README.md

---
library_name: transformers
pipeline_tag: text-generation
base_model: openai/gpt-oss-20b
license: apache-2.0
tags:
  - text-generation
  - causal-lm
  - gpt_oss
  - moe
  - fp8
  - conversational
  - english
---

# Model Card for `KJML/gpt-oss-20b-FP8-Dynamic`

This repository provides an FP8-dynamic quantized variant of **OpenAI’s `gpt-oss-20b`** model.  
It is intended for users who want the reasoning capabilities of gpt-oss-20b with a **smaller memory footprint and faster inference** on modern GPUs that support FP8 inference.

> ⚠️ This model is **not** trained or fine-tuned further; it is a **post-training quantization** of the original `openai/gpt-oss-20b` weights.

---

## Model Details

### Model Description

- **Base model:** `openai/gpt-oss-20b`
- **Architecture:** Mixture-of-Experts (MoE) Transformer language model (≈21B total params, ≈3.6B active per token, inherited from base)
- **Quantization:** FP8 dynamic (weights + activations) for inference
- **Context length:** Same as base `gpt-oss-20b` (long-context, Harmony-format chat)
- **Language(s):** Primarily English; inherits multilingual capability from base model
- **License:** Apache 2.0 (inherits from base model)
- **Model type:** Causal language model for text / chat generation
- **Developer of this variant:** KJML
- **Finetuned from model:** `openai/gpt-oss-20b` (no additional training; quantization only)

The original `gpt-oss-20b` is an open-weight reasoning model from OpenAI, designed for agentic workflows, tool use, and configurable reasoning effort. This FP8-dynamic variant preserves those capabilities while targeting **more efficient deployment**.

### Model Sources

- **Base model repository:** <https://huggingface.co/openai/gpt-oss-20b>
- **Upstream project / docs:** <https://github.com/openai/gpt-oss>
- **This quantized model:** <https://huggingface.co/KJML/gpt-oss-20b-FP8-Dynamic> (this repo)

---

## Uses

### Direct Use

Typical direct-use scenarios (without additional fine-tuning):

- General chat and assistant-style dialogue (English-first)
- Reasoning and analysis (step-by-step / chain-of-thought) for:
  - Technical explanations
  - Brainstorming and ideation
  - Code reasoning and pseudo-code (light coding assistance)
- Agentic / tool-using setups:
  - Function calling and structured outputs
  - Retrieval-augmented generation (RAG) backends
  - Local “AI PC” / workstation deployments where FP8 is supported

**Note:** The model is trained on OpenAI’s Harmony response format. For best results, use a chat template that applies the Harmony format (e.g. `tokenizer.apply_chat_template` in Transformers) when prompting.

### Downstream Use

The FP8-dynamic variant can be used as a drop-in replacement for `openai/gpt-oss-20b` in:

- Custom backends with vLLM / TGI / custom inference servers
- Local desktop apps (LM Studio, Ollama-style setups, etc.) that support FP8
- RAG systems where latency and VRAM usage are important
- Multi-agent frameworks where many concurrent contexts are needed

If you fine-tune or adapt this model further, treat it as you would the base `gpt-oss-20b` model, but keep in mind that **quantization can slightly change numeric behavior**, especially for very long generations.

### Out-of-Scope Use

The model (and this quantized variant) is **not recommended** for:

- High-stakes decision making without human review, e.g.:
  - Medical, legal, or financial advice
  - Safety-critical environments (autonomous driving, industrial control, etc.)
- Generating content that violates laws or platform policies
- Acting as the sole decision-maker in any context where errors could cause **harm to people or property**

Users should always keep a human in the loop for sensitive or impactful applications.

---

## Bias, Risks, and Limitations

This model inherits all **biases, risks, and limitations** of the base `gpt-oss-20b` model. As a large language model trained on internet-scale data, it may:

- Produce **biased or stereotypical content**, including along axes such as gender, race, nationality, or religion.
- Hallucinate facts, references, or citations.
- Overstate its own certainty.
- Generate unsafe or undesirable content if prompted adversarially or without proper safety layers.

The FP8-dynamic quantization may also:

- Introduce small degradations in quality vs. BF16 / MXFP4 versions, particularly for:
  - Very long generations
  - Edge cases that are numerically sensitive
- Behave slightly differently from the base model, even with identical prompts.

### Recommendations

- **Do not** rely on this model as a single source of truth.
- Add **safety filters** and/or a moderation layer around generations.
- Use **human review** for any high-impact or user-facing deployment.
- Evaluate the FP8-dynamic variant on your own tasks and data before using in production.

