---
license: ecl-2.0
base_model:
- openai/gpt-oss-20b
tags:
- Claude
- Fable
- Antropic
- Agent
- Ollama
widget:
- output:
    url: images/Claude-OSS-Fable-5.webp
  text: '-'
datasets:
- Tesleum/Fable-5-traces-Harmony
---


# Highlights

* **Claude Fable 5:** Fully Train model to Calude Fable 5 specific use case through parameter fine-tuning.
* **Agentic capabilities:** Use the models’ native capabilities for function calling, [web browsing](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#browser), [Python code execution](https://github.com/openai/gpt-oss/tree/main?tab=readme-ov-file#python), and Structured Outputs.
* **MXFP4 quantization:** The models were post-trained with MXFP4 quantization of the MoE weights, making model run within 16GB of memory. All evals were performed with the same MXFP4 quantization.
* **Configurable reasoning effort:** Easily adjust the reasoning effort (low, medium, high) based on your specific use case and latency needs.  
* **Full chain-of-thought:** Gain complete access to the model’s reasoning process, facilitating easier debugging and increased trust in outputs. It’s not intended to be shown to end users.  

---

# Inference examples

## Transformers

You can use `Claude-OSS` with Transformers. If you use the Transformers chat template, it will automatically apply the [harmony response format](https://github.com/openai/harmony). If you use `model.generate` directly, you need to apply the harmony format manually using the chat template or use our [openai-harmony](https://github.com/openai/harmony) package.

To get started, install the necessary dependencies to setup your environment:

```
pip install -U transformers kernels torch 
```

Once, setup you can proceed to run the model by running the snippet below:

```py
from transformers import pipeline
import torch

model_id = "Tesleum/Claude-OSS"

pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [
    {"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
]

outputs = pipe(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```

Alternatively, you can run the model via [`Transformers Serve`](https://huggingface.co/docs/transformers/main/serving) to spin up a OpenAI-compatible webserver:

```
transformers serve
transformers chat localhost:8000 --model-name-or-path Tesleum/Claude-OSS
```

## vLLM

vLLM recommends using [uv](https://docs.astral.sh/uv/) for Python dependency management. You can use vLLM to spin up an OpenAI-compatible webserver. The following command will automatically download the model and start the server.

```bash
uv pip install --pre vllm==0.10.1+gptoss \
    --extra-index-url https://wheels.vllm.ai/gpt-oss/ \
    --extra-index-url https://download.pytorch.org/whl/nightly/cu128 \
    --index-strategy unsafe-best-match

vllm serve Tesleum/Claude-OSS
```

## PyTorch / Triton

To learn about how to use this model with PyTorch and Triton, check out our [reference implementations in the gpt-oss repository](https://github.com/openai/gpt-oss?tab=readme-ov-file#reference-pytorch-implementation).

## 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
```

---

# Reasoning levels

You can adjust the reasoning level that suits your task across three levels:

* **Low:** Fast responses for general dialogue.  
* **Medium:** Balanced speed and detail.  
* **High:** Deep and detailed analysis.

The reasoning level can be set in the system prompts, e.g., "Reasoning: high".

# Tool use

The claude-oss models are excellent for:
* Agentic operations like browser tasks
* Function calling with defined schemas
* Web browsing (using built-in browsing tools)