Tapask/telecom-oss-8b-merged

Telecom OSS/BSS Domain LLM (Merged Standalone)

Built with Meta Llama 3.

A standalone 8B model merging the `Tapask/telecom-oss-8b` LoRA adapter into its base `AliMaatouk/LLama-3-8B-Tele`. Specialised for TMF Frameworx (eTOM, SID, Open APIs) and OSS/BSS telecom operations. No PEFT runtime required — load and use like any Llama-3 model.

Two flavours of the same fine-tune:

• —Standalone (this repo) — single load, simpler for inference
• —[Adapter-only](https://huggingface.co/Tapask/telecom-oss-8b) — 670 MB, needs the base model at load time (smaller download)

Model summary

Intended use

Domain-specialised completions for:

• —TMF Open API payload generation (TMF620–TMF700 suite)
• —eTOM process decomposition (Fulfillment / Assurance / Billing end-to-end flows)
• —SID entity relationship reasoning (ProductOffering → Service → Resource hierarchies, Party/Role patterns, characteristic specifications)
• —Inventory reconciliation (discovery–inventory mismatches, phantom/orphan resources)
• —OSS/BSS architecture decisions (ODA components, build-vs-buy, MANO choices)
• —Fault-to-inventory correlation (service impact from topology traversal)
• —TMF spec Q&A (technical knowledge retrieval)
• —Integration code (TMF-compliant Python clients)

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "Tapask/telecom-oss-8b-merged"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
model.eval()

prompt = """Below is an instruction that describes a task related to telecom OSS/BSS systems, TMF Frameworx, or network operations. Write a response that appropriately completes the request.

### Instruction:
Generate a TMF641 service order payload for a 5G network slice with URLLC characteristics targeting an enterprise IoT customer.

### Response:
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
output = model.generate(**inputs, max_new_tokens=1024, temperature=0.3, do_sample=True)
print(tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Uses the Alpaca prompt template the model was trained with. Keep the ### Instruction: / ### Response: markers exactly.

Deploying with Ollama / llama.cpp

This repo is fp16 safetensors. For Ollama/llama.cpp, convert to GGUF:

git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp
pip install -r requirements/requirements-convert_hf_to_gguf.txt
python convert_hf_to_gguf.py /path/to/downloaded/telecom-oss-8b-merged \
    --outfile telecom-oss-8b.f16.gguf --outtype f16
./llama-quantize telecom-oss-8b.f16.gguf telecom-oss-8b.Q4_K_M.gguf Q4_K_M

Then create an Ollama Modelfile with the Llama-3 chat template and FROM ./telecom-oss-8b.Q4_K_M.gguf.

Training data

18,779 instruction–response pairs generated programmatically via Claude API, Kimi K2.5 on Ollama Cloud, and GLM-5 on Ollama Cloud, prompted with 8 category-specific TMF expert personas (system prompts + 4–5 batch variants each). Distribution:

Splits (seed 42): 16,901 train / 939 val / 939 test.

Quality passes applied:

• —MD5-hash deduplication on instruction field
• —Category-aware soft validators (TMF API reference presence, SID entity coverage, eTOM term coverage, JSON validity for payload categories)
• —Refusal-pattern scrubbing (I cannot, As an AI, etc. removed)
• —Type coercion for 297 pairs where source models emitted output as nested JSON objects instead of JSON strings

Evaluation loss trajectory

Loss plateaued and began ticking up after epoch 2.56 — classic mild overfitting signal. load_best_model_at_end=True ensured the merged model corresponds to the epoch 2.56 region.

Limitations

• —Synthetic data provenance — training pairs were generated by LLMs (Claude, Kimi K2.5, GLM-5) prompted with TMF expert personas. Content is stylistically consistent with TMF specs but not validated line-by-line against official TMF Open API documents. Treat outputs as starting points, not canonical.
• —Inner-JSON flaws — ~15% of category-1 pairs had minor inner-JSON issues (unescaped quotes inside payload strings). Not filtered out for training.
• —Category 8 undertrained — TMF Code Generation category ended at 74% of its 3,000-pair target due to narrow topic space and dedup loss. Code-generation quality is the weakest axis.
• —Domain scope — the model is narrow. General-purpose conversation, math, or code outside TMF integration will be no better (and often worse) than the base.
• —Standards currency — trained against TMF Open API versions current as of the prompt design (~v4/v5 dominant). May cite outdated endpoint paths for newer TMF releases.

Intended use — restrictions

Follows the Llama 3 Community License and Acceptable Use Policy. Intended for:

• —Domain research, prototyping, and educational use
• —Assistant-style answers to TMF/OSS/BSS engineering questions
• —Starter payload generation (to be reviewed before use in production)

Not suitable for:

• —Generating production systems config without human review
• —Compliance-sensitive deployments (TMF spec accuracy is not guaranteed)
• —Any of the prohibited uses in the Llama 3 AUP

License

• —Model weights: inherit Llama 3 Community License from the base model meta-llama/Meta-Llama-3-8B
• —"Built with Meta Llama 3" attribution required (see top of this card)
• —Note that Llama 3 license restricts some commercial uses (700M+ MAU clause) and prohibited use cases — consult the license before redistribution

Acknowledgements

• —Meta AI — Llama 3 base model
• —Ali Maatouk — telecom-pretrained continuation `AliMaatouk/LLama-3-8B-Tele`
• —Anthropic, Moonshot AI, Zhipu AI — Claude, Kimi K2.5, GLM-5 (used to generate synthetic training data)
• —TMForum — the eTOM, SID, and Open API standards this model targets

Citation

@misc{tapask_telecom_oss_8b_merged_2026,
  title        = {Telecom OSS/BSS Domain LLM (Merged, based on LLama-3-8B-Tele)},
  author       = {Tapas},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/Tapask/telecom-oss-8b-merged}},
}