Instructions to use Tesslate/OmniCoder-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Tesslate/OmniCoder-9B with Transformers:

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

pipe = pipeline("text-generation", model="Tesslate/OmniCoder-9B")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Tesslate/OmniCoder-9B")
model = AutoModelForMultimodalLM.from_pretrained("Tesslate/OmniCoder-9B", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Tesslate/OmniCoder-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Tesslate/OmniCoder-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Tesslate/OmniCoder-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Tesslate/OmniCoder-9B

SGLang

How to use Tesslate/OmniCoder-9B 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 "Tesslate/OmniCoder-9B" \
    --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": "Tesslate/OmniCoder-9B",
		"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 "Tesslate/OmniCoder-9B" \
        --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": "Tesslate/OmniCoder-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Tesslate/OmniCoder-9B with Docker Model Runner:
```
docker model run hf.co/Tesslate/OmniCoder-9B
```

smirki commited on Mar 12

Commit

fde9b80

verified ·

1 Parent(s): 9f0c62c

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@@ -23,7 +23,7 @@ model-index:
         metrics:
           - name: pass@5
             type: accuracy
-            value: 90
       - task:
           type: text-generation
         dataset:
@@ -44,7 +44,7 @@ model-index:
         metrics:
           - name: Pass Rate
             type: accuracy
-            value: 28
 ---
 <div align="center">
@@ -86,12 +86,12 @@ The model shows strong agentic behavior: it recovers from errors (read-before-wr
 <div align="center">
-| Benchmark | **OmniCoder-9B** | Qwen3.5-9B | Qwen3-Next-80B | GPT-OSS-120B | GPT-OSS-20B | GLM 4.7 |
-|:---|:---:|:---:|:---:|:---:|:---:|:---:|
-| **AIME 2025** (pass@5) | 90 | | | | | |
-| **GPQA Diamond** (pass@1) | **83.8** | 81.7 | 77.2 | 80.1 | 71.5 | |
-| **GPQA Diamond** (pass@3) | **86.4** | | | | | |
-| **Terminal-Bench 2.0** | **28** | 20 | | | | 33.4 |
 </div>
@@ -164,16 +164,6 @@ See all quantizations: [Tesslate/OmniCoder-9B-GGUF](https://huggingface.co/Tessl
 | **Precision** | bf16 |
 | **Optimizer** | AdamW (lr=2e-4, cosine schedule) |
-### Training Data Sources
-| Source | Samples | Description |
-|:---|---:|:---|
-| NVIDIA Nemotron-Terminal-Corpus | 226K | Terminal agent trajectories |
-| CoderForge-Preview (reward >= 0.5) | 155K | SWE-bench style coding trajectories |
-| Nemotron Skill-Based | 24K | Skill-based coding tasks |
-| Scale-SWE | 20K | Real GitHub issue patches (synthesized trajectories) |
-| Opus Reasoning | 2.3K | Chain-of-thought reasoning |
 ---
 ## Architecture
@@ -181,7 +171,7 @@ See all quantizations: [Tesslate/OmniCoder-9B-GGUF](https://huggingface.co/Tessl
 OmniCoder inherits Qwen3.5-9B's hybrid architecture:
 - **Gated Delta Networks** : Linear attention layers interleaved with standard attention for efficient long-range dependencies
-- **VLM Backbone** : Built on `Qwen3_5ForConditionalGeneration` (supports future multimodal extensions)
 ---

         metrics:
           - name: pass@5
             type: accuracy
+            value: 90.0
       - task:
           type: text-generation
         dataset:
         metrics:
           - name: Pass Rate
             type: accuracy
+            value: 28.0
 ---
 <div align="center">
 <div align="center">
+| Benchmark | **OmniCoder-9B** | Qwen3.5-9B | Qwen3-Next-80B | GPT-OSS-120B | GPT-OSS-20B | GLM-4.7-Flash | GLM 4.7 | Claude Haiku 4.5 |
+|:---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| **AIME 2025** (pass@5) | 90 | | | | 91.7 | 91.6 | | |
+| **GPQA Diamond** (pass@1) | **83.8** | 81.7 | 77.2 | 80.1 | 71.5 | | | 73 |
+| **GPQA Diamond** (pass@3) | **86.4** | | | | | | | |
+| **Terminal-Bench 2.0** | **28** | 20 | | | | | 33.4 | 27 |
 </div>
 | **Precision** | bf16 |
 | **Optimizer** | AdamW (lr=2e-4, cosine schedule) |
 ---
 ## Architecture
 OmniCoder inherits Qwen3.5-9B's hybrid architecture:
 - **Gated Delta Networks** : Linear attention layers interleaved with standard attention for efficient long-range dependencies
+- **VLM Backbone** : Built on `Qwen3_5ForConditionalGeneration`
 ---