Normalize model card metadata and document GGUF in place

README.md

@@ -1,102 +1,102 @@
----
-library_name: transformers
-license: apache-2.0
-pipeline_tag: text-generation
-tags:
-  - custom-code
-  - nova
----
-
-# Nova v3
-
-Nova v3 is a decoder-only causal language model with **148.8 million parameters**. This repository provides the Transformers checkpoint, tokenizer, and custom model code required to load the model.
-
-## Model specifications
-
-| Specification | Value |
-|---|---:|
-| Parameters | 148.8M |
-| Vocabulary | 32,768 tokens |
-| Hidden size | 640 |
-| Transformer layers | 26 |
-| Attention | 10 query heads, 5 key/value heads |
-| Maximum context | 2,048 tokens |
-| Checkpoint step | 3,500 |
-
-The model supports key/value caching during generation. Its custom implementation supports Transformers 4.57 and 5.x. Review the custom modeling files before loading with `trust_remote_code=True`.
-
-## Load with Transformers
-
-Install PyTorch and Transformers:
-
-```bash
-pip install torch transformers
-```
-
-Load the tokenizer and model:
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
-
-model_id = "plasmova/nova-v3"
-tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
-model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
-
-system_prompt = (
-    "You are Nova, a helpful and accurate assistant. Answer the user's request directly and concisely. "
-    "For simple arithmetic, calculate the result and state it plainly. Do not invent names, scenarios, or equations."
-)
-prompt = (
-    f"<|system|>{system_prompt}<|/system|>"
-    "<|user|>Explain why the sky appears blue in one sentence.<|/user|>"
-    "<|assistant|>"
-)
-inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
-output = model.generate(
-    **inputs,
-    max_new_tokens=160,
-    do_sample=True,
-    temperature=0.2,
-    top_p=0.85,
-    top_k=12,
-    repetition_penalty=1.12,
-    no_repeat_ngram_size=3,
-    eos_token_id=[
-        tokenizer.eos_token_id,
-        tokenizer.convert_tokens_to_ids("<|/assistant|>"),
-    ],
-)
-print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
-```
-
-Recommended starting settings are temperature **0.2**, top-p **0.9**, top-k **40**, and repetition penalty **1.08**. These focused settings are a starting point; adjust them for your task. The local inference script applies this concise system instruction by default. Recommended generation defaults are temperature **0.2**, top-p **0.85**, top-k **12**, repetition penalty **1.12**, no-repeat n-gram size **3**, and up to **160 new tokens**. Keep the total prompt and generated text within the 2,048-token context. The example runs on CPU; for GPU inference, move the model and inputs to a supported device and dtype.
-
-## Evaluation
-
-The following zero-shot results use the first 500 examples from each task with `lm-evaluation-harness` 0.4.13 on CPU. These are limited samples, not full validation-set results.
-
-| Benchmark | Samples | Accuracy | Normalized accuracy |
-|---|---:|---:|---:|
-| HellaSwag | 500 | 37.4% | 43.6% |
-| ARC Easy | 500 | 51.0% | 48.2% |
-| ARC Challenge | 500 | 21.8% | 25.0% |
-| WinoGrande | 500 | 52.6% | 52.2% |
-| OpenBookQA | 500 | 18.8% | 30.0% |
-
-### Comparison with SmolLM2-135M
-
-In a matched zero-shot run using 500 examples per task, Nova v3 scored 43.6% normalized accuracy on HellaSwag and 52.2% on WinoGrande, compared with 44.2% and 50.6% for SmolLM2-135M. The WinoGrande difference is small relative to sampling uncertainty; treat both results as early directional measurements, not established performance claims. The comparison details and per-run metrics are in [benchmark_comparison.json](benchmark_comparison.json).
-
-On a separate 100-question GSM8K sample with five examples in context, Nova v3 and SmolLM2-135M-Instruct each answered 4 questions correctly (4.0% exact match). This small sample does not establish equivalence. Details are in [gsm8k_comparison.json](gsm8k_comparison.json).
-
-## Training and license
-
-The v3 training code specifies pretraining on a mixture of FineWeb-Edu, DCLM, Cosmopedia-v2, FineMath, Wikipedia, and CodeParrot, followed by SmolTalk fine-tuning. Dataset contents remain subject to their respective upstream terms.
-
-The model weights and accompanying custom model code are released under the Apache License 2.0. See [LICENSE](LICENSE).
-
-## llama.cpp and mobile apps
-
-The validated F16 GGUF export is [`nova-v3-f16.gguf`](https://huggingface.co/plasmova/nova-v3/blob/main/nova-v3-f16.gguf) (about 341 MB). It uses the `qwen3` GGUF architecture metadata needed for Nova v3's per-head query/key normalization. It was checked with llama.cpp b8882, including the version pinned by Pocket AI Lab; all 289 tensors load and CPU generation completes.
-
-In Pocket AI Lab, import `plasmova/nova-v3` and select `nova-v3-f16.gguf`. If you downloaded the previous incompatible file, remove that local copy and download this updated file.
