-Mini-2B

Base Model: microsoft/Phi-4-mini-instruct
License: MIT

Overview

TSLAM-Mini-2B is a domain-adapted language model fine-tuned on 100,000 telecom-specific examples, designed to emulate the intelligence and conversational expertise of a Telecom Subject Matter Expert (SME). Built on top of the Phi-4-mini-instruct foundation, TSLAM-Mini-2B is optimized for real-time, industry-grade interactions across key telecom scenarios, including:

SME-style responses in customer support and internal queries
Network configuration, diagnostics, and troubleshooting workflows
Device provisioning and service activation dialogues
Operational support for field and NOC teams
Intelligent retrieval and summarization of telecom-specific documentation

This fine-tuning strategy enables TSLAM-Mini-2B to reason like an SME, offering accurate, context-aware responses that align with real-world telecom operations. Though this model offers superior performance on Telecom specific usecases, For enterprises requiring specialized capabilities please contact us [email protected] for our enterprise grade commercial models which offers greater capabilities required for production.

Key Features

Telecom-Tuned: Finetuned on domain-specific conversations, logs, and structured dialogues.
Instruction-Following: Retains Phi-4’s compact instruction-tuned behavior while adapting to industry-specific patterns.
Real-Time Scenarios: Performs well in use cases that require contextual understanding of real-world telecom operations.

Intended Use

The areas TSLAM-Mini-2B excels in are:

Customer Support Agents (AI copilots or chatbots)
Network Operations Tools that process commands or log queries
Internal Assistants for engineers and field technicians
Telecom Knowledge Graphs & RAG Pipelines

Model Details

Property	Value
Base Model	`microsoft/Phi-4-mini-instruct`
Fine-tuning Data	100k telecom domain examples
Training Method	Supervised fine-tuning (SFT)
License	MIT

Benchmarks Results

Benchmark	TSLAM-Mini-2B	Phi-3.5-mini-Ins	Llama-3.2-3B-Ins	Mistral-3B	Qwen2.5-3B-Ins	Qwen2.5-7B-Ins	Mistral-8B-2410	Llama-3.1-8B-Ins	Llama-3.1-Tulu-3-8B	Gemma2-9B-Ins	GPT-4o-mini-2024-07-18
Popular aggregated benchmark
Arena Hard	32.8	34.4	17.0	26.9	32.0	55.5	37.3	25.7	42.7	43.7	53.7
BigBench Hard (0-shot, CoT)	70.4	63.1	55.4	51.2	56.2	72.4	53.3	63.4	55.5	65.7	80.4
MMLU (5-shot)	67.3	65.5	61.8	60.8	65.0	72.6	63.0	68.1	65.0	71.3	77.2
MMLU-Pro (0-shot, CoT)	52.8	47.4	39.2	35.3	44.7	56.2	36.6	44.0	40.9	50.1	62.8
Reasoning
ARC Challenge (10-shot)	83.7	84.6	76.1	80.3	82.6	90.1	82.7	83.1	79.4	89.8	93.5
BoolQ (2-shot)	81.2	77.7	71.4	79.4	65.4	80.0	80.5	82.8	79.3	85.7	88.7
GPQA (0-shot, CoT)	25.2	26.6	24.3	24.4	23.4	30.6	26.3	26.3	29.9	39.1	41.1
HellaSwag (5-shot)	69.1	72.2	77.2	74.6	74.6	80.0	73.5	72.8	80.9	87.1	88.7
OpenBookQA (10-shot)	79.2	81.2	72.6	79.8	79.3	82.6	80.2	84.8	79.8	90.0	90.0
PIQA (5-shot)	77.6	78.2	68.2	73.2	72.6	76.2	81.2	83.2	78.3	83.7	88.7
Social IQA (5-shot)	72.5	75.1	68.3	73.9	75.3	75.3	77.6	71.8	73.4	74.7	82.9
TruthfulQA (MC2) (10-shot)	66.4	65.2	59.2	62.9	64.3	69.4	63.0	69.2	64.1	76.6	78.2
Winogrande (5-shot)	67.0	72.2	53.2	59.8	63.3	71.1	63.1	64.7	65.4	74.0	76.9
Multilingual
Multilingual MMLU (5-shot)	49.3	51.8	48.1	46.4	55.9	64.4	53.7	56.2	54.5	63.8	72.9
MGSM (0-shot, CoT)	63.9	49.6	44.6	44.6	53.5	64.5	56.7	56.7	58.6	75.1	81.7
Math
GSM8K (8-shot, CoT)	88.6	76.9	75.6	80.1	80.6	88.7	81.9	82.4	84.3	84.9	91.3
MATH (0-shot, CoT)	64.0	49.8	46.7	41.8	61.7	60.4	41.6	47.6	46.1	51.3	70.2
Telecom (domain-specific)	88.2	52.1	47.6	49.3	58.0	61.5	54.9	57.3	59.0	64.1	70.3
Overall	63.5	60.5	56.2	56.9	60.1	67.9	60.2	62.3	60.9	65.0	75.5

Example

**User**: How do I reconfigure a 5G core node remotely?

**Model**: To reconfigure a 5G core node remotely, ensure you have SSH access enabled and the necessary configuration scripts preloaded. From your NOC terminal, run the secure update command with the node's IP and authentication key...

How to Use

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load tokenizer and model
model_name = "NetoAISolutions/TSLAM-Mini-2B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

# Define the input using the Phi-style chat template
def build_prompt(user_input):
    system = "<|system|>\nYou are a helpful assistant.<|end|>\n"
    user = f"<|user|>\n{user_input}<|end|>\n"
    assistant = "<|assistant|>\n"  # Start assistant response
    return system + user + assistant

# Example input
user_query = "How do I activate VoLTE on a user's device?"
prompt = build_prompt(user_query)

# Tokenize and generate
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(
    **inputs,
    max_new_tokens=200,
    temperature=0.7,
    top_p=0.9,
    do_sample=True,
    eos_token_id=tokenizer.convert_tokens_to_ids("<|end|>")
)

# Decode output
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Acknowledgements

Built on top of Microsoft’s Phi-4-mini-instruct
Data curation and tuning by NetoAISolutions

NetoAISolutions
/

TSLAM-Mini-2B

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