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", device_map="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 Settings

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 Desktop
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
```

razor5050 commited on May 12

Commit

5f5f46b

verified ·

1 Parent(s): d0ea605

Mark under8000 complete

Browse files

Files changed (1) hide show

metrics_under8000/status.json +9 -3

metrics_under8000/status.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
-  "updated_at": "2026-05-12T02:49:11.881710+00:00",
-  "phase": "final_save",
   "dataset_dir": "/workspace/datasets/codex_qwen_chatml_under8000",
   "filter_meta": {
     "done": true,
@@ -52,5 +52,11 @@
     "train_loss": 0.5639091513346053,
     "epoch": 1.0
   },
-  "checkpoint": "checkpoints_under8000/checkpoint-74653"
 }

 {
+  "updated_at": "2026-05-12T03:08:45.638267+00:00",
+  "phase": "complete",
   "dataset_dir": "/workspace/datasets/codex_qwen_chatml_under8000",
   "filter_meta": {
     "done": true,
     "train_loss": 0.5639091513346053,
     "epoch": 1.0
   },
+  "checkpoint": "checkpoints_under8000/checkpoint-74653",
+  "hf_repo": "razor5050/codex-qwen2-5-0-5b-unsloth-codex1m",
+  "final_step": 74653,
+  "latest_loss": 0.7097312450408936,
+  "latest_eval_loss": 0.7063287496566772,
+  "inference_samples": 6,
+  "deliverable": "base_model_plus_lora_adapter_option_a"
 }