This commit removes the '-st` make target for running the converted
embedding model.
The motivation for this is that the pooling type is now part of the
.gguf metdata of the model and this is used by llama-debug when running
the model. So there is no need to specify the pooling type separately
any more.
The commit also adds an option to specify the type of normalization
applied to the output embeddings when running the converted model.
And the readme documentation has been updated to reflect these changes.
* examples : add debug utility/example
This commit introduces a new example named llama-debug which is a
utility that is intended to be used to assist with developing/debugging
a converted model.
The motivation for this utilitiy is to assist in model conversion work
to verify that the model produces the expected outputs. It is intended
to replace logits.cpp in examples/model-conversion.
Example usage:
```console
./build/bin/llama-debug \
-m models/Qwen2.5-0.5B-Instruct.gguf \
--prompt "Hello, my name is" \
--save-logits
...
Model add_bos: false
Input prompt: "Hello, my name is"
Token ids (5):
Hello(9707) ,(11) my(847) name(829) is(374)
Data saved to data/llamacpp-Qwen2.5-0.5B-Instruct.bin
Data saved to data/llamacpp-Qwen2.5-0.5B-Instruct.txt
Prompt saved to data/llamacpp-Qwen2.5-0.5B-Instruct-prompt.txt
Tokens saved to data/llamacpp-Qwen2.5-0.5B-Instruct-tokens.bin
```
For more details about the options available for this example, please
refer to examples/debug/README.md.
* throw runtime error instead of logging error
* remove params.warmup and enable the warmup/nowarmup option
* model-conversion : remove logits.cpp
This commit removes logits.cpp in favor of using llama-debug for
generating logits and embeddings.
* examples : remove model-conversion directory
This was missed in the previous commit.
* model-conversion : add support for saving prompt and token ids
This commit add support for storing the prompt and the token ids for the
prompt when running the original models.
The motivation for this is that this will allow us to compare the prompt
and the tokens generated for the prompt when verifing the converted
model. Currently it is possible that even if the same prompt is used
that the tokens generated are different if there is a difference in the
tokenization between the original and converted model which would
currently go unnoticed (the verification will most likely fail but it
might not be obvious why).
* squash! model-conversion : add support for saving prompt and token ids
fix pyright errors.
* model-conversion : add compare_tokens utility
This commit adds a script to compare token outputs between original and
converted models.
Example usage:
```console
(venv) $ ./scripts/utils/compare_tokens.py pytorch-gemma-3-270m-it llamacpp-gemma-3-270m-it-bf16
Comparing tokens between:
Original : pytorch-gemma-3-270m-it (6 tokens)
Converted: llamacpp-gemma-3-270m-it-bf16 (6 tokens)
✅ All 6 tokens match!
```
And there is a verbose flag that will also print out the prompts:
```console
(venv) $ ./scripts/utils/compare_tokens.py pytorch-gemma-3-270m-it llamacpp-gemma-3-270m-it-bf16 -v
Original model prompt (pytorch-gemma-3-270m-it):
prompt: Hello, my name is
n_tokens: 6
token ids: 2, 9259, 236764, 1041, 1463, 563
Converted model prompt (llamacpp-gemma-3-270m-it-bf16):
prompt: Hello, my name is
n_tokens: 6
token ids: 2, 9259, 236764, 1041, 1463, 563
Comparing tokens between:
Original : pytorch-gemma-3-270m-it (6 tokens)
Converted: llamacpp-gemma-3-270m-it-bf16 (6 tokens)
✅ All 6 tokens match!
```
* model-conversion : add token comparison to verifiction scripts
This commit add the calling of the compare_tokens function in
compare-logits.py and semantic_check.py to ensure that the token ids
that the tokenizers procoduce are the same before proceeding with
verifying the logits/embeddings.
Placing them in the existing scripts instead calling them separately
ensures that the token comparison is always done prior to the
logit/embedding verifications.
