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Model: reallexi/lexi-resume-v6 Source: Original Platform
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1
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COPYRIGHT.md
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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NOTICE
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NOTICE
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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This standalone model was produced by Reallexi LLC AI Model Builder.
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Core backlink: https://llm.reallexi.io
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The upstream base model and training datasets retain their own licenses and terms.
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---
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license: other
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license_name: "inherits-base-model-and-dataset-terms"
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base_model: "Qwen/Qwen2.5-0.5B-Instruct"
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library_name: transformers
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pipeline_tag: "text-generation"
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tags:
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- "ai-model-builder"
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- "fine-tuned"
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- reallexi
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- slm
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- "text-generation"
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---
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# lexi-resume-v6
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**lexi-resume-v6** by Reallexi LLC AI Model Builder — [llm.reallexi.io](https://llm.reallexi.io)
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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A standalone model of 495M parameters, derived from [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct).
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The adapter has been merged into the base weights, so no PEFT adapter is needed at runtime.
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## Size and requirements
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| | |
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|---|---|
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| Parameters | 495,114,112 (495M) |
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| Weights on disk | 942 MB |
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| Trained context length | 8,192 tokens |
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| Base model | `Qwen/Qwen2.5-0.5B-Instruct` |
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Approximate memory to hold the weights. Add context and runtime overhead on top.
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| Precision | Weights |
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|---|---|
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| FP16 / BF16 | 944 MB |
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| 8-bit (Q8_0) | 472 MB |
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| 4-bit (Q4_K_M) | 260 MB |
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## Training
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| | |
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|---|---|
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| Strategy | slm |
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| Adapter | Auto LoRA |
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| LoRA rank / alpha | 8 / 16 |
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| Dataset | `AzharAli05/Resume-Screening-Dataset` |
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| Samples learned | 10,000 (through phase 3 of 3) |
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| Training steps | 2,250 |
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| Epochs | 3 |
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## Before / after samples
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The same prompts, drawn from the training data, run through the base model before training and the finished model after. This shows what the run changed on representative prompts -- it is not a benchmark. Full outputs are in `SAMPLES.md` and `samples.json` alongside this file.
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**Prompt:** Role: AR/VR Developer; Resume: Here's a professional resume for Mary Johnson, tailored to the AR/VR Developer role: Mary Johnson Contact
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- Before: Information:
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* Address: 123 Main St, Anytown, USA 12345
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* Phone: (555) 555-5555
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* Email: [mary.johnson@email.com](mailto:mary.johnson@email.com)
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Professional Summary:
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Highly motivated and experienced AR/VR Developer with expertise in Unity, C#,
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- After: Information:
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* Address: 123 Main St, Anytown, USA 12345
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* Phone: (555) 555-5555
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* Email: [mary.johnson@email.com](mailto:mary.johnson@email.com)
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* LinkedIn: linkedin.com/in/maryjohnsondeveloper
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Summary:
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Highly motivated and
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**Prompt:** Role: product manager; Resume: here's a sample resume for brent brown applying for the role of product manager: brent brown
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- Before: Product Manager
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Contact Information:
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* Email: [brent.brown@email.com](mailto:brent.brown@email.com)
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* Phone: (123) 456-7890
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* LinkedIn: linkedin.com/in/brentbrown
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Summary:
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Highly motivated and detail-oriented Product Manager with 5+ years of experience in driving successful product launches, delivering high
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- After: product manager
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contact information:
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* email: [brent.brown@email.com](mailto:brent.brown@email.com)
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* phone: 555-555-5555
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* linkedin: linkedin.com/in/brentbrown
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summary:
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highly motivated and detail-oriented product manager with 3+ years of experience in creating and executing successful product strategies.
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**Prompt:** Role: data engineer; Resume: **gina mehta** **data engineer candidate** gina mehta is a highly skilled and experienced data engineer with
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|
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- Before: over 5 years of experience in designing, developing, and deploying scalable data solutions. She has a strong background in cloud computing, database design, and data visualization.