+---
+library_name: transformers
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+  - custom-code
+  - nova
+---
+
+# Nova v3
+
+Nova v3 is a decoder-only causal language model with **148.8 million parameters**. This repository provides the Transformers checkpoint, tokenizer, and custom model code required to load the model.
+
+## Model specifications
+
+| Specification | Value |
+|---|---:|
+| Parameters | 148.8M |
+| Vocabulary | 32,768 tokens |
+| Hidden size | 640 |
+| Transformer layers | 26 |
+| Attention | 10 query heads, 5 key/value heads |
+| Maximum context | 2,048 tokens |
+| Checkpoint step | 3,500 |
+
+The model supports key/value caching during generation. Its custom implementation supports Transformers 4.57 and 5.x. Review the custom modeling files before loading with `trust_remote_code=True`.
+
+## Load with Transformers
+
+Install PyTorch and Transformers:
+
+```bash
+pip install torch transformers
+```
+
+Load the tokenizer and model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "plasmova/nova-v3"
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+
+system_prompt = (
+    "You are Nova, a helpful and accurate assistant. Answer the user's request directly and concisely. "
+    "For simple arithmetic, calculate the result and state it plainly. Do not invent names, scenarios, or equations."
+)
+prompt = (
+    f"<|system|>{system_prompt}<|/system|>"
+    "<|user|>Explain why the sky appears blue in one sentence.<|/user|>"
+    "<|assistant|>"
+)
+inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False)
+output = model.generate(
+    **inputs,
+    max_new_tokens=160,
+    do_sample=True,
+    temperature=0.2,
+    top_p=0.85,
+    top_k=12,
+    repetition_penalty=1.12,
+    no_repeat_ngram_size=3,
+    eos_token_id=[
+        tokenizer.eos_token_id,
+        tokenizer.convert_tokens_to_ids("<|/assistant|>"),
+    ],
+)
+print(tokenizer.decode(output[0, inputs["input_ids"].shape[1]:], skip_special_tokens=True))
+```
+
+Recommended starting settings are temperature **0.2**, top-p **0.9**, top-k **40**, and repetition penalty **1.08**. These focused settings are a starting point; adjust them for your task. The local inference script applies this concise system instruction by default. Recommended generation defaults are temperature **0.2**, top-p **0.85**, top-k **12**, repetition penalty **1.12**, no-repeat n-gram size **3**, and up to **160 new tokens**. Keep the total prompt and generated text within the 2,048-token context. The example runs on CPU; for GPU inference, move the model and inputs to a supported device and dtype.
+
+## Evaluation
+
+The following zero-shot results use the first 500 examples from each task with `lm-evaluation-harness` 0.4.13 on CPU. These are limited samples, not full validation-set results.
+
+| Benchmark | Samples | Accuracy | Normalized accuracy |
+|---|---:|---:|---:|
+| HellaSwag | 500 | 37.4% | 43.6% |
+| ARC Easy | 500 | 51.0% | 48.2% |
+| ARC Challenge | 500 | 21.8% | 25.0% |
+| WinoGrande | 500 | 52.6% | 52.2% |
+| OpenBookQA | 500 | 18.8% | 30.0% |
+
+### Comparison with SmolLM2-135M
+
+In a matched zero-shot run using 500 examples per task, Nova v3 scored 43.6% normalized accuracy on HellaSwag and 52.2% on WinoGrande, compared with 44.2% and 50.6% for SmolLM2-135M. The WinoGrande difference is small relative to sampling uncertainty; treat both results as early directional measurements, not established performance claims. The comparison details and per-run metrics are in [benchmark_comparison.json](benchmark_comparison.json).
+
+On a separate 100-question GSM8K sample with five examples in context, Nova v3 and SmolLM2-135M-Instruct each answered 4 questions correctly (4.0% exact match). This small sample does not establish equivalence. Details are in [gsm8k_comparison.json](gsm8k_comparison.json).
+
+## Training and license
+
+The v3 training code specifies pretraining on a mixture of FineWeb-Edu, DCLM, Cosmopedia-v2, FineMath, Wikipedia, and CodeParrot, followed by SmolTalk fine-tuning. Dataset contents remain subject to their respective upstream terms.
+
+The model weights and accompanying custom model code are released under the Apache License 2.0. See [LICENSE](LICENSE).
+
+## llama.cpp and mobile apps
+
+The validated F16 GGUF export is [`nova-v3-f16.gguf`](https://huggingface.co/plasmova/nova-v3/blob/main/nova-v3-f16.gguf) (about 341 MB). It uses the `qwen3` GGUF architecture metadata needed for Nova v3's per-head query/key normalization. It was checked with llama.cpp b8882, including the version pinned by Pocket AI Lab; all 289 tensors load and CPU generation completes.
+
+In Pocket AI Lab, import `plasmova/nova-v3` and select `nova-v3-f16.gguf`. If you downloaded the previous incompatible file, remove that local copy and download this updated file.