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Model: Tapask/telecom-oss-8b-merged Source: Original Platform
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---
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base_model: AliMaatouk/LLama-3-8B-Tele
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license: llama3
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language:
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- en
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tags:
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- telecom
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- oss
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- bss
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- tmf
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- tmforum
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- etom
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- sid
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- llama-3
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- merged
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Telecom OSS/BSS Domain LLM (Merged Standalone)
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**Built with Meta Llama 3.**
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A standalone 8B model merging the [`Tapask/telecom-oss-8b`](https://huggingface.co/Tapask/telecom-oss-8b) LoRA adapter into its base [`AliMaatouk/LLama-3-8B-Tele`](https://huggingface.co/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.
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Two flavours of the same fine-tune:
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- **Standalone (this repo)** — single load, simpler for inference
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- **[Adapter-only](https://huggingface.co/Tapask/telecom-oss-8b)** — 670 MB, needs the base model at load time (smaller download)
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## Model summary
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| | |
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|---|---|
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| **Architecture** | Llama-3 8B (transformers-native, fp16 safetensors) |
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| **Origin** | `AliMaatouk/LLama-3-8B-Tele` + QLoRA fine-tune (r=64, α=128, dropout=0.05) |
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| **Fine-tune target modules** | `q_proj`, `k_proj`, `v_proj`, `o_proj`, `gate_proj`, `up_proj`, `down_proj` |
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| **Training data** | 18,779 synthetic instruction–response pairs across 8 TMF-aligned categories |
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| **Training config** | 3 epochs · effective batch 16 · seq 4096 · cosine LR (peak 2e-4) · bf16 · gradient checkpointing |
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| **Training hardware** | NVIDIA A100 SXM4 80GB · ~8.3 h wall time |
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| **Best eval loss** | **0.8438** (epoch 2.56) — `load_best_model_at_end=True` |
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| **Sharded safetensors** | 5 × ~3-4 GB files (~16.1 GB total) |
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## Intended use
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Domain-specialised completions for:
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- **TMF Open API** payload generation (TMF620–TMF700 suite)
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- **eTOM** process decomposition (Fulfillment / Assurance / Billing end-to-end flows)
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- **SID** entity relationship reasoning (ProductOffering → Service → Resource hierarchies, Party/Role patterns, characteristic specifications)
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- **Inventory reconciliation** (discovery–inventory mismatches, phantom/orphan resources)
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- **OSS/BSS architecture** decisions (ODA components, build-vs-buy, MANO choices)
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- **Fault-to-inventory correlation** (service impact from topology traversal)
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- **TMF spec Q&A** (technical knowledge retrieval)
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- **Integration code** (TMF-compliant Python clients)
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### How to use
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "Tapask/telecom-oss-8b-merged"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")
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model.eval()
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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.
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### Instruction:
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Generate a TMF641 service order payload for a 5G network slice with URLLC characteristics targeting an enterprise IoT customer.
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### Response:
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"""
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inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
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output = model.generate(**inputs, max_new_tokens=1024, temperature=0.3, do_sample=True)
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print(tokenizer.decode(output[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
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```
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Uses the **Alpaca prompt template** the model was trained with. Keep the `### Instruction: / ### Response:` markers exactly.
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### Deploying with Ollama / llama.cpp
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This repo is fp16 safetensors. For Ollama/llama.cpp, convert to GGUF:
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```bash
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git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp
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pip install -r requirements/requirements-convert_hf_to_gguf.txt
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python convert_hf_to_gguf.py /path/to/downloaded/telecom-oss-8b-merged \
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--outfile telecom-oss-8b.f16.gguf --outtype f16
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./llama-quantize telecom-oss-8b.f16.gguf telecom-oss-8b.Q4_K_M.gguf Q4_K_M
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```
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Then create an Ollama Modelfile with the Llama-3 chat template and `FROM ./telecom-oss-8b.Q4_K_M.gguf`.