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|
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**Key skills:**
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|
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* Cloud platforms (AWS, Azure, Google Cloud)
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* Database management (MySQL, PostgreSQL, MongoDB)
|
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* Data modeling and query…
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- After: a strong background in designing, developing, and deploying scalable data solutions. she has a proven track record of delivering high-quality data products that meet the needs of clients across various industries.
|
||||
|
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**key skills and achievements:**
|
||||
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* **data engineering:** demonstrated expertise in designing, developing…
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## Training curve
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model = AutoModelForCausalLM.from_pretrained("lexi-resume-v6")
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tokenizer = AutoTokenizer.from_pretrained("lexi-resume-v6")
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```
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## License and attribution
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The effective terms are inherited from the base model and the training data, which are not necessarily the same as this project's own license. Review both before redistributing.
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- Base model: [`Qwen/Qwen2.5-0.5B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
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- Training data: `AzharAli05/Resume-Screening-Dataset`
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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Produced by Reallexi LLC AI Model Builder from training job #1609.
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Core: https://llm.reallexi.io
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## Who, where, and what platform trained this?
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Produced by Reallexi LLC on [Reallexi AI Model Builder](https://llm.reallexi.io), a local-first training platform (https://llm.reallexi.io). Hugging Face repository: [reallexi/lexi-resume-v6](https://huggingface.co/reallexi/lexi-resume-v6).
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Copyright (c) 2026 Reallexi LLC. All rights reserved.
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75
SAMPLES.md
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# Before / after training samples
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Generated automatically from a few prompts drawn from the training data, run once against the base model before training started and once against the finished model. This shows what this run changed on representative prompts -- it is not a benchmark and does not measure generalization.
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## Prompt: Role: AR/VR Developer; Resume: Here's a professional resume for Mary Johnson, tailored to the AR/VR Developer role: Mary Johnson Contact
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**Before:**
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> Information:
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* Address: 123 Main St, Anytown, USA 12345
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* Phone: (555) 555-5555
|
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* Email: [mary.johnson@email.com](mailto:mary.johnson@email.com)
|
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Professional Summary:
|
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Highly motivated and experienced AR/VR Developer with expertise in Unity, C#,
|
||||
|
||||
**After:**
|
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|
||||
> Information:
|
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* Address: 123 Main St, Anytown, USA 12345
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* Phone: (555) 555-5555
|
||||
* Email: [mary.johnson@email.com](mailto:mary.johnson@email.com)
|
||||
* LinkedIn: linkedin.com/in/maryjohnsondeveloper
|
||||
|
||||
Summary:
|
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Highly motivated and
|
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|
||||
## Prompt: Role: product manager; Resume: here's a sample resume for brent brown applying for the role of product manager: brent brown
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||||
|
||||
**Before:**
|
||||
|
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> Product Manager
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Contact Information:
|
||||
|
||||
* Email: [brent.brown@email.com](mailto:brent.brown@email.com)
|
||||
* Phone: (123) 456-7890
|
||||
* LinkedIn: linkedin.com/in/brentbrown
|
||||
|
||||
Summary:
|
||||
Highly motivated and detail-oriented Product Manager with 5+ years of experience in driving successful product launches, delivering high
|
||||
|
||||
**After:**
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|
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> product manager
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contact information:
|
||||
|
||||
* email: [brent.brown@email.com](mailto:brent.brown@email.com)
|
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* phone: 555-555-5555
|
||||
* linkedin: linkedin.com/in/brentbrown
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|
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summary:
|
||||
highly motivated and detail-oriented product manager with 3+ years of experience in creating and executing successful product strategies.
|
||||
|
||||
## Prompt: Role: data engineer; Resume: **gina mehta** **data engineer candidate** gina mehta is a highly skilled and experienced data engineer with
|
||||
|
||||
**Before:**
|
||||
|
||||
> over 5 years of experience in designing, developing, and deploying scalable data solutions. She has a strong background in cloud computing, database design, and data visualization.