---

## How to Get Started with the Model

Basic usage with 🤗 Transformers:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "KJML/gpt-oss-20b-FP8-Dynamic"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",     # Will use FP8 where supported
    device_map="auto",
)

messages = [
    {"role": "system", "content": "You are a helpful AI assistant."},
    {"role": "user", "content": "Explain what FP8 dynamic quantization is in simple terms."},
]

inputs = tokenizer.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=256,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))
````

Make sure you are using a recent version of **Transformers** and a PyTorch build that supports FP8 where applicable.

---

## Training Details

### Training Data

No new training data is introduced in this repository.

* **This model is not trained from scratch.**
* It directly reuses the weights and training data of `openai/gpt-oss-20b`.
* For full details on the original training data and methodology, see the official gpt-oss model card and paper.

### Training Procedure

No additional gradient-based training was performed. The steps were:

1. Start from base `openai/gpt-oss-20b` weights.
2. Apply FP8-dynamic post-training quantization (weights and activations) for inference.
3. Export quantized weights to `safetensors` format for deployment.

#### Preprocessing

No extra data preprocessing was done beyond what OpenAI used for the base model.

#### Training Hyperparameters

* **Training regime for this repo:** *None* (no fine-tuning; quantization only)
* **Original base model:** Trained by OpenAI using high-precision training and post-training MXFP4 quantization of MoE weights (see upstream model card / paper for specifics).

#### Speeds, Sizes, Times

Exact performance depends on your hardware and FP8 support, but in general:

* **VRAM usage:** Lower than the BF16 / MXFP4 original, enabling more concurrent contexts or larger batch sizes.
* **Throughput:** Higher tokens/sec on FP8-capable hardware compared to running BF16 weights, especially at batch size >1.

You should benchmark on your own GPU(s) for precise numbers.

---

## Evaluation

No separate benchmark suite has been run specifically for the FP8-dynamic variant at this time.

### Testing Data, Factors & Metrics

* **Testing data:** Not re-evaluated independently here.
* It is reasonable to expect **similar qualitative behavior** to `openai/gpt-oss-20b`, with minor differences due to quantization.

### Results

If you run your own evals (e.g. on reasoning or coding benchmarks), please feel free to share issues / PRs or discussion links so others can reference them.

#### Summary

* Use this model when you want **gpt-oss-20b-level reasoning** with **lower memory usage and better throughput**.
* Expect small quality differences vs. the original due to FP8 quantization.

---

## Model Examination (Optional)

No additional interpretability or probing analysis has been carried out on this quantized variant.

For deeper analysis and interpretability work, refer to:

* The official gpt-oss paper / model card.
* Independent community evaluations of `gpt-oss-20b`.

---

## Environmental Impact

This repository does **not** involve training a new model.

* The main compute cost is a **one-time quantization pass** over the base weights.
* Carbon footprint is therefore negligible compared to the original model training.

For estimates of training-time emissions, please consult the original gpt-oss model card and related publications.

---

## Technical Specifications

### Model Architecture and Objective

* **Architecture:** Mixture-of-Experts Transformer language model (same as `gpt-oss-20b`)
* **Objective:** Next-token prediction / causal language modeling
* **Quantization:**

  * FP8 dynamic for weights and activations at inference time
  * Intended for GPUs / accelerators that support efficient FP8 matmul

The quantization is applied in a way that preserves the original architecture and I/O behavior.

### Compute Infrastructure

Quantization was performed on a single modern GPU (exact details may vary; see repository description or commits if you need exact hardware).

#### Hardware

* Single GPU with FP8 support (for quantization and testing)
* Standard CPU + RAM sufficient to host original and quantized weights

#### Software

* PyTorch (FP8-capable build)
* Hugging Face Transformers
* Supporting libraries for FP8 quantization and safetensor export

---

## Citation

If you use this model in academic or commercial work, please cite at least the original gpt-oss paper/model card from OpenAI:

**BibTeX:**

```bibtex
@misc{openai2025gptoss120bgptoss20bmodel,
      title={gpt-oss-120b & gpt-oss-20b Model Card},
      author={OpenAI},
      year={2025},
      eprint={2508.10925},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2508.10925}
}
```

You may also optionally reference this quantized variant as:

```bibtex
@misc{kjml2025gptoss20bfp8dynamic,
      title={KJML/gpt-oss-20b-FP8-Dynamic: FP8-dynamic Quantized Variant of gpt-oss-20b},
      author={KJML},
      year={2025},
      howpublished={Hugging Face model repository},
      url={https://huggingface.co/KJML/gpt-oss-20b-FP8-Dynamic}
}
```

---

## Glossary

* **MoE (Mixture-of-Experts):** Architecture where only a subset of “experts” (parameter blocks) are active per token, reducing compute vs. dense models.
* **FP8 dynamic:** 8-bit floating point representation with dynamic scaling, used to reduce memory and bandwidth while preserving model quality.
* **Harmony format:** OpenAI’s chat / response formatting used for training gpt-oss models; must be respected for best performance.

---

## More Information

* Base model details, prompts, and advanced usage examples: see `openai/gpt-oss-20b` on Hugging Face and the official gpt-oss GitHub repository.
* For questions, issues, or suggestions around this FP8-dynamic variant, please open an issue or discussion in this repository.

---

## Model Card Authors

* **Author:** KJML
* **Contact:** Kongphop.ct.ja.sc@gmail.com

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