Instructions to use CodeSoft/MetaDiffusion-600M-ChatBase with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CodeSoft/MetaDiffusion-600M-ChatBase with Transformers:

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

pipe = pipeline("text-generation", model="CodeSoft/MetaDiffusion-600M-ChatBase", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("CodeSoft/MetaDiffusion-600M-ChatBase", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CodeSoft/MetaDiffusion-600M-ChatBase with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CodeSoft/MetaDiffusion-600M-ChatBase"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CodeSoft/MetaDiffusion-600M-ChatBase",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CodeSoft/MetaDiffusion-600M-ChatBase

SGLang

How to use CodeSoft/MetaDiffusion-600M-ChatBase 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 "CodeSoft/MetaDiffusion-600M-ChatBase" \
    --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": "CodeSoft/MetaDiffusion-600M-ChatBase",
		"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 "CodeSoft/MetaDiffusion-600M-ChatBase" \
        --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": "CodeSoft/MetaDiffusion-600M-ChatBase",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CodeSoft/MetaDiffusion-600M-ChatBase with Docker Model Runner:
```
docker model run hf.co/CodeSoft/MetaDiffusion-600M-ChatBase
```

CodeSoft commited on 4 days ago

Commit

18eebaf

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1 Parent(s): 6f36850

Update README.md

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@@ -76,38 +76,38 @@ print(tok.decode(out[0], skip_special_tokens=True))
 # Chat with it (chat.py)
 ```bash
-python3 MetaDiffusion-600M-ChatBase/scripts/chat.py \
-      --model-path MetaDiffusion-600M-ChatBase/model.safetensors \
-      --tokenizer MetaDiffusion-600M-ChatBase/tokenizer \
       --im-end-bias 2.0 --im-end-bias-t 0.3 --watch
 ```
 ## Fine-tune (train.py)
 ```bash
 # 1. Init: convert the AR model to a diffusion init
-python scripts/convert.py --source Qwen/Qwen3-0.6B \
     --output init/metadiffusion-600M-instruct.pt \
     --tokenizer-out data/tokenizer
 # 2. Corpus: smol, opc, math and no_robots, or a local --jsonl of {"messages": [...]} rows.
 #    --val-fraction holds out a disjoint val set for early stopping.
-python scripts/prepare_data.py --datasets smol,math --out data \
     --val-fraction 0.05
 # 3. Train (defaults: lr 5e-5, bf16, seq 512, batch auto-detected)
-python scripts/train.py --init-checkpoint init/metadiffusion-600M-instruct.pt \
     --data-dir data --output-dir checkpoints --max-steps 30000
 # 4. Continue a run: checkpoints carry model + optimizer + scheduler
 #    state, so --resume-from picks up LR position and momentum exactly
-python scripts/train.py --init-checkpoint init/metadiffusion-600M-instruct.pt \
-    --data-dir data --output-dir checkpoints_p3 \
     --resume-from checkpoints_p2/step_20000.pt --max-steps 16000
 # 5. Test, then ship
-python scripts/chat.py --model-path checkpoints_p3/step_30000.pt \
     --tokenizer data/tokenizer --watch
-python scripts/export_hf.py --checkpoint checkpoints_p3/step_30000.pt \
     --tokenizer data/tokenizer --output MetaDiffusion-600M-ChatBase
 ```
 ## Limitations

 # Chat with it (chat.py)
 ```bash
+python chat.py \
+      --model-path model.safetensors \
+      --tokenizer ./tokenizer \
       --im-end-bias 2.0 --im-end-bias-t 0.3 --watch
 ```
 ## Fine-tune (train.py)
 ```bash
 # 1. Init: convert the AR model to a diffusion init
+python convert.py --source Qwen/Qwen3-0.6B \
     --output init/metadiffusion-600M-instruct.pt \
     --tokenizer-out data/tokenizer
 # 2. Corpus: smol, opc, math and no_robots, or a local --jsonl of {"messages": [...]} rows.
 #    --val-fraction holds out a disjoint val set for early stopping.
+python prepare_data.py --datasets smol,math --out data \
     --val-fraction 0.05
 # 3. Train (defaults: lr 5e-5, bf16, seq 512, batch auto-detected)
+python train.py --init-checkpoint init/metadiffusion-600M-instruct.pt \
     --data-dir data --output-dir checkpoints --max-steps 30000
 # 4. Continue a run: checkpoints carry model + optimizer + scheduler
 #    state, so --resume-from picks up LR position and momentum exactly
+python train.py --init-checkpoint init/metadiffusion-600M-instruct.pt \
+    --data-dir data --output-dir checkpoints \
     --resume-from checkpoints_p2/step_20000.pt --max-steps 16000
 # 5. Test, then ship
+python chat.py --model-path checkpoints_/step_30000.pt \
     --tokenizer data/tokenizer --watch
+python export_hf.py --checkpoint checkpoints/step_30000.pt \
     --tokenizer data/tokenizer --output MetaDiffusion-600M-ChatBase
 ```
 ## Limitations