|
||||
|
||||
**Key skills:**
|
||||
|
||||
* Cloud platforms (AWS, Azure, Google Cloud)
|
||||
* Database management (MySQL, PostgreSQL, MongoDB)
|
||||
* Data modeling and querying
|
||||
* Data visualization tools (Tableau, Power BI)
|
||||
* Big data analytics
|
||||
|
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**After:**
|
||||
|
||||
> a strong background in designing, developing, and deploying scalable data solutions. she has a proven track record of delivering high-quality data products that meet the needs of clients across various industries.
|
||||
|
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**key skills and achievements:**
|
||||
|
||||
* **data engineering:** demonstrated expertise in designing, developing, and deploying complex data pipelines using tools like apache spark, hive, and hbase.
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* **big data analytics:** proficient in
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207
adapter/README.md
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adapter/README.md
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---
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base_model: Qwen/Qwen2.5-0.5B-Instruct
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library_name: peft
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pipeline_tag: text-generation
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tags:
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- base_model:adapter:Qwen/Qwen2.5-0.5B-Instruct
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- lora
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- transformers
|
||||
---
|
||||
|
||||
# Model Card for Model ID
|
||||
|
||||
<!-- Provide a quick summary of what the model is/does. -->
|
||||
|
||||
|
||||
|
||||
## Model Details
|
||||
|
||||
### Model Description
|
||||
|
||||
<!-- Provide a longer summary of what this model is. -->
|
||||
|
||||
|
||||
|
||||
- **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]
|
||||
|
||||
<!-- Provide the basic links for the model. -->
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||||
|
||||
- **Repository:** [More Information Needed]
|
||||
- **Paper [optional]:** [More Information Needed]
|
||||
- **Demo [optional]:** [More Information Needed]
|
||||
|
||||
## Uses
|
||||
|
||||
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
|
||||
|
||||
### Direct Use
|
||||
|
||||
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Downstream Use [optional]
|
||||
|
||||
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Out-of-Scope Use
|
||||
|
||||
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Bias, Risks, and Limitations
|
||||
|
||||
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Recommendations
|
||||
|
||||
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
|
||||
|
||||
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
|
||||
|
||||
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Training Procedure
|
||||
|
||||
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
|
||||
|
||||
#### Preprocessing [optional]
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
|
||||
#### Training Hyperparameters
|
||||
|
||||
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
|
||||
|
||||
#### Speeds, Sizes, Times [optional]
|
||||
|
||||
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Evaluation
|
||||
|
||||
<!-- This section describes the evaluation protocols and provides the results. -->
|
||||
|
||||
### Testing Data, Factors & Metrics
|
||||
|
||||
#### Testing Data
|
||||
|
||||
<!-- This should link to a Dataset Card if possible. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Factors
|
||||
|
||||
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Metrics
|
||||
|
||||
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
### Results
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
#### Summary
|
||||
|
||||
|
||||
|
||||
## Model Examination [optional]
|
||||
|
||||
<!-- Relevant interpretability work for the model goes here -->
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Environmental Impact
|
||||
|
||||
