naksyu

gemma4-e4b-diagnostic-v2-fft100

Model Summary

Base checkpoint: local gemma4-e4b-it export at training time.
Architecture: Gemma4ForConditionalGeneration.
Published format: BF16 model.safetensors.
Hugging Face safetensors metadata reports about 7.94B BF16 parameters.
Training method: full fine-tune, not LoRA.
Primary language coverage: English plus some Korean.
Primary use: training diagnostics and numeric/log reasoning.
Not intended for: deployment without validation, hidden chain-of-thought training, tool execution, vision/audio tasks, or safety-critical decisions.

The repository name uses fft100 to identify the published checkpoint-style snapshot. The local training config was set up as a 300-step run with saves every 100 steps, so this should be treated as an intermediate experimental snapshot unless a trainer_state.json is published later.

Intended Behavior

The data was built around a neutral diagnostic system prompt:

text
You are a concise Gemma4 E4B training-diagnostics assistant. Use the provided numbers and logs, give short visible rationale when useful, make a concrete decision, obey requested output formats, and stop after the final answer.

The target behavior is concise visible rationale plus a concrete decision, for example:

identify plateau, overfit, grad-norm, OOM, and label-coverage problems;
select a better checkpoint from eval metrics;
explain why eval_loss: nan may be mechanical rather than model collapse;
summarize trends without long hidden-thought style output.

Training Recipe

Table with columns: Setting, Value
Setting	Value
Method	`full_finetune_diagnostic_e4b`
Base model path	`gemma4 e4b`
Precision	BF16
Gradient checkpointing	enabled
Input field	pre-rendered Gemma4 turn text
Max sequence length	1024
Learning rate	`1.5e-6`

Data Mix

The dataset was rebuilt from raw/local source folders under data/; previous derived data/sft mixes were explicitly denied as inputs.

Table with columns: Source family, Rows
Source family	Rows
`OpenThoughts-114k`	17,644
`synthetic_training_diagnostics_v2`	5,337
`time_series_text_prediction`	5,350
`time_series_reasoning`	1,421
`dense_anchor_alignment`	391
`reasoning_trajectory_prediction`

Split:

Table with columns: Split, Rows
Split	Rows
Train	28,156
Eval	2,000

Data Policy

OpenThoughts rows were capped and stripped to visible assistant solution text.
Time-series text rows were kept broadly for numeric trend behavior.
Time-series reasoning and trajectory rows were filtered to training-log keywords.
Synthetic diagnostic rows were generated locally to teach short rationale plus concrete training decisions.
System prompts were replaced with the diagnostic Gemma4 E4B prompt.
Rows were de-duplicated by normalized message content.
Text was rendered with the local Gemma4 turn template: <bos><|turn>role\n...<turn|>.

Denied from this mix:

previous data/sft derived mixes;
persona/style/Lime/Luna sources;
planner JSON and recommended-expert routing data;
tool-call rows;
image rows;
hidden-thought tags such as <think>, <|think|>, and related markers.

Usage

Use a Transformers build that supports Gemma4ForConditionalGeneration and the included chat_template.jinja.

python
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "naksyu/gemma4-e4b-diagnostic-v2-fft100"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": "You are a concise Gemma4 E4B training-diagnostics assistant. Use the provided numbers and logs, give short visible rationale when useful, make a concrete decision, obey requested output formats, and stop after the final answer.",
    },
    {
        "role": "user",
        "content": "Eval loss: 1.228053, 1.227654, 1.227541, 1.227467 over four checkpoints. Should I keep training?",
    },
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)
inputs = {key: value.to(model.device) for key, value in inputs.items()}

outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
print(processor.decode(outputs[0], skip_special_tokens=True))

Limitations

This model has not been released with a full benchmark report.
It may still hallucinate training causes or overstate confidence from weak metrics.
It should not replace actual eval scripts, checkpoint probes, or dataset inspection.
It was tuned as text-only even though the base architecture may expose multimodal tokens.
Safety behavior was not the main target of this tune.

License and Terms

This model is a derivative experimental tune of the base Gemma4 E4B checkpoint. Use is subject to the base model terms and to the licenses or terms of the upstream data sources used in the mix. This model card does not grant separate rights to redistribute any underlying datasets.

