KJML
/

gpt-oss-20b-FP8-Dynamic

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 ---
 library_name: transformers
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 ---
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 ---
 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))