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
|
||||
|
||||
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
|
||||
|
||||
- **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]
|
||||
|
||||
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
|
||||
|
||||
**BibTeX:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
**APA:**
|
||||
|
||||
[More Information Needed]
|
||||
|
||||
## Glossary [optional]
|
||||
|
||||
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
|
||||
|
||||
[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
|
||||
45
adapter/adapter_config.json
Normal file
45
adapter/adapter_config.json
Normal file
@@ -0,0 +1,45 @@
|
||||
{
|
||||
"alora_invocation_tokens": null,
|
||||
"alpha_pattern": {},
|
||||
"arrow_config": null,
|
||||
"auto_mapping": null,
|
||||
"base_model_name_or_path": "Qwen/Qwen2.5-0.5B-Instruct",
|
||||
"bias": "none",
|
||||
"corda_config": null,
|
||||
"ensure_weight_tying": false,
|
||||
"eva_config": null,
|
||||
"exclude_modules": null,
|
||||
"fan_in_fan_out": false,
|
||||
"inference_mode": true,
|
||||
"init_lora_weights": true,
|
||||
"layer_replication": null,
|
||||
"layers_pattern": null,
|
||||
"layers_to_transform": null,
|
||||
"loftq_config": {},
|
||||
"lora_alpha": 16,
|
||||
"lora_bias": false,
|
||||
"lora_dropout": 0.05,
|
||||
"lora_ga_config": null,
|
||||
"megatron_config": null,
|
||||
"megatron_core": "megatron.core",
|
||||
"modules_to_save": null,
|
||||
"peft_type": "LORA",
|
||||
"peft_version": "0.19.1",
|
||||
"qalora_group_size": 16,
|
||||
"r": 8,
|
||||
"rank_pattern": {},
|
||||
"revision": null,
|
||||
"target_modules": [
|
||||
"o_proj",
|
||||
"k_proj",
|
||||
"v_proj",
|
||||
"q_proj"
|
||||
],
|
||||
"target_parameters": null,
|
||||
"task_type": "CAUSAL_LM",
|
||||
"trainable_token_indices": null,
|
||||
"use_bdlora": null,
|
||||
"use_dora": false,
|
||||
"use_qalora": false,
|
||||
"use_rslora": false
|
||||
}
|
||||
3
adapter/adapter_model.safetensors
Normal file
3
adapter/adapter_model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d76dd9c72984aadc26a1edd287eb3f6fbacba3e717b455775a4dd7bcaaf6b9c8
|
||||
size 4350392
|
||||
54
adapter/chat_template.jinja
Normal file
54
adapter/chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
3
adapter/tokenizer.json
Normal file
3
adapter/tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a51259b50f79cf111fc86a73fb6223f4317039d9e4f163710e2831eca66a5ad7
|
||||
size 11421991
|
||||
33
adapter/tokenizer_config.json
Normal file
33
adapter/tokenizer_config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
"add_prefix_space": false,
|
||||
"backend": "tokenizers",
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"is_local": true,
|
||||
"max_length": 8112,
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"stride": 0,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"truncation_side": "right",
|
||||
"truncation_strategy": "longest_first",
|
||||
"unk_token": null
|
||||
}
|
||||
54
chat_template.jinja
Normal file
54
chat_template.jinja
Normal file
@@ -0,0 +1,54 @@
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- messages[0]['content'] }}
|
||||
{%- else %}
|
||||
{{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}
|
||||
{%- endif %}
|
||||
{{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0]['role'] == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{{- '<|im_start|>' + message.role }}
|
||||
{%- if message.content %}
|
||||
{{- '\n' + message.content }}
|
||||
{%- endif %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if tool_call.function is defined %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- endif %}
|
||||
57
config.json
Normal file
57
config.json
Normal file
@@ -0,0 +1,57 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen2ForCausalLM"
|
||||
],
|
||||
"attention_dropout": 0.0,
|
||||
"bos_token_id": 151643,
|
||||
"dtype": "float16",
|
||||
"eos_token_id": 151645,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 896,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 4864,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 32768,
|
||||
"max_window_layers": 21,
|
||||
"model_type": "qwen2",
|
||||
"num_attention_heads": 14,
