Shirodora233

gemma4-e2b-repomind-qlora-synth-v2-pilot

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Intended Use

The adapter is intended for controlled research and evaluation of repo-local symbol-level call edge extraction. It was trained on synthetic call-chain samples only. AstrBot and Scrapy real evaluation cases were not mixed into the training set.

This is a pilot adapter, not a final production model. Synthetic dev loss improved strongly, but real-case evaluation did not show a net gain over the previous adapter.

Training Summary

Base model: google/gemma-4-E2B-it
Dataset: full-synthetic-augmented-v2-20260621
Dataset SHA256: 09601acfabebab623f543d76366b186f2da0bbc9143a69ffaff00e476802781a
Train/dev samples: 400 / 100
Max sequence length: 512
Steps: 100
LoRA targets: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
Excluded modules: regex:.*(vision_tower|audio_tower).*
Adapter SHA256: 8ef422a6eeb142d657da41deeb41abae9e8ca3e7f11f5fb1605b0b6af845b2c7

Synthetic dev loss:

Table with columns: Step, Eval loss
Step	Eval loss
initial	2.231752
20	0.453445
40	0.300186
60	0.245931
80	0.206748
100	0.193902

Overfit monitor assessment: no_overfit_signal_eval_loss_decreased.

Real-Case Evaluation

Four real repository cases were evaluated with the same Oracle Context prompt:

Table with columns: Variant, Precision, Recall, Evidence accuracy
Variant	Precision	Recall	Evidence accuracy
base	n/a	0.000	n/a
v1 adapter	0.750	0.250	0.667
v2 adapter	0.750	0.250	0.333

Conclusion: the v2 adapter improved synthetic dev loss but did not produce a net improvement on the 4-case real-repo smoke test. Use it as an experimental checkpoint.

Loading Example

python
import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

adapter_id = "Shirodora233/gemma4-e2b-repomind-qlora-synth-v2-pilot"

tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
model = AutoPeftModelForCausalLM.from_pretrained(
    adapter_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "system",
        "content": (
            "Return repo-local symbol-level call edges as strict JSON. "
            "Use fully qualified symbols, include file/line/evidence for every call edge."
        ),
    },
    {
        "role": "user",
        "content": "{\"case_id\":\"example\",\"question\":\"Find repo-local call edges.\"}",
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=768,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
    )

generated = output_ids[0, inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))

Included Files

adapter_model.safetensors
adapter_config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja
metadata/training_summary.json
metadata/overfit_monitor.json
metadata/run_config.json
metadata/environment_snapshot.json

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Shirodora233

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google/gemma-4-E2B-it

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Intended Use

This is a pilot adapter, not a final production model. Synthetic dev loss improved strongly, but real-case evaluation did not show a net gain over the previous adapter.

Training Summary

Base model: google/gemma-4-E2B-it
Dataset: full-synthetic-augmented-v2-20260621
Dataset SHA256: 09601acfabebab623f543d76366b186f2da0bbc9143a69ffaff00e476802781a
Train/dev samples: 400 / 100
Max sequence length: 512
Steps: 100
LoRA targets: q_proj,k_proj,v_proj,o_proj,gate_proj,up_proj,down_proj
Excluded modules: regex:.*(vision_tower|audio_tower).*
Adapter SHA256: 8ef422a6eeb142d657da41deeb41abae9e8ca3e7f11f5fb1605b0b6af845b2c7

Synthetic dev loss:

Table with columns: Step, Eval loss
Step	Eval loss
initial	2.231752
20	0.453445
40	0.300186
60	0.245931
80	0.206748
100	0.193902

Overfit monitor assessment: no_overfit_signal_eval_loss_decreased.

Real-Case Evaluation

Four real repository cases were evaluated with the same Oracle Context prompt:

Table with columns: Variant, Precision, Recall, Evidence accuracy
Variant	Precision	Recall	Evidence accuracy
base	n/a	0.000	n/a
v1 adapter	0.750	0.250	0.667
v2 adapter	0.750	0.250	0.333

Conclusion: the v2 adapter improved synthetic dev loss but did not produce a net improvement on the 4-case real-repo smoke test. Use it as an experimental checkpoint.

Loading Example

python
import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

adapter_id = "Shirodora233/gemma4-e2b-repomind-qlora-synth-v2-pilot"

tokenizer = AutoTokenizer.from_pretrained(adapter_id, trust_remote_code=True)
model = AutoPeftModelForCausalLM.from_pretrained(
    adapter_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {
        "role": "system",
        "content": (
            "Return repo-local symbol-level call edges as strict JSON. "
            "Use fully qualified symbols, include file/line/evidence for every call edge."
        ),
    },
    {
        "role": "user",
        "content": "{\"case_id\":\"example\",\"question\":\"Find repo-local call edges.\"}",
    },
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
    return_dict=True,
).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=768,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id,
    )

generated = output_ids[0, inputs["input_ids"].shape[-1]:]
print(tokenizer.decode(generated, skip_special_tokens=True))

Included Files

adapter_model.safetensors
adapter_config.json
tokenizer.json
tokenizer_config.json
chat_template.jinja
metadata/training_summary.json
metadata/overfit_monitor.json
metadata/run_config.json
metadata/environment_snapshot.json

gemma4-e2b-repomind-qlora-synth-v2-pilot

Get help setting up a custom Dedicated Endpoints.

README

Intended Use

Training Summary

Real-Case Evaluation

Loading Example

Included Files

Explore FriendliAI today

README

Intended Use

Training Summary

Real-Case Evaluation

Loading Example

Included Files