# What does this PR do?
[Provide a short summary of what this PR does and why. Link to relevant
issues if applicable.]
3783861877
```
diff --git a/docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb b/docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb
index c55c8da..3979088 100644
--- a/docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb
+++ b/docs/notebooks/Alpha_Llama_Stack_Post_Training.ipynb
@@ -6431,4 +6431,4 @@
},
"nbformat": 4,
"nbformat_minor": 0
-}
\ No newline at end of file
+}
Error: Process completed with exit code 1.
```
[//]: # (If resolving an issue, uncomment and update the line below)
[//]: # (Closes #[issue-number])
## Test Plan
[Describe the tests you ran to verify your changes with result
summaries. *Provide clear instructions so the plan can be easily
re-executed.*]
[//]: # (## Documentation)
---------
Signed-off-by: reidliu <reid201711@gmail.com>
Co-authored-by: reidliu <reid201711@gmail.com>
## context
Now, in llama stack, we only support inference / eval a finetuned
checkpoint with meta-reference as inference provider. This is
sub-optimal since meta-reference is pretty slow.
Our vision is that developer can inference / eval a finetuned checkpoint
produced by post training apis with all the inference providers on the
stack. To achieve this, we'd like to define an unified output checkpoint
format for post training providers. So that, all the inference provider
can respect that format for customized model inference.
By spotting check how
[ollama](https://github.com/ollama/ollama/blob/main/docs/import.md) and
[fireworks](https://docs.fireworks.ai/models/uploading-custom-models) do
inference on a customized model, we defined the output checkpoint format
as /adapter/adapter_config.json and /adapter/adapter_model.safetensors
(as we only support LoRA post training now, we begin from adapter only
checkpoint)
## test
we kick off a post training job and configured checkpoint format as
'huggingface'. Output files
we did a proof of concept with ollama to see if ollama can inference our
finetuned checkpoint
1. create Modelfile like
<img width="799" alt="Screenshot 2025-01-22 at 5 04 18 PM"
src="https://github.com/user-attachments/assets/7fca9ac3-a294-44f8-aab1-83852c600609"
/>
2. create a customized model with `ollama create llama_3_2_finetuned`
and run inference successfully
This is just a proof of concept with ollama cmd line. As next step, we'd
like to wrap loading / inference customized model logic in the inference
provider implementation.
