Instructions to use Tesleum/0xCoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tesleum/0xCoder with Transformers:

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

pipe = pipeline("text-generation", model="Tesleum/0xCoder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Tesleum/0xCoder")
model = AutoModelForCausalLM.from_pretrained("Tesleum/0xCoder", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tesleum/0xCoder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tesleum/0xCoder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tesleum/0xCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tesleum/0xCoder

SGLang

How to use Tesleum/0xCoder 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 "Tesleum/0xCoder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tesleum/0xCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Tesleum/0xCoder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tesleum/0xCoder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tesleum/0xCoder with Docker Model Runner:
```
docker model run hf.co/Tesleum/0xCoder
```

stevelionheart commited on 16 days ago

Commit

f37820f

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1 Parent(s): a4b57b9

Update README.md

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README.md CHANGED Viewed

@@ -42,7 +42,7 @@ model-index:
 # 📝 Claude-OSS (Fable 5)
 > [!NOTE]
-> **Hugging Face Metadata:** The automated metadata tag at the top of this page miscalculates this model as a 12B BF16/U8 model because the Hub parser cannot natively calculate the 32-expert MoE layout of `gpt_oss`. The actual model size is **21B parameters** running natively on **MXFP4 microquantization**.
 <Gallery />
@@ -124,24 +124,11 @@ To learn about how to use this model with PyTorch and Triton, check out our [ref
 ## Ollama
-If you are trying to run gpt-oss on consumer hardware, you can use Ollama by running the following commands after [installing Ollama](https://ollama.com/download).
-```bash
-# Tesleum/claude-oss
-ollama pull Tesleum/claude-oss
-ollama run Tesleum/claude-oss
-```
-[Learn more about how to use gpt-oss with Ollama.](https://cookbook.openai.com/articles/gpt-oss/run-locally-ollama)
 #### LM Studio
-If you are using [LM Studio](https://lmstudio.ai/) you can use the following commands to download.
-```bash
-# claude-oss
-lms get Tesleum/claude-oss
-```
 ---

 # 📝 Claude-OSS (Fable 5)
 > [!NOTE]
+> **Hugging Face Metadata:** The automated metadata tag at the top of this page miscalculates this model as a 12B BF16/U8 model because the Hub parser cannot natively calculate the 32-expert MoE layout of `Claude-OSS`. The actual model size is **21B parameters** running natively on **MXFP4 microquantization**.
 <Gallery />
 ## Ollama
+To achieve better performance and quality, use **vLLM** instead
 #### LM Studio
+To achieve better performance and quality, use **vLLM** instead
 ---