171 steps, train_loss=0.6808, eval_loss=0.6348 (comparable to v1's 0.6306),
peak CUDA memory 74.58GB -- identical to v1's run, confirming the 13
corrective seeds didn't change the memory/length envelope. All guards
verified in adapter_config.json: use_rslora=false, use_dora=false,
lora_bias=false, modules_to_save=null, r=32, lora_alpha=64.
v1 adapter (out/lora-adapter-penpot/, commit 363648f) stays untouched as
a rollback point. New OUTPUT_DIR avoided the overwrite guard.
adapter_model.safetensors sha256 verified identical before/after copying
out of the container (root-owned file, fixed ownership via chown inside
the container since this OUTPUT_DIR landed inside the worktree, unlike
v1's which was copied in from outside).
5.1 KiB
base_model, library_name, pipeline_tag, tags
| base_model | library_name | pipeline_tag | tags | |||
|---|---|---|---|---|---|---|
| /workspace/ft-models/Qwen3.6-35B-A3B-mcp-bf16 | peft | text-generation |
|
Model Card for Model ID
Model Details
Model Description
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
Training Details
Training Data
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
Framework versions
- PEFT 0.19.1