|
||||
"num_hidden_layers": 24,
|
||||
"num_key_value_heads": 2,
|
||||
"pad_token_id": null,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_parameters": {
|
||||
"rope_theta": 1000000.0,
|
||||
"rope_type": "default"
|
||||
},
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"transformers_version": "5.3.0",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"repetition_penalty": 1.1,
|
||||
"temperature": 0.7,
|
||||
"top_k": 20,
|
||||
"top_p": 0.8,
|
||||
"transformers_version": "5.3.0"
|
||||
}
|
||||
3
lexi-resume-v6-f16.gguf
Normal file
3
lexi-resume-v6-f16.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:d4df79c4a237ddbfc2f969d2f5152a611e97ec2020c36e2ecefa4774bd2e3de2
|
||||
size 994156832
|
||||
3
lexi-resume-v6-q4_k_m.gguf
Normal file
3
lexi-resume-v6-q4_k_m.gguf
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:2a37c8f29c5876d27ffa36c9988551fc1198cd0fb3dedbc45d5498c4e9f4eba3
|
||||
size 397807904
|
||||
389
manifest.json
Normal file
389
manifest.json
Normal file
@@ -0,0 +1,389 @@
|
||||
{
|
||||
"schema_version": 1,
|
||||
"job_id": 1609,
|
||||
"base_model": "trained-job-1608",
|
||||
"base_model_id": "Qwen/Qwen2.5-0.5B-Instruct",
|
||||
"modality": "text",
|
||||
"training_strategy": "slm",
|
||||
"artifact_type": "lora_adapter",
|
||||
"model_path": "/app/backend/data/artifacts/job_1609/adapter",
|
||||
"tokenizer_path": "/app/backend/data/artifacts/job_1609/adapter",
|
||||
"context_length": 8192,
|
||||
"phase": {
|
||||
"enabled": true,
|
||||
"index": 2,
|
||||
"number": 3,
|
||||
"total": 3,
|
||||
"sample_start": 7000,
|
||||
"sample_end": 10000,
|
||||
"target_samples": 10000
|
||||
},
|
||||
"created_at": "2026-08-07T21:41:36Z",
|
||||
"test_supported": true,
|
||||
"ownership": {
|
||||
"owner": "Reallexi LLC",
|
||||
"copyright": "Copyright (c) 2026 Reallexi LLC. All rights reserved.",
|
||||
"core_backlink": "https://llm.reallexi.io",
|
||||
"license": "Apache-2.0 unless third-party base model or dataset terms require more restrictions.",
|
||||
"notice": "This artifact was produced by Reallexi LLC AI Model Builder. Review upstream model, adapter, dataset and source licenses before redistribution."
|
||||
},
|
||||
"lineage": {
|
||||
"mission": "incremental",
|
||||
"parent_job_id": 1608,
|
||||
"lineage_root_job_id": 256,
|
||||
"base_artifact_job_id": 1608,
|
||||
"base_artifact_path": "/app/backend/data/artifacts/job_1608",
|
||||
"resume_from_job_id": null,
|
||||
"resume_checkpoint_path": "",
|
||||
"rerun_policy": "",
|
||||
"continue_policy": "train-next-phase-from-completed-artifact",
|
||||
"history": [
|
||||
{
|
||||
"timestamp": "2026-08-07T21:22:59.565539",
|
||||
"action": "incremental",
|
||||
"trigger": "automatic",
|
||||
"from_job_id": 1606,
|
||||
"from_status": "completed",
|
||||
"base_model": "trained-job-1606",
|
||||
"phase_index": 1,
|
||||
"phase_sample_start": 3500,
|
||||
"phase_sample_end": 7000
|
||||
},
|
||||
{
|
||||
"timestamp": "2026-08-07T21:33:35.341886",
|
||||
"action": "incremental",
|
||||
"trigger": "automatic",
|
||||
"from_job_id": 1608,
|
||||
"from_status": "completed",
|
||||
"base_model": "trained-job-1608",
|
||||
"phase_index": 2,
|
||||
"phase_sample_start": 7000,
|
||||
"phase_sample_end": 10000
|
||||
}
|
||||
]
|
||||
},
|
||||
"adapter": {
|
||||
"enabled": true,
|
||||
"method": "peft-lora-adapter",
|
||||
"type": "auto",
|
||||
"label": "Auto LoRA",
|
||||
"description": "Choose PEFT target modules from the loaded model architecture.",
|
||||
"target_modules": "",
|
||||
"rank": 8,
|
||||
"alpha": 16,
|
||||
"model_class": "PeftModelForCausalLM"
|
||||
},
|
||||
"dataset_preparation": {
|
||||
"modality": "text",
|
||||
"source": "hub",
|
||||
"dataset": "AzharAli05/Resume-Screening-Dataset",
|
||||
"max_samples": 10000,
|
||||
"phase": {
|
||||
"enabled": true,
|
||||
"number": 3,
|
||||
"total": 3,
|
||||
"sample_start": 7000,
|
||||
"sample_end": 10000,
|
||||
"phase_samples": 3000
|
||||
},
|
||||
"external_sources": [],
|
||||
"temporary_external_cache": true,
|
||||
"text_column": "text",
|
||||
"split": "train",
|
||||
"conversion": "dataset rows are normalized into a text column before tokenization."
