Text Generation
PyTorch
English
lfm2_moe
telecom
telecommunications
gsma
rag
full-parameter-fine-tuning
fine-tuned
conversational
Instructions to use farbodtavakkoli/OTel-LLM-24B-IT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Inference
Update model card for OTel-LLM-24B-IT
Browse files
README.md
CHANGED
|
@@ -151,7 +151,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
| 151 |
|
| 152 |
- OTel models are domain-specific to telecommunications and should not be treated as general-purpose models.
|
| 153 |
- The current release is English-only and primarily text-centric.
|
| 154 |
-
-
|
| 155 |
- Aggregate scores can hide subdomain variation; collaborator stress tests suggest O-RAN retrieval is comparatively strong, while academic-paper and GSMA PRD examples need further curation.
|
| 156 |
- Generated telecom content should be verified before operational, customer-facing, regulatory, safety, or network-configuration use.
|
| 157 |
- Users must comply with both the OTel release license and the upstream base-model license or terms.
|
|
|
|
| 151 |
|
| 152 |
- OTel models are domain-specific to telecommunications and should not be treated as general-purpose models.
|
| 153 |
- The current release is English-only and primarily text-centric.
|
| 154 |
+
- The reported OTel performance results use held-out OTel evaluation partitions and should not be interpreted as results from a fully independent external benchmark suite.
|
| 155 |
- Aggregate scores can hide subdomain variation; collaborator stress tests suggest O-RAN retrieval is comparatively strong, while academic-paper and GSMA PRD examples need further curation.
|
| 156 |
- Generated telecom content should be verified before operational, customer-facing, regulatory, safety, or network-configuration use.
|
| 157 |
- Users must comply with both the OTel release license and the upstream base-model license or terms.
|