Follow up commit/pr could refactor the causal logits verification into
a single script instead of the two that exist now. This would reduce the
code and make it consistent with the embeddings verficiation which only
has a single script.
* debug : use llama_model_n_embd_out
This commit updates the debug example to use the new function
llama_model_n_embd_out instead of llama_model_n_embd.
The motivation for this change is to support late interation retriever
models, like LFM2-ColBert-350M, where the output embeddings are down
projected to a lower dimension.
* debug : add print_usage function
This commit adds a print_usage function that is passed to the
common_params_parse.
The motivation for this is that this enables a specific usage message
which will be printed after all the options, for example:
```console
example usage:
Print tensors:
./build/bin/llama-debug -m model.gguf -p "Hello my name is" --verbose
The tensors to be printed can be filtered with --tensor-filter option.
Save logits/embeddings:
./build/bin/llama-debug -m model.gguf -p "Hello my name is" --save-logits
Add --embedding to save embeddings
```
* model-conversion : add support for SentenceTransformers
This commit adds support for models that use SentenceTransformer layers.
The motivation for this is that if converted model includes any of the
numbered layers specified in the original models repository then these
changes enable these models to be used and verified. Currently the
model-conversion only support the base model output without any of
the additional transformation layers.
Usage:
Convert the model that also includes the SentenceTransformer layers:
```console
(venv) $ export EMBEDDING_MODEL_PATH="~/google/embeddinggemma-300M"
(venv) make embedding-convert-model
```
Verify the produced embeddings from the converted model against the
original model embeddings:
```console
(venv) make embedding-verify-logits-st
```
The original model can be run using SentenceTransformer:
```console
(venv) make embedding-run-original-model-st
```
Run the converted model using "SentenceTransformer" layers whic
enables pooling and normalization:
```console
(venv) make embedding-run-converted-model-st
```
* add model-conversion example requirements
* add support for -st flag in embedding model conversion
This commit add support for the -st flag in the embedding model
conversion script. This will enable models to be converted using
sentence transformers dense layers.
This commit adds support for passing a prompt file to the model
conversion targets/scripts. It also updates the logits.cpp to print out
embedding information in the same format as when running the original
embedding model.
The motivation for this is that it allows us to pass files of different
sizes when running the converted models and validating the logits.
This can be particularly important when testing the sliding window
functionality of models where the sequence length needs to exceed a
certain number of tokens to trigger the sliding window logic.
* model-conversion : remove hardcoded /bin/bash shebangs [no ci]
This commit updates the bash scripts to use env instead of using
hardcoded /bin/bash in the shebang line.
The motivation for this is that some systems may have bash installed
in a different location, and using /usr/bin/env bash ensures that
the script will use the first bash interpreter found in the user's
PATH, making the scripts more portable across different environments.
* model-conversion : rename script to .py [no ci]
This commit renames run-casual-gen-embeddings-org.sh to
run-casual-gen-embeddings-org.py to reflect its Python nature.
* examples : add model conversion tool/example
This commit adds an "example/tool" that is intended to help in the
process of converting models to GGUF. Currently it supports normal
causal models and embedding models. The readme contains instructions and
command to guide through the process.
The motivation for this to have a structured and repeatable process for
model conversions and hopefully with time improve upon it to make the
process easier and more reliable. We have started to use this for new
model conversions internally and will continue doing so and improve it
as we go along. Perhaps with time this should be placed in a different
directory than the examples directory, but for now it seems like a good
place to keep it while we are still developing it.
* squash! examples : add model conversion tool/example
Remove dependency on scikit-learn in model conversion example.
* squash! examples : add model conversion tool/example
Update transformer dep to use non-dev version. And also import
`AutoModelForCausalLM` instead of `AutoModel` to ensure compatibility
with the latest version.
* squash! examples : add model conversion tool/example
Remove the logits requirements file from the all requirements file.
Daniel Bevenius