|
||||
},
|
||||
"exports": {
|
||||
"requested": [
|
||||
"huggingface",
|
||||
"safetensors",
|
||||
"gguf",
|
||||
"onnx",
|
||||
"pytorch-bin"
|
||||
],
|
||||
"formats": [
|
||||
{
|
||||
"id": "huggingface",
|
||||
"label": "Native Training Output",
|
||||
"extension": "folder",
|
||||
"generated": true,
|
||||
"description": "The original Transformers model or PEFT adapter folder saved when training completes.",
|
||||
"available": true,
|
||||
"status": "generated"
|
||||
},
|
||||
{
|
||||
"id": "safetensors",
|
||||
"label": "Standalone SafeTensors",
|
||||
"extension": ".zip",
|
||||
"generated": false,
|
||||
"build_on_demand": true,
|
||||
"description": "Complete merged SafeTensors model built and verified after adapter training.",
|
||||
"available": false,
|
||||
"status": "build-on-demand"
|
||||
},
|
||||
{
|
||||
"id": "gguf",
|
||||
"label": "GGUF Q8_0",
|
||||
"extension": ".gguf",
|
||||
"generated": false,
|
||||
"build_on_demand": true,
|
||||
"description": "Complete GGUF model built automatically with the pinned official llama.cpp converter.",
|
||||
"available": false,
|
||||
"status": "build-on-demand"
|
||||
},
|
||||
{
|
||||
"id": "onnx",
|
||||
"label": "ONNX",
|
||||
"extension": ".onnx",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
"label": "PyTorch Bin",
|
||||
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|
||||
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|
||||
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|
||||
"available": false,
|
||||
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|
||||
}
|
||||
],
|
||||
"artifact_type": "lora_adapter"
|
||||
},
|
||||
"proof": {
|
||||
"source": "hub",
|
||||
"dataset": "AzharAli05/Resume-Screening-Dataset",
|
||||
"split": "train",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"containerized": true,
|
||||
"runtime_os": "linux",
|
||||
"host_os": "win32",
|
||||
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|
||||
{
|
||||
"id": "cuda:0",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"usable": true,
|
||||
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|
||||
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|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"reason": "Detected by Windows, but AMD GPUs are not exposed to this Linux Docker runtime.",
|
||||
"driver": "32.0.23034.4"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"dataset_preparation": {
|
||||
"modality": "text",
|
||||
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|
||||
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|
||||
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|
||||
"phase": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"text_column": "text",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"has_samples": true,
|
||||
"has_training_curve": true,
|
||||
"sample_outputs": [
|
||||
{
|
||||
"prompt": "Role: AR/VR Developer; Resume: Here's a professional resume for Mary Johnson, tailored to the AR/VR Developer role: Mary Johnson Contact",
|
||||
"before": "Information:\n* Address: 123 Main St, Anytown, USA 12345\n* Phone: (555) 555-5555\n* Email: [mary.johnson@email.com](mailto:mary.johnson@email.com)\nProfessional Summary:\nHighly motivated and experienced AR/VR Developer with expertise in Unity, C#,",
|
||||
"after": "Information:\n* Address: 123 Main St, Anytown, USA 12345\n* Phone: (555) 555-5555\n* Email: [mary.johnson@email.com](mailto:mary.johnson@email.com)\n* LinkedIn: linkedin.com/in/maryjohnsondeveloper\n\nSummary:\nHighly motivated and"
|
||||
},
|
||||
{
|
||||
"prompt": "Role: product manager; Resume: here's a sample resume for brent brown applying for the role of product manager: brent brown",
|
||||
"before": "Product Manager\nContact Information:\n\n* Email: [brent.brown@email.com](mailto:brent.brown@email.com)\n* Phone: (123) 456-7890\n* LinkedIn: linkedin.com/in/brentbrown\n\nSummary:\nHighly motivated and detail-oriented Product Manager with 5+ years of experience in driving successful product launches, delivering high",
|
||||
"after": "product manager\n\ncontact information:\n\n* email: [brent.brown@email.com](mailto:brent.brown@email.com)\n* phone: 555-555-5555\n* linkedin: linkedin.com/in/brentbrown\n\nsummary:\nhighly motivated and detail-oriented product manager with 3+ years of experience in creating and executing successful product strategies."
