Update README.md

README.md

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----
-license: apache-2.0
----
+---
+library_name: mlx
+pipeline_tag: text-generation
+inference: false
+license: apache-2.0
+base_model: openai/gpt-oss-120b
+language:
+- en
+- ro
+tags:
+- apple-silicon
+- metal
+- arm64
+- bf16
+- mlx
+- mlx-lm
+- openai
+- halley-ai
+---
+
+# gpt-oss-120b — MLX bf16 (non-quantized)
+
+**Summary.** This is a non-quantized MLX conversion of gpt-oss-120B in bfloat16 (bf16). Built for Apple Silicon with Metal acceleration.
+
+- **Base model:** `openai/gpt-oss-120b` (Apache-2.0)
+- **Precision:** bfloat16 (no quantization)
+- **Files:** MLX weight shards + `config.json`; tokenizer files included for drop-in use
+- **Intended use:** local inference / research on M-series Macs
+- **Not intended for:** safety-critical decisions; outputs may be inaccurate or biased
+
+## Requirements
+
+Runs on Apple Silicon (M1 or newer) with macOS ≥ 13.5 via MLX (Metal).
+
+- Not supported: Intel macOS / Linux / Windows (consider a GGUF build + llama.cpp instead).
+- Memory guidance: large unified memory recommended (e.g., 64–96 GB). The effective GPU working set is capped by Metal’s budget; keep 5–10% headroom.
+
+## How to use (MLX)
+
+```bash
+pip install mlx-lm
+```
+
+```python
+# Python API (uses tokenizer bundled with this repo)
+from mlx_lm import load, generate
+
+model, tokenizer = load("halley-ai/gpt-oss-120b-MLX-bf16")
+print(generate(
+    model, tokenizer,
+    prompt="Explain the Chudnovsky algorithm to compute π.",
+    max_tokens=256, max_kv_size=512
+))
+```
+
+```bash
+# CLI
+python -m mlx_lm generate --model halley-ai/gpt-oss-120b-MLX-bf16 \
+  --prompt "Explain the Chudnovsky algorithm to compute pi." \
+  --max-kv-size 512 --max-tokens 256
+```
+
+## Evaluation
+
+Perplexity (PPL) streaming evaluation on WikiText-2 (raw, test); fast preset with `window=stride=4096`, ~100k tokens, EOS inserted between docs.
+
+| Variant              | PPL (ctx=4096, fast) |
+|----------------------|-----------------------|
+| MLX bf16 (non-quant) | 7.38                  |
+| MLX 8-bit (gs=32)    | 7.39                  |
+
+Notes:
+
+- Results from local runs on Apple Silicon using MLX; numbers vary slightly with tokenizer details, logits dtype, and token subset.
+- For more sensitive comparisons, use overlapping windows (e.g., `--stride 512`) and evaluate the full split.
+
+## Conversion details (provenance)
+
+```bash
+python -m mlx_lm convert \
+  --hf-path openai/gpt-oss-120b \
+  --mlx-path gpt-oss-120b-MLX-bf16 \
+  --dtype bfloat16
+```
+
+## Sibling & reference models
+
+- halley-ai/gpt-oss-120b-MLX-8bit-gs32 (int8, group size 32)
+
+## Limitations & biases
+
+Outputs may be factually wrong or unsafe. Do not use for medical, legal, or financial decisions without human review. Large models can be sensitive to prompts; prefer explicit instructions and structure.
+
+## License & credits
+
+- License: Apache-2.0 (inherits from base model)
+- Base model: OpenAI gpt-oss-120B
+- Conversion: Halley AI Lab (MLX bf16)
+- Please cite both the base model and this repository when you use the weights.