Provenance Files

Local build metadata used for this card:

train_gemma4_e4b_diagnostic_v2_fft.yaml
gemma4_e4b_diagnostic_v2_manifest.json
gemma4_e4b_diagnostic_v2_used.md

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Model Details

Model Provider

naksyu

Model Tree

Base

this model

Input Modalities

TextImage

Output Modalities

Text

Supported Functionality

Dedicated EndpointsContainer

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Model Summary

Base checkpoint: local gemma4-e4b-it export at training time.
Architecture: Gemma4ForConditionalGeneration.
Published format: BF16 model.safetensors.
Hugging Face safetensors metadata reports about 7.94B BF16 parameters.
Training method: full fine-tune, not LoRA.
Primary language coverage: English plus some Korean.
Primary use: training diagnostics and numeric/log reasoning.
Not intended for: deployment without validation, hidden chain-of-thought training, tool execution, vision/audio tasks, or safety-critical decisions.

Intended Behavior

The data was built around a neutral diagnostic system prompt:

text
You are a concise Gemma4 E4B training-diagnostics assistant. Use the provided numbers and logs, give short visible rationale when useful, make a concrete decision, obey requested output formats, and stop after the final answer.

The target behavior is concise visible rationale plus a concrete decision, for example:

identify plateau, overfit, grad-norm, OOM, and label-coverage problems;
select a better checkpoint from eval metrics;
explain why eval_loss: nan may be mechanical rather than model collapse;
summarize trends without long hidden-thought style output.

Training Recipe

Table with columns: Setting, Value
Setting	Value
Method	`full_finetune_diagnostic_e4b`
Base model path	`gemma4 e4b`
Precision	BF16
Gradient checkpointing	enabled
Input field	pre-rendered Gemma4 turn text
Max sequence length	1024
Learning rate	`1.5e-6`

Data Mix

The dataset was rebuilt from raw/local source folders under data/; previous derived data/sft mixes were explicitly denied as inputs.

Table with columns: Source family, Rows
Source family	Rows
`OpenThoughts-114k`	17,644
`synthetic_training_diagnostics_v2`	5,337
`time_series_text_prediction`	5,350
`time_series_reasoning`	1,421
`dense_anchor_alignment`	391
`reasoning_trajectory_prediction`

Split:

Table with columns: Split, Rows
Split	Rows
Train	28,156
Eval	2,000

Data Policy

OpenThoughts rows were capped and stripped to visible assistant solution text.
Time-series text rows were kept broadly for numeric trend behavior.
Time-series reasoning and trajectory rows were filtered to training-log keywords.
Synthetic diagnostic rows were generated locally to teach short rationale plus concrete training decisions.
System prompts were replaced with the diagnostic Gemma4 E4B prompt.
Rows were de-duplicated by normalized message content.
Text was rendered with the local Gemma4 turn template: <bos><|turn>role\n...<turn|>.

Denied from this mix:

previous data/sft derived mixes;
persona/style/Lime/Luna sources;
planner JSON and recommended-expert routing data;
tool-call rows;
image rows;
hidden-thought tags such as <think>, <|think|>, and related markers.

Usage

Use a Transformers build that supports Gemma4ForConditionalGeneration and the included chat_template.jinja.

python
import torch
from transformers import AutoModelForImageTextToText, AutoProcessor

model_id = "naksyu/gemma4-e4b-diagnostic-v2-fft100"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

messages = [
    {
        "role": "system",
        "content": "You are a concise Gemma4 E4B training-diagnostics assistant. Use the provided numbers and logs, give short visible rationale when useful, make a concrete decision, obey requested output formats, and stop after the final answer.",
    },
    {
        "role": "user",
        "content": "Eval loss: 1.228053, 1.227654, 1.227541, 1.227467 over four checkpoints. Should I keep training?",
    },
]

inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
)
inputs = {key: value.to(model.device) for key, value in inputs.items()}

outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
print(processor.decode(outputs[0], skip_special_tokens=True))

Limitations

This model has not been released with a full benchmark report.
It may still hallucinate training causes or overstate confidence from weak metrics.
It should not replace actual eval scripts, checkpoint probes, or dataset inspection.
It was tuned as text-only even though the base architecture may expose multimodal tokens.
Safety behavior was not the main target of this tune.

License and Terms

Provenance Files

Local build metadata used for this card:

train_gemma4_e4b_diagnostic_v2_fft.yaml
gemma4_e4b_diagnostic_v2_manifest.json
gemma4_e4b_diagnostic_v2_used.md

gemma4-e4b-diagnostic-v2-fft100

README

Model Summary

Intended Behavior

Training Recipe

Data Mix

Data Policy

Usage

Limitations

License and Terms

Provenance Files

Explore FriendliAI today

README

Model Summary

Intended Behavior

Training Recipe

Data Mix

Data Policy

Usage

Limitations

License and Terms

Provenance Files