|
||||
},
|
||||
{
|
||||
"prompt": "Role: data engineer; Resume: **gina mehta** **data engineer candidate** gina mehta is a highly skilled and experienced data engineer with",
|
||||
"before": "over 5 years of experience in designing, developing, and deploying scalable data solutions. She has a strong background in cloud computing, database design, and data visualization.\n\n**Key skills:**\n\n* Cloud platforms (AWS, Azure, Google Cloud)\n* Database management (MySQL, PostgreSQL, MongoDB)\n* Data modeling and querying\n* Data visualization tools (Tableau, Power BI)\n* Big data analytics",
|
||||
"after": "a strong background in designing, developing, and deploying scalable data solutions. she has a proven track record of delivering high-quality data products that meet the needs of clients across various industries.\n\n**key skills and achievements:**\n\n* **data engineering:** demonstrated expertise in designing, developing, and deploying complex data pipelines using tools like apache spark, hive, and hbase.\n* **big data analytics:** proficient in"
|
||||
}
|
||||
]
|
||||
},
|
||||
"numerical_validation": {
|
||||
"valid": true,
|
||||
"trainable_tensors_checked": 192,
|
||||
"trainable_values_checked": 1081344
|
||||
},
|
||||
"adapter_path": "/app/backend/data/artifacts/job_1609/adapter"
|
||||
}
|
||||
151387
merges.txt
Normal file
151387
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model.safetensors
Normal file
3
model.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:615e3f25208bbab6479726411b0dda2e466ecc459db519acf89b28644fb7fb01
|
||||
size 988097536
|
||||
20
reallexi-model.json
Normal file
20
reallexi-model.json
Normal file
@@ -0,0 +1,20 @@
|
||||
{
|
||||
"schema_version": 1,
|
||||
"job_id": 1609,
|
||||
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|
||||
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|
||||
"standalone": true,
|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
"copyright": "Copyright (c) 2026 Reallexi LLC. All rights reserved.",
|
||||
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|
||||
"created_at": "2026-08-08T16:19:50+00:00"
|
||||
}
|
||||
35
samples.json
Normal file
35
samples.json
Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
"prompts": [
|
||||
"Role: AR/VR Developer; Resume: Here's a professional resume for Mary Johnson, tailored to the AR/VR Developer role: Mary Johnson Contact",
|
||||
"Role: product manager; Resume: here's a sample resume for brent brown applying for the role of product manager: brent brown",
|
||||
"Role: data engineer; Resume: **gina mehta** **data engineer candidate** gina mehta is a highly skilled and experienced data engineer with"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
{
|
||||
"prompt": "Role: product manager; Resume: here's a sample resume for brent brown applying for the role of product manager: brent brown",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
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|
||||
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|
||||
33
tokenizer_config.json
Normal file
33
tokenizer_config.json
Normal file
@@ -0,0 +1,33 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
BIN
training_curve.png
Normal file
BIN
training_curve.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 58 KiB |
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user