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 13 days ago

Commit

dd5fd26

verified ·

1 Parent(s): b053000

Update README.md

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README.md +33 -4

README.md CHANGED Viewed

@@ -20,6 +20,8 @@ datasets:
 - Tesleum/Fable-5-traces-Harmony
 pipeline_tag: text-generation
 library_name: transformers
 model-index:
 - name: Claude-OSS
   results:
@@ -29,14 +31,41 @@ model-index:
       name: Model Specifications
       type: custom
     metrics:
-    - name: Model Size
       type: parameters
-      value: 21B Total Params (3.6B Active)
-    - name: Precision / Format
       type: format
-      value: MXFP4 (Microscaling 4-bit)
 ---
 <Gallery />
 ## Model description

 - Tesleum/Fable-5-traces-Harmony
 pipeline_tag: text-generation
 library_name: transformers
+# This structures your data into Hugging Face's official widget database
 model-index:
 - name: Claude-OSS
   results:
       name: Model Specifications
       type: custom
     metrics:
+    - name: True Architecture Size
       type: parameters
+      value: "21B Total Params (3.6B Active)"
+    - name: Native Hardware Format
       type: format
+      value: "MXFP4 (Microscaling 4-bit)"
 ---
+# 📝 Claude-OSS (Fable 5)
+> [!NOTE]
+> **Hugging Face Metadata Warning:** 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**.
+## 💬 Chat Template
+To fix the missing chat template error on the Hugging Face Hub UI, use this standard Chat template block. It is pre-formatted for seamless deployment across vLLM, Ollama, and Transformers setups:
+```json
+{{- choice -}}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- set system_message = messages[0]['content'] -%}
+    {{- 'System: ' + system_message + '\n' -}}
+{%- else -%}
+    {{- 'System: You are a helpful AI assistant.\n' -}}
+{%- endif -%}
+{%- for message in messages -%}
+    {%- if message['role'] == 'user' -%}
+        {{- 'User: ' + message['content'] + '\n' -}}
+    {%- elif message['role'] == 'assistant' -%}
+        {{- 'Assistant: ' + message['content'] + '\n' -}}
+    {%- endif -%}
+{%- endfor -%}
+{{- 'Assistant: ' -}}
 <Gallery />
 ## Model description