Instructions to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="razor5050/codex-qwen2-5-0-5b-unsloth-codex1m")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("razor5050/codex-qwen2-5-0-5b-unsloth-codex1m", dtype="auto")

PEFT
How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/razor5050/codex-qwen2-5-0-5b-unsloth-codex1m

SGLang

How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Studio new

How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for razor5050/codex-qwen2-5-0-5b-unsloth-codex1m to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for razor5050/codex-qwen2-5-0-5b-unsloth-codex1m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for razor5050/codex-qwen2-5-0-5b-unsloth-codex1m to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
    max_seq_length=2048,
)

Docker Model Runner
How to use razor5050/codex-qwen2-5-0-5b-unsloth-codex1m with Docker Model Runner:
```
docker model run hf.co/razor5050/codex-qwen2-5-0-5b-unsloth-codex1m
```

CodeX Qwen2.5 0.5B Unsloth SFT - 1M Rows

This repository is prepared for a cloud-only Unsloth LoRA supervised fine-tuning run of Qwen/Qwen2.5-0.5B-Instruct on the first 1,000,000 rows of Modotte/CodeX-2M-Thinking.

Status: training setup/provisioning. Final metrics, checkpoints, adapters, inference samples, and report will be uploaded here during/after training.

Base Model

Base: Qwen/Qwen2.5-0.5B-Instruct
Architecture: Qwen2ForCausalLM
Parameters: ~494M
Context length: 32,768 tokens
License: Apache-2.0

Dataset

Dataset: Modotte/CodeX-2M-Thinking
Planned subset: first 1,000,000 rows
Columns used:
- input → user/problem prompt
- output → assistant solution/reasoning/code response
Intended task: coding instruction following, algorithmic reasoning, solution explanation, and code generation.

Planned Training Method

Framework: Unsloth + TRL SFTTrainer + PEFT LoRA
Quantized base loading: 4-bit
LoRA rank: 32
LoRA alpha: 64
LoRA dropout: 0
Target modules:
- q_proj, k_proj, v_proj, o_proj
- gate_proj, up_proj, down_proj
Max sequence length for first run: 8,192 tokens
Epochs: 1
Checkpointing: approximately every 2 hours
Durable storage: Hugging Face Hub checkpoints and metrics

Planned Artifacts

During training, this repo will receive:

checkpoints/checkpoint-* — resumable trainer checkpoints
metrics/metrics.jsonl — train/eval logs
metrics/status.json — latest job status
final_lora_adapter/ — final PEFT LoRA adapter
final_merged_16bit/ — merged model if cloud disk/runtime allows
reports/inference_samples.json — post-training inference outputs
reports/TRAINING_REPORT.md — final training report

Intended Use

This model is intended for experimentation with small code-reasoning SFT models, especially:

Python coding assistance
algorithm explanation
competitive-programming style solution drafting
reasoning-heavy coding responses

Limitations

This is a sub-500M parameter model, so it should not be expected to match larger coding models.
The dataset is synthetic and may transfer dataset-specific response style.
Long outputs may still be limited by the training sequence length and generation settings.
The model may produce incorrect code; generated solutions require testing and review.

Safety and Evaluation

Post-training evaluation will include sample inference prompts and loss tracking. This repository should not be treated as production-ready until final metrics and qualitative outputs are reviewed.

Downloads last month: -; Downloads are not tracked for this model. How to track

Model tree for razor5050/codex-qwen2-5-0-5b-unsloth-codex1m

Base model

Qwen/Qwen2.5-0.5B

Finetuned

Qwen/Qwen2.5-0.5B-Instruct

Adapter

(596)

this model

razor5050
/

codex-qwen2-5-0-5b-unsloth-codex1m