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## Training data
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18,779 instruction–response pairs generated programmatically via [Claude API](https://www.anthropic.com/), [Kimi K2.5 on Ollama Cloud](https://ollama.com/), and [GLM-5 on Ollama Cloud](https://ollama.com/), prompted with 8 category-specific TMF expert personas (system prompts + 4–5 batch variants each). Distribution:
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| # | Category | Pairs | Primary model |
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|---|---|---:|---|
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| 1 | TMF Open API Payloads | 2,962 | GLM-5 |
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| 2 | eTOM Process Decomposition | 1,967 | GLM-5 |
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| 3 | SID Entity Reasoning | 1,963 | Kimi K2.5 |
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| 4 | Inventory Reconciliation | 2,962 | Kimi K2.5 |
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| 5 | OSS/BSS Architecture | 1,893 | Kimi K2.5 |
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| 6 | Fault-to-Inventory Correlation | 1,929 | GLM-5 |
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| 7 | TMF Spec Q&A | 2,875 | Kimi K2.5 (after GLM-5 hit 54% dedup rate) |
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| 8 | TMF Integration Code Generation | 2,228 | GLM-5 |
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Splits (seed 42): **16,901 train / 939 val / 939 test.**
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Quality passes applied:
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- MD5-hash deduplication on `instruction` field
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- Category-aware soft validators (TMF API reference presence, SID entity coverage, eTOM term coverage, JSON validity for payload categories)
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- Refusal-pattern scrubbing (`I cannot`, `As an AI`, etc. removed)
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- Type coercion for 297 pairs where source models emitted `output` as nested JSON objects instead of JSON strings
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## Evaluation loss trajectory
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| Epoch | Eval loss |
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|---|---|
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| 2.27 | 0.8545 |
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| 2.37 | 0.8440 |
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| 2.46 | 0.8447 |
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| **2.56** | **0.8438** ← best, used for merge |
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| 2.65 | 0.8479 |
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| 2.75 | 0.8478 |
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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.
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## Limitations
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- **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.
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- **Inner-JSON flaws** — ~15% of category-1 pairs had minor inner-JSON issues (unescaped quotes inside payload strings). Not filtered out for training.
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- **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.
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- **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.
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- **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.
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## Intended use — restrictions
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Follows the [Llama 3 Community License](https://llama.meta.com/llama3/license/) and [Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/). Intended for:
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- Domain research, prototyping, and educational use
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- Assistant-style answers to TMF/OSS/BSS engineering questions
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- Starter payload generation (to be reviewed before use in production)
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Not suitable for:
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- Generating production systems config without human review
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- Compliance-sensitive deployments (TMF spec accuracy is not guaranteed)
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- Any of the prohibited uses in the Llama 3 AUP
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## License
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- Model weights: inherit **Llama 3 Community License** from the base model `meta-llama/Meta-Llama-3-8B`
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- "Built with Meta Llama 3" attribution required (see top of this card)
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- Note that Llama 3 license restricts some commercial uses (700M+ MAU clause) and prohibited use cases — consult the license before redistribution
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## Acknowledgements
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- **Meta AI** — Llama 3 base model
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- **Ali Maatouk** — telecom-pretrained continuation [`AliMaatouk/LLama-3-8B-Tele`](https://huggingface.co/AliMaatouk/LLama-3-8B-Tele)
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- **Anthropic, Moonshot AI, Zhipu AI** — Claude, Kimi K2.5, GLM-5 (used to generate synthetic training data)
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- **TMForum** — the eTOM, SID, and Open API standards this model targets
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## Citation
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```
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@misc{tapask_telecom_oss_8b_merged_2026,
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title = {Telecom OSS/BSS Domain LLM (Merged, based on LLama-3-8B-Tele)},
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author = {Tapas},
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year = {2026},
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howpublished = {\url{https://huggingface.co/Tapask/telecom-oss-8b-merged}},
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}
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```
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"mlp_bias": false,
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"model_type": "llama",
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"rope_scaling": null,
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"torch_dtype": "float16",
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"transformers_version": "4.49.0",
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"use_cache": false,
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"vocab_size": 128256
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}
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
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||||
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|
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||||
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|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:3c5cf44023714fb39b05e71e425f8d7b92805ff73f7988b083b8c87f0bf87393
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||||
size 17209